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3.4: Trip Generation

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David Levinson et al.
Associate Professor (Engineering) via Wikipedia

Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.

Every trip has two ends, and we need to know where both of them are. The first part is determining how many trips originate in a zone and the second part is how many trips are destined for a zone. Because land use can be divided into two broad category (residential and non-residential) we have models that are household based and non-household based (e.g. a function of number of jobs or retail activity).

For the residential side of things, trip generation is thought of as a function of the social and economic attributes of households (households and housing units are very similar measures, but sometimes housing units have no households, and sometimes they contain multiple households, clearly housing units are easier to measure, and those are often used instead for models, it is important to be clear which assumption you are using).

At the level of the traffic analysis zone, the language is that of land uses "producing" or attracting trips, where by assumption trips are "produced" by households and "attracted" to non-households. Production and attractions differ from origins and destinations. Trips are produced by households even when they are returning home (that is, when the household is a destination). Again it is important to be clear what assumptions you are using.

People engage in activities, these activities are the "purpose" of the trip. Major activities are home, work, shop, school, eating out, socializing, recreating, and serving passengers (picking up and dropping off). There are numerous other activities that people engage on a less than daily or even weekly basis, such as going to the doctor, banking, etc. Often less frequent categories are dropped and lumped into the catchall "Other".

Every trip has two ends, an origin and a destination. Trips are categorized by purposes , the activity undertaken at a destination location.

Observed trip making from the Twin Cities (2000-2001) Travel Behavior Inventory by Gender

Some observations:

Men and women behave differently on average, splitting responsibilities within households, and engaging in different activities,
Most trips are not work trips, though work trips are important because of their peaked nature (and because they tend to be longer in both distance and travel time),
The vast majority of trips are not people going to (or from) work.

People engage in activities in sequence, and may chain their trips. In the Figure below, the trip-maker is traveling from home to work to shop to eating out and then returning home.

Specifying Models

How do we predict how many trips will be generated by a zone? The number of trips originating from or destined to a purpose in a zone are described by trip rates (a cross-classification by age or demographics is often used) or equations. First, we need to identify what we think the relevant variables are.

The total number of trips leaving or returning to homes in a zone may be described as a function of:

\[T_h = f(housing \text{ }units, household \text{ }size, age, income, accessibility, vehicle \text{ }ownership)\]

Home-End Trips are sometimes functions of:

Housing Units
Household Size
Accessibility
Vehicle Ownership
Other Home-Based Elements

At the work-end of work trips, the number of trips generated might be a function as below:

\[T_w=f(jobs(area \text{ }of \text{ } space \text{ } by \text{ } type, occupancy \text{ } rate\]

Work-End Trips are sometimes functions of:

Area of Workspace
Occupancy Rate
Other Job-Related Elements

Similarly shopping trips depend on a number of factors:

\[T_s = f(number \text{ }of \text{ }retail \text{ }workers, type \text{ }of \text{ }retail, area, location, competition)\]

Shop-End Trips are sometimes functions of:

Number of Retail Workers
Type of Retail Available
Area of Retail Available
Competition
Other Retail-Related Elements

A forecasting activity conducted by planners or economists, such as one based on the concept of economic base analysis, provides aggregate measures of population and activity growth. Land use forecasting distributes forecast changes in activities across traffic zones.

Estimating Models

Which is more accurate: the data or the average? The problem with averages (or aggregates) is that every individual’s trip-making pattern is different.

To estimate trip generation at the home end, a cross-classification model can be used. This is basically constructing a table where the rows and columns have different attributes, and each cell in the table shows a predicted number of trips, this is generally derived directly from data.

In the example cross-classification model: The dependent variable is trips per person. The independent variables are dwelling type (single or multiple family), household size (1, 2, 3, 4, or 5+ persons per household), and person age.

The figure below shows a typical example of how trips vary by age in both single-family and multi-family residence types.

The figure below shows a moving average.

Non-home-end

The trip generation rates for both “work” and “other” trip ends can be developed using Ordinary Least Squares (OLS) regression (a statistical technique for fitting curves to minimize the sum of squared errors (the difference between predicted and actual value) relating trips to employment by type and population characteristics.

The variables used in estimating trip rates for the work-end are Employment in Offices ($E_{off}$), Retail ($E_{ret}$), and Other ($E_{oth}$)

A typical form of the equation can be expressed as:

\[T_{D,k}=a_1E_{off,k}+a_2E_{oth,k}+a_3E_{ret,k}\]

$T_{D,k}$ - Person trips attracted per worker in Zone k
$E_{off,i}$ - office employment in the ith zone
$E_{oth,i}$ - other employment in the ith zone
$E_{ret,i}$- retail employment in the ith zone
$a_1,a_2,a_3$ - model coefficients

Normalization

For each trip purpose (e.g. home to work trips), the number of trips originating at home must equal the number of trips destined for work. Two distinct models may give two results. There are several techniques for dealing with this problem. One can either assume one model is correct and adjust the other, or split the difference.

It is necessary to ensure that the total number of trip origins equals the total number of trip destinations, since each trip interchange by definition must have two trip ends.

The rates developed for the home end are assumed to be most accurate,

The basic equation for normalization:

\[T'_{D,j}=T_{D,j} \dfrac{ \displaystyle \sum{i=1}^I T_{O,i}}{\displaystyle \sum{j=1}^J T_{TD,j}}\]

Sample Problems

Planners have estimated the following models for the AM Peak Hour

$T_{O,i}=1.5*H_i$

$T_{D,j}=(1.5*E_{off,j})+(1*E_{oth,j})+(0.5*E_{ret,j})$

$T_{O,i}$ = Person Trips Originating in Zone $i$

$T_{D,j}$ = Person Trips Destined for Zone $j$

$H_i$ = Number of Households in Zone $i$

You are also given the following data

A. What are the number of person trips originating in and destined for each city?

B. Normalize the number of person trips so that the number of person trip origins = the number of person trip destinations. Assume the model for person trip origins is more accurate.

Solution to Trip Generation Problem Part A

\[T'_{D,j}=T_{D,j} \dfrac{ \displaystyle \sum{i=1}^I T_{O,i}}{\displaystyle \sum{j=1}^J T_{TD,j}}=>T_{D,j} \dfrac{37500}{36750}=T_{D,j}*1.0204\]

Solution to Trip Generation Problem Part B

Modelers have estimated that the number of trips leaving Rivertown ($T_O$) is a function of the number of households (H) and the number of jobs (J), and the number of trips arriving in Marcytown ($T_D$) is also a function of the number of households and number of jobs.

$T_O=1H+0.1J;R^2=0.9$

$T_D=0.1H+1J;R^2=0.5$

Assuming all trips originate in Rivertown and are destined for Marcytown and:

Rivertown: 30000 H, 5000 J

Marcytown: 6000 H, 29000 J

Determine the number of trips originating in Rivertown and the number destined for Marcytown according to the model.

Which number of origins or destinations is more accurate? Why?

T_Rivertown =T_O ; T_O= 1(30000) + 0.1(5000) = 30500 trips

T_(MarcyTown)=T_D ; T_D= 0.1(6000) + 1(29000) = 29600 trips

Origins(T_{Rivertown}) because of the goodness of fit measure of the Statistical model (R^2=0.9).

Modelers have estimated that in the AM peak hour, the number of trip origins (T_O) is a function of the number of households (H) and the number of jobs (J), and the number of trip destinations (T_D) is also a function of the number of households and number of jobs.

$T_O=1.0H+0.1J;R^2=0.9$

Suburbia: 30000 H, 5000 J

Urbia: 6000 H, 29000 J

1) Determine the number of trips originating in and destined for Suburbia and for Urbia according to the model.

2) Does this result make sense? Normalize the result to improve its accuracy and sensibility?

$f(t_{ij})=t_{ij}^{-2}$

$T_{O,i}$ - Person trips originating in Zone i
$T_{D,j}$ - Person Trips destined for Zone j
$T_{O,i'}$ - Normalized Person trips originating in Zone i
$T_{D,j'}$ - Normalized Person Trips destined for Zone j
$T_h$ - Person trips generated at home end (typically morning origins, afternoon destinations)
$T_w$ - Person trips generated at work end (typically afternoon origins, morning destinations)
$T_s$ - Person trips generated at shop end
$H_i$ - Number of Households in Zone i
$E_{off,k}$ - office employment in Zone k
$E_{ret,k}$ - retail employment in Zone k
$E_{oth,k}$ - other employment in Zone k
$B_n$ - model coefficients

Abbreviations

H2W - Home to work
W2H - Work to home
W2O - Work to other
O2W - Other to work
H2O - Home to other
O2H - Other to home
O2O - Other to other
HBO - Home based other (includes H2O, O2H)
HBW - Home based work (H2W, W2H)
NHB - Non-home based (O2W, W2O, O2O)

External Exercises

Use the ADAM software at the STREET website and try Assignment #1 to learn how changes in analysis zone characteristics generate additional trips on the network.

Additional Problems

the start and end time (to the nearest minute)
start and end location of each trip,
primary mode you took (drive alone, car driver with passenger, car passenger, bus, LRT, walk, bike, motorcycle, taxi, Zipcar, other). (use the codes provided)
purpose (to work, return home, work related business, shopping, family/personal business, school, church, medical/dental, vacation, visit friends or relatives, other social recreational, other) (use the codes provided)
if you traveled with anyone else, and if so whether they lived in your household or not.

Bonus: Email your professor at the end of everyday with a detailed log of your travel diary. (+5 points on the first exam)

Are number of destinations always less than origins?
Pose 5 hypotheses about factors that affect work, non-work trips? How do these factors affect accuracy, and thus normalization?
What is the acceptable level of error?
Describe one variable used in trip generation and how it affects the model.
What is the basic equation for normalization?
Which of these models (home-end, work-end) are assumed to be more accurate? Why is it important to normalize trip generation models
What are the different trip purposes/types trip generation?
Why is it difficult to know who is traveling when?
What share of trips during peak afternoon peak periods are work to home (>50%, <50%?), why?
What does ORIO abbreviate?
What types of employees (ORIO) are more likely to travel from work to home in the evening peak
What does the trip rate tell us about various parts of the population?
What does the “T-statistic” value tell us about the trip rate estimation?
Why might afternoon work to home trips be more or less than morning home to work trips? Why might the percent of trips be different?
Define frequency.
Why do individuals > 65 years of age make fewer work to home trips?
Solve the following problem. You have the following trip generation model:

\[Trips=B_1Off+B_2Ind+B_3Ret\]

And you are given the following coefficients derived from a regression model.

If there are 600 office employees, 300 industrial employees, and 200 retail employees, how many trips are going from work to home?

Fundamentals of Transportation/Trip Generation

Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice , Mode Choice , and Route Choice ), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.

1 Activities
2.1 Home-end
2.2 Work-end
2.3 Shop-end
3 Input Data
4.1 Home-end
4.2 Non-home-end
5 Normalization
6 Sample Problems
7 Variables
8 Abbreviations
9 External Exercises
10 Additional Problems
11 End Notes
12 Further reading
14 References

Activities [ edit | edit source ]

Every trip has two ends, an origin and a destination. Trips are categorized by purposes , the activity undertaken at a destination location.

Some observations:

Men and women behave differently on average, splitting responsibilities within households, and engaging in different activities,
Most trips are not work trips, though work trips are important because of their peaked nature (and because they tend to be longer in both distance and travel time),
The vast majority of trips are not people going to (or from) work.

People engage in activities in sequence, and may chain their trips. In the Figure below, the trip-maker is traveling from home to work to shop to eating out and then returning home.

Specifying Models [ edit | edit source ]

Home-end [ edit | edit source ]

The total number of trips leaving or returning to homes in a zone may be described as a function of:

$T_{h}=f(housing\ units,\ household\ size,\ age,\ income,\ accessibility,\ vehicle\ ownership).\,\!$

Home-End Trips are sometimes functions of:

Housing Units
Household Size
Accessibility
Vehicle Ownership
Other Home-Based Elements

Work-end [ edit | edit source ]

At the work-end of work trips, the number of trips generated might be a function as below:

$T_{w}=f(jobs(area\ of\ space\ by\ type,\ occupancy\ rate))\,\!$

Work-End Trips are sometimes functions of:

Area of Workspace
Occupancy Rate
Other Job-Related Elements

Shop-end [ edit | edit source ]

Similarly shopping trips depend on a number of factors:

$\,\!T_{s}=f(number\ of\ retail\ workers,\ type\ of\ retail,\ area,\ location,\ competition)$

Shop-End Trips are sometimes functions of:

Number of Retail Workers
Type of Retail Available
Area of Retail Available
Competition
Other Retail-Related Elements

Input Data [ edit | edit source ]

Estimating Models [ edit | edit source ]

Which is more accurate: the data or the average? The problem with averages (or aggregates) is that every individual’s trip-making pattern is different.

The figure below shows a typical example of how trips vary by age in both single-family and multi-family residence types.

The figure below shows a moving average.

Non-home-end [ edit | edit source ]

$E_{off}\,\!$

A typical form of the equation can be expressed as:

$T_{D,k}=a_{1}E_{off,k}+a_{2}E_{oth,k}+a_{3}E_{ret,k}\,\!$

Normalization [ edit | edit source ]

It is necessary to ensure that the total number of trip origins equals the total number of trip destinations, since each trip interchange by definition must have two trip ends.

The rates developed for the home end are assumed to be most accurate,

The basic equation for normalization:

$T'_{D,j}=T_{D,j}{\frac {\sum \limits _{i=1}^{I}{T_{O,i}}}{\sum \limits _{j=1}^{J}{T_{D,j}}}}\,\!$

Sample Problems [ edit | edit source ]

Problem ( Solution )

Variables [ edit | edit source ]

$T_{O},i$

Abbreviations [ edit | edit source ]

H2W - Home to work
W2H - Work to home
W2O - Work to other
O2W - Other to work
H2O - Home to other
O2H - Other to home
O2O - Other to other
HBO - Home based other (includes H2O, O2H)
HBW - Home based work (H2W, W2H)
NHB - Non-home based (O2W, W2O, O2O)

External Exercises [ edit | edit source ]

Use the ADAM software at the STREET website and try Assignment #1 to learn how changes in analysis zone characteristics generate additional trips on the network.

Additional Problems [ edit | edit source ]

Additional Problems

End Notes [ edit | edit source ]

Videos [ edit | edit source ]

Trip Generation
Normalization

References [ edit | edit source ]

Book:Fundamentals of Transportation

Trip generation

What is trip generation .

A trip is usually defined in transport modeling as a single journey made by an individual between two points by a specified mode of travel and for a defined purpose. Trips are often considered as productions of a particular land-use and attracted to other specified land-uses. The number of trips arises in unit time, usually for a specified zonal land use , is called the trip generation rate.

How to estimate trip generation ?

Trip generation is estimated in three ways:

(i) traditionally by linear and multiple regression

(ii) by aggregating the trip generating capability of a household or car and aggregating the total according to the distribution of each selected category in the zones, and

(iii) by household classification method through a catalogue of the characteristic mean trip rates for specific types of household.

The attraction points are identified as trip generated by work, and other purpose visits. By assigning suitable values to the independent variables of the regression equations forecasts can be made of the future trip ends for zones by either method.

Trip distribution :Trip generation estimates the number and types of trips originating and terminating in zones. Trip distribution is the process of computing the number of trips between one zone and all other. A trip matrix is drawn up with the sums of rows indicating the total number of trips originating in zone i and the sums of columns the total number of destinations attracted to zone j.

Each cell in the matrix indicates the number of trips that go from each origin zone to each destination zone. The trips on the diagonal are intra-zonal trips, trips that originate and end in the same zone. The balancing equation is implemented in a series of steps that include modeling the number of trips originating in each cases, adding in trips originating from outside the study area(external trips), and statistically balancing the origins and destinations.

This is done in the trip generation stage. But, it is essential that the step should have been completed for the trip distribution to be implemented. Two trip distribution matrices need to be distinguished. The first is the observed distribution. This is the actual number of trips that are observed traveling between each origin zone and each destination zone. It is calculated by simply enumerating the number of trips by each origin-destination combination. It is also called trip-link. The second distribution is a model of the trip distribution matrix, called the predicted distribution.

Generally trips should be distributed over the area proportionally to the attractiveness of activities and inversely proportional to the travel resistances between areas. It is assumed that the trips between zones will be by the most direct or cheapest routes and, taking each zone in turn, a minimum path is traced out to all other zones to form a minimum path tree. The trip distribution is a model of travel between zones-trips or links. The modeled trip distribution can then be compared to the actual distribution to see whether the model produces a reasonable approximation.

Read about: Zoning of Land for OD Survey , Traffic Volume Count , Origin Destination Survey Methods

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trip-rate noun

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What does the noun trip-rate mean?

There is one meaning in OED's entry for the noun trip-rate . See ‘Meaning & use’ for definition, usage, and quotation evidence.

Entry status

OED is undergoing a continuous programme of revision to modernize and improve definitions. This entry has not yet been fully revised.

How common is the noun trip-rate ?

Where does the noun trip-rate come from.

Earliest known use

The earliest known use of the noun trip-rate is in the 1900s.

OED's only evidence for trip-rate is from 1901, in Westminster Gazette .

trip-rate is formed within English, by compounding.

Etymons: trip n. 1 , rate n. 1

Nearby entries

tripping, adj. 1562–
trippingly, adv. 1600–
trippist, n. 1792–
trippkeite, n. 1881–
tripple, n. 1880–
tripple, v.¹ a1640–
tripple, v.² 1899–
trippler, n. 1909–
trippling, n. & adj. 1901–
trippy, adj. 1969–
trip-rate, n. 1901–
triprolidine, n. 1956–
triprosthomerous, adj. 1902–
triprostyle, adj. 1841–
trip-shaft, n. 1864–
trip shunter, n. 1921–
trip-sill, n. 1905–
trip-skin, n. a1825
trip slip, n. 1876–
tripsome, adj. 1846–
trip switch, n. 1924–

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Meaning & use

Entry history for trip-rate, n..

Originally published as part of the entry for trip, n.¹

trip, n.¹ was first published in 1915; not yet revised.

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Citation details

Factsheet for trip-rate, n., browse entry.

Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models (2012)

Chapter: chapter 4 - trip generation parameters and benchmark statistics.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

47 This, and subsequent sections on transferable parameters and benchmark statistics, will fol- low a similar format. The first subsection will provide benchmark statistics and parameters from existing statewide models. This will be followed by a discussion of analytical approaches used to estimate transferable parameters. Next will be a presentation of long-distance transferable parameters and benchmarks. Each section will then conclude with rural travel parameters and benchmarks. The section on trip generation specifically touches on alternate trip generation approaches to statewide models, and statewide model trip purposes, as well as differences between urban, rural, and long-distance trip-making. This will be followed by a presentation of transferable trip production rates and guidance on making adjustments to these parameters. This section will also provide benchmark statistics on aggregate trip rates and percent trips by purpose. 4.1 Long-Distance and Rural Trip Generation Benchmark Statistics from Statewide Models and Other Sources This section of the trip generation chapter explores the characteristics of statewide models further to identify sources that could be used in comparing, developing, and recommending trip production rates for estimating rural and long-distance travel. Other statewide model statistics such as friction factors, mode choice coefficients, and peak-to-daily/time-of-day factors, and other model parameters, are summarized later in Sections 5.1, 6.1, and 7.1, which are devoted to other steps in the four-step modeling process. Other secondary sources of model parameters and benchmarks are provided for comparative purposes. Statewide Model Parameters and Benchmarks The final report for the NCHRP Statewide Model Validation Study (Cambridge Systematics, Inc., 2010d) included a series of tables describing model parameters and benchmark statistics from statewide models, including information on long-distance and rural trip purposes, where these were separated from typical urban model purposes. Some of this information was derived either from recent work on the NCHRP model validation report or prior work on national model research for FHWA. Establishment of trip purposes used in statewide models is important because this will largely determine the stratifications used in subsequent model statistics (i.e., these are reported by trip purpose). Some trip purposes in statewide models are duplicative, using different names but meaning the same thing. This has been fleshed out through discussions with state DOT contacts C h a p t e r 4 Trip Generation Parameters and Benchmark Statistics

48 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models and their consultants. Some models differentiate short-distance from long-distance trip purposes while others do not. Where long-distance trips are separated from routine travel, the percent of long-distance trips varies widely in statewide models from less than 1 percent (Florida, Louisiana) to greater than 4 percent (Massachusetts); this may reflect, to some extent, the close proximity of densely developed urbanized areas, resulting high levels of through-trip activity, long-distance commuting, and other unique factors that make transferability of this statistic difficult. Also, reported statistics make use of different thresholds for long-distance travel. Trip generation model statistics compiled by trip purpose include aggregate trip rates and percent trips by purpose. In many cases, states have incorporated methods for forecasting long- distance trips along with shorter regional trips in their statewide models. In most cases, statewide models incorporate truck and auto long-distance trips; however, in some cases, additional modes are incorporated such as air and intercity transit. The threshold for defining long-distance trips also varies among statewide models, with some states considering trips over 100 miles to be long distance, and others considering 50 miles or 75 minutes as long distance. Table 4.1 is a summary of the percentage of trips that are long-distance for each statewide model, along with a breakdown of each stateâs definition for long-distance trips, as reported in available technical reports. Long-distance trip production rates were documented for Georgia and Wisconsin statewide models only, as depicted in Tables 4.2 and 4.3. No trip attraction rates were found for rural or long-distance travel in any of statewide model documents reviewed. These tables, as well as other statewide model statistics found in subsequent sections, depict passenger trips except where noted otherwise. The numbers found in these tables, in all cases, came directly from statewide model technical reports because the study team was not tasked with obtaining and executing these models. Bureau of Transportation Statistics In May 2006, the Bureau of Transportation Statistics (BTS) published findings from the 2001 NHTS on long-distance trip-making. A number of these statistics could be useful as transferable parameters or benchmark statistics against which to compare statewide model results. Although later tasks in this study will include data analysis of 2009 NHTS, 2001 NHTS, 1995 ATS, and other relevant state survey datasets, it was thought that information from the BTS report, America on the Go, Findings from the National Household Travel Survey (U.S. Department of Transportation, Long-Distance Threshold Long-Distance Total Percentage in Miles in Minutes Trips Trips Long Distance Arizona 50 â â â â California 100 â â â â Florida 50 â 176,587 52,281,363 0.34% Georgia â 75 418,000 31,223,000 1.34% Indiana â â 280,395 25,158,208 1.11% Louisiana 100 â 75,087 11,717,965 0.64% Massachusetts â â 957,046 22,951,483 4.17% Mississippi 100 â 212,862 7,095,161 3.00% Ohio 50 â 248,628 36,702,991 0.60% Utah â â 68,866 7,313,412 0.94% Virginia â 100 1,071,566 37,868,443 2.83% Wisconsin 50 â 42,966 71,313,993 0.06% Table 4.1. Percentages of long-distance trips in statewide models.

trip Generation parameters and Benchmark Statistics 49 Table 4.2. Georgia long-distance internal and external trip rates by purpose, income, area, and persons per household. Income Area Persons per Household HBW-IE (GA Int-Ext) HBW-II (GA Internal) HBO-II (GA Internal) NHB-II (GA Internal) Low Urban 1 0.008 0.001 0.036 0.005 2 0.045 0.002 0.063 0.009 3 0.025 0.003 0.083 0.020 4 0.077 0.005 0.060 0.154 Rural 1 0.045 0.045 0.016 0.010 2 0.020 0.043 0.087 0.130 3 0.091 0.003 0.045 0.040 4 0.056 0.167 0.667 0.056 Non-Low Urban 1 0.016 0.003 0.013 0.010 2 0.046 0.005 0.041 0.017 3 0.051 0.009 0.041 0.054 4 0.051 0.015 0.127 0.036 Rural 1 0.015 0.002 0.032 0.021 2 0.035 0.022 0.104 0.042 3 0.052 0.007 0.095 0.087 4 0.070 0.022 0.081 0.059 Source: Atkins, Development of Statewide Model Draft Report, prepared for Georgia Department of Transportation, April 15, 2011. 1 Household Member 2 Household Members 3 Household Members 4 Household Members 0 Autos 1 Auto 2 Autos 0 Autos 1 Auto 2 Autos 0 Autos 1 Auto 2 Autos 0 Autos 1 Auto 2 Autos Business Appleton/Oshkosh/Green Bay 0.00000 0.00367 0.01316 0.00000 0.00479 0.01997 0.00000 0.00057 0.02917 0.00000 0.00792 0.03429 Madison 0.00046 0.00545 0.02347 0.00000 0.00624 0.02737 0.00000 0.01451 0.02480 0.00000 0.06359 0.03617 All other MPOs 0.00145 0.00633 0.03949 0.00145 0.01656 0.02953 0.00145 0.00829 0.04266 0.00145 0.02101 0.04851 SEWRPC Region 0.00148 0.00405 0.00851 0.00148 0.00370 0.02202 0.00148 0.00251 0.01687 0.00148 0.00909 0.03399 Rest of Wisconsin 0.00060 0.00647 0.04464 0.00060 0.01875 0.02487 0.00060 0.00590 0.04473 0.00060 0.02524 0.05001 Personal Business Appleton/Oshkosh/Green Bay 0.00133 0.00293 0.00576 0.00133 0.01358 0.01133 0.00133 0.01370 0.02396 0.00133 0.01475 0.02079 Madison 0.00352 0.00427 0.00583 0.00352 0.01175 0.01246 0.00352 0.01092 0.01461 0.00352 0.00836 0.02078 All other MPOs 0.00107 0.00436 0.01110 0.00107 0.01415 0.02099 0.00107 0.01373 0.03127 0.00107 0.03690 0.02986 SEWRPC Region 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 0.00895 Rest of Wisconsin 0.00077 0.00479 0.01266 0.00077 0.01523 0.02234 0.00077 0.01025 0.03376 0.00077 0.04535 0.03144 Pleasure Appleton/Oshkosh/Green Bay 0.00752 0.01399 0.02085 0.00752 0.05138 0.06096 0.00752 0.05508 0.07750 0.00752 0.05330 0.12458 Madison 0.01538 0.01773 0.01712 0.01538 0.04875 0.06335 0.01538 0.04614 0.08682 0.01538 0.07146 0.12193 All other MPOs 0.00684 0.01829 0.02851 0.00684 0.04550 0.07099 0.00684 0.04713 0.08072 0.00684 0.06755 0.09718 SEWRPC Region 0.00717 0.01443 0.01740 0.00717 0.03076 0.05512 0.00717 0.03350 0.06772 0.00717 0.02557 0.07727 Rest of Wisconsin 0.00440 0.01575 0.26130 0.00440 0.03908 0.05959 0.00440 0.05192 0.08034 0.00440 0.08730 0.10344 Source: Cambridge Systematics, Inc. and HNTB, Wisconsin Statewide Model âPassenger and Freight Models, prepared for Wisconsin Department of Transportation, September 2006. Table 4.3. Wisconsin daily long-distance trip rates by purpose, household size, and number of autos.

50 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models Research and Innovative Technology Administration, Bureau of Transportation Statistics, 2006) fit into the context of this discussion. This 2006 BTS analysis of long-distance trips identified characteristics such as percent of trips by mode and purpose, as depicted in Table 4.4. According to BTSâ analysis, over 50 per- cent of long-distance trips would be considered for the purposes of pleasure, with another 16 percent of trips occurring for business purposes. Travel modes are fairly consistent for most long-distance purposes with the exception of business trips, which are far more likely to use air travel than other long-distance trip purposes. This shows the importance of modeling long- distance business trips separately from other LD trip types when modeling multiple transpor- tation modes. Oak Ridge National Laboratories A 2006 report, Trends in New York State Long-Distance Travel (Oak Ridge National Labora- tory, 2006), produced by staff from the Oak Ridge National Laboratory, provides a number of statistics on long-distance travel patterns based on analyses of 1995 ATS and 2001 NHTS data for residents of, and visitors to, the State of New York. Statistics provided in this report include growth in long-distance trips, the number of person trips, trips per person, miles per person, and miles per trip tabulated by means of transportation, trip purpose, income, age, and gen- der. These statistics are not necessarily transferable to other states but could be useful in benchmark checking against comparable statistics calculated from other surveys or for other states. A 2009 NHTS Update to this report is under way and select chapters are available for downloading (https://www.dot.ny.gov/divisions/policy-and-strategy/darb/dai-unit/ttss/nhts/ 2009-comparision-report). Statewide Travel Surveys by State DOTs The Ohio statewide model considers long-distance trips to be 50 miles or greater, excluding work tours (Ohio Department of Transportation, Report-Ohio-LongDistanceTravelModule- Extracted.pdf Section 4.7, LDT). Background information on the Ohio Long-Distance Travel Survey was provided in Section 2.5 of this report. Attention was focused on summary statis- tics that already were reported in available survey documentation. Section 2.5 provides graphs depicting the frequency of, and travel modes used in, long-distance trip-making based on the Ohio surveys. In addition to concerns over resource sufficiency to analyze additional state data- sets using SAS as part of this research effort, there is the issue of whether or not a supplemental Percent Trips by Mode LD Purpose Percent by Purpose Personal Vehicle Air Bus Train Other Pleasure 55.5% 90.4% 6.7% 2.2% 0.5% 0.2% Business 15.9% 79.3% 17.8% 0.8% 1.6% 0.5% Commuting 12.6% 96.4% 1.5% 0.5% 1.7% 0.0% Personal Business 12.6% 89.3% 4.7% 5.6% 0.3% 0.1% Other 3.4% 96.6% 1.9% 0.5% 0.0% 1.0% Total 100.0% 89.5% 7.4% 2.1% 0.8% 0.2% Source: BTS. Table 4.4. 2001 long-distance trips by purpose and mode.

trip Generation parameters and Benchmark Statistics 51 analysis of state DOT datasets would provide the same results as previously reported, due to weighting/expansion and tools used in the analysis. The Ohio statewide travel survey documentation provided to the study team did include person trip rates per household (7.78) and person (4.94) for rural versus urban settings (7.56â8.76 per HH and 4.83â5.49 per person, depending on the specific urban area). The survey documentation, however, does not include long-distance trip rates, trips by pur- pose, trip distribution factors, average trip lengths, mode splits, or auto occupancy rates. Beyond sociodemographic characteristics of survey respondents and the graphs and charts previously depicted in earlier chapters of this Guidebook, information was also provided on the number of stops (60.9 percent made stops) for long-distance trips and the percent of nonhome-based long-distance trips (53 percent). Section 2.6 of this report provides background information on the Michigan surveys, includ- ing annual long-distance household trip rates (7.34 in 2004 and 6.25 in 2009), trip purposes, travel modes, and long-distance trip distribution by state. The difference between Michigan long-distance trip rates and those based on ATS (10.15) and 2001 NHTS (12.32) excluding 50â100 mile trips, indicate potential issues of transferability as long-distance trips were defined as 100 miles or greater in the Michigan surveys. Considerable information was provided in the Michigan documentation about trip characteristics that are more relevant to the discussion of rural trip rates in Chapter 3 of this report. The Michigan statewide household survey documentation provided household person trip rates for different urban and rural stratifications. Table 4.5 depicts person trip rates per house- hold for the first and second Michigan Travel Counts Surveys (2004 and 2009, respectively). Nonurbanized and rural household and person trip rates are depicted in bold underlined text, Sample Areas Households Weighted Persons Weighted HH Trip Rates Person Trip Rates Unweighted Weighted Unweighted Weighted Estimated Number of HHs, Persons, and Trips by MTC I (2004) SEMCOG 1,846,277 4,638,216 9.09 9.14 3.62 3.64 Small Cities (0-50k pop) 129,369 296,162 9.74 8.82 3.89 3.85 Upper Peninsula Rural 87,115 209,919 8.35 8.40 3.49 3.49 Northern Lower Peninsula Rural 206,210 501,075 8.08 7.96 3.30 3.27 Southern Lower Peninsula Rural 394,588 1,044,969 8.98 9.41 3.53 3.55 TMAs 579,415 1,465,017 9.68 9.53 3.78 3.77 Small Urban Modeled Areas 545,557 1,360,511 9.29 9.39 3.75 3.77 State Total 3,788,531 9,515,870 9.05 9.17 3.63 3.65 Estimated Number of HHs, Persons, and Trips by MTC II (2009) SEMCOG 2,071,786 4,820,277 7.73 8.60 3.66 3.70 Small Cities (0-50k pop) 147,121 315,640 9.10 8.14 3.96 3.80 Upper Peninsula Rural 90,553 212,970 7.29 7.64 3.28 3.25 Northern Lower Peninsula Rural 218,238 520,125 7.32 7.93 3.30 3.33 Southern Lower Peninsula Rural 412,944 1,078,905 8.07 9.15 3.43 3.50 TMAs 622,928 1,519,419 8.34 8.83 3.65 3.62 Small Urban Modeled Areas 593,556 1,399,086 7.87 8.72 3.68 3.70 State Total 4,157,125 9,866,421 7.97 8.63 3.57 3.64 Source: Michigan Travel Counts Surveys. Table 4.5. Michigan TCS rural versus urban household trip rates.

52 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models separately for small cities, upper peninsula, northern lower peninsula, and southern lower peninsula. With the exception of the more heavily populated southern lower peninsula, non- urbanized households exhibit lower weighted trip rates than urban households. This finding is somewhat contrary to the 2009 NHTS analysis in Appendix G, unless most of the Michigan urbanized household surveys were conducted in suburban settings, since suburban house- holds showed higher trip rates than rural households in the 2009 NHTS. It is also worth noting that the differences found among nonurbanized areas of Michigan might indicate limitations to transferability. Recent and Ongoing GPS Surveys in the United States Table 4.6 depicts the split between weekend and weekday travel from recent GPS surveys described earlier in Section 2.3 of this Guidebook. These splits, after survey expansion, are simi- lar in each survey. In all cases, as expected, the percent of weekend trips is highest for the long- distance trips (most 30 percent or higher) when compared against urban and rural travel. Table 4.7 depicts four different trip/demographic measurements for each of the four surveys and overall, for the same three different geographic definitions found in the prior two tables. Daily trip production rates for long-distance trips are low, as expected. Rural trip production rates are substantially higher than urban rates for these four surveys, although the rates are comparable to analysis of 2009 NHTS, as described later in this chapter. Long-Distance Rural Urban Weekday Weekend Weekday Weekend Weekday Weekend Overall 68% 32% 77% 23% 79% 21% Atlanta 66% 34% 75% 25% 79% 21% Denver 72% 28% 78% 22% 80% 20% Massachusetts 68% 32% 86% 14% 88% 12% Chicago 64% 36% 73% 27% 75% 25% Source: Geostats based on recent GPS-based travel surveys. Table 4.6. Travel day statistics from recent GPS-based surveys. Number of Trips Trip Production Rate Average Household Size Average Number of Vehicles/HH L on g D is ta n ce R u ra l U rb an A ll T ri p s L on g D is ta n ce R u ra l U rb an L on g D is ta n ce R u ra l U rb an L on g D is ta n ce R u ra l U rb an Overall 1,253 31,689 103,832 6.20 0.04 9.39 5.23 2.85 2.86 2.75 2.23 2.18 2.05 Atlanta 580 16,932 48,098 5.92 0.03 8.24 5.03 2.89 2.85 2.77 Denver 395 9,836 31,377 6.11 0.04 9.42 4.85 2.77 2.81 2.68 2.22 2.16 2.07 Massachusetts 176 3,349 10,325 5.90 0.04 13.56 5.04 2.92 2.96 2.82 2.27 2.21 2.02 Chicago 102 1,572 14,032 7.89 0.05 8.86 7.05 Source: Geostats based on recent GPS-based travel surveys. Table 4.7. Trips and households from recent GPS-based surveys.

trip Generation parameters and Benchmark Statistics 53 2010 Travel Survey of Residents of Canada As described in Appendix B, the Travel Survey of Residents of Canada (TSRC) is designed to measure the size and status of Canadaâs tourism industry at the national level. Through direct contact with Canadian officials, the research team was able to obtain a spreadsheet data analysis of the 2010 TSRC. Without direct access to the data, which would have required additional budget for purchasing data, this study was limited to information provided in this spreadsheet. Table 4.8 depicts the percent of long-distance trips by purpose, with single-day travel sepa- rated from overnight travel. Although the trip purposes used in the TSRC are different from those found in the ATS, business-related trips are considerably less in the TSRC, at slightly more than 5 percent, versus the ATS at 22 percent. These statistics are from fully weighted survey data; however, without additional analysis, it is unclear whether the lower percent is a function of sampling or that long-distance business travel is considerably less common than in the United States. 4.2 Analytical Approach to Estimating Long-Distance and Rural Trip Generation Parameters and Benchmarks One key to implementing the analytical plan and developing transferable parameters was to obtain access to all datasets from the American Travel Survey (ATS) and identify trip purposes, average trip lengths, vehicle occupancies, and other statistics typified by long-distance travelers. The 1995 ATS datasets are dated; however, these data are the only long-distance data that provide statistically sound estimates of long-distance travel in and between the states. Although the 2001 National Household Travel Survey (NHTS) had a long-distance compo- nent, this survey did not have sufficient samples to calculate estimates of long-distance travel for most states (New York and Wisconsin were exceptions to this, because of the large Add-On in the former and stratified sampling of the latter, although neither Add-On was included in the official 2001 NHTS long-distance file). The approach to using NHTS 2001 data was based on discussions with FHWA NHTS support staff, both past and present, as well as members of the research team with extensive experience using different versions of the NHTS. All of these dis- cussions pointed to concerns over the use of NHTS 2001 for long-distance trips and at least some Trip Duration: Total â Domestic Travel (Age 18+): Person Trips with the Destination in Canada *** Row Percents *** Main Trip Purpose: Standard person trip stub variables Total Pleasure, Vacation, Holiday Visiting Friends or Relatives Business and All Conferences or Conventions Shopping and Other >>> Final Data <<< Total Long-Distance Trips: 100.00% 36.99% 46.72% 5.40% 10.90% Single-Day Long-Distance Trips 100.00% 34.43% 45.59% 5.14% 14.84% Overnight Long-Distance Trips 100.00% 40.79% 48.41% 5.77% 5.03% Source: Travel Survey of Residents of Canada. Table 4.8. Canadian residentsâ long-distance trips by percent purpose.

54 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models of these concerns are documented elsewhere in this report. All of the NHTS 2001 long-distance data, including state Add-On samples, were made available for use by the research team as well. These two long-distance datasets can be used together, yet separately, since the 2001 ques- tionnaire relied heavily on the 1995 ATS as a template. Definitional categories for mode and purpose are comparable. The study team also obtained readily available state DOT survey data and documentation from statewide household travel surveys for Michigan and Ohio. The research team also coordinated with Canadian officials to identify available long-distance travel parameters readily available from their recent household travel surveys. Finally, recent travel surveys using Global Positioning Systems (GPS) were mined for parameters on long-distance travel as well as rural parameters. Transferable rural travel parameters largely focused on the 2009 NHTS and its state Add-On surveys. Analysis of variance (ANOVA) and other statistical tests were run on 2009 NHTS data in an attempt to identify which available attributes best explain differences in rural trip-making and whether certain parameters should be stratified for different conditions such as urban clusters and proximity to urbanized area boundaries. Existing statewide models also played a significant role in this analytical plan, in terms of quantifying reasonableness ranges against which to compare resulting ATS/NHTS survey-based model parameters. Also, documented model parameters were identified for potential transfer- ability to other statewide models, based on the characteristics of the state where the data were collected versus the state to which a parameter might be proposed for transferability. Inter- regional or intercity travel components are included in some statewide models to capture both intrastate and interstate trips. The core model design feature is the recognition that interregional travel is very different from urban area travel, where different sets of explanatory variables or different sensitivities to levels of service are involved. A set of typical long-distance and rural trip purposes was established from this analysis so that model parameters could be stratified by such categories and reasonableness benchmarks could be established for percent trips by purpose. Mean trip length statistics, both in miles and min- utes, also were estimated from the survey databases for use as benchmarks in future statewide model validation efforts; however, the survey analysis for this study did not include the calculation of state-by-state trip lengths. As discussed previously, statewide models and travel surveys have used a range of thresholds to define long-distance trip-making. Most sources cited in this study used either 50, 75, or 100 miles as the minimum threshold for trips to be considered âlong-distance.â In an effort to maximize the number of long-distance trip samples, this report looks at model parameters at three different long-distance trip thresholds: 50â100 miles, 100â300 miles, and more than 300 miles. By separat- ing out 50â100 mile trips from 100â300 miles, this allows for differentiation of long-distance trips by the two most common thresholds, beginning and ending at 100 miles. The rationale for using 300 miles as another cutoff point is that preliminary data analysis indicated a mode shift from personal auto to air travel at this distance. The remainder of this section of the chapter on trip generation focuses on the data sources used for parameter estimation, along with some general comparisons among sources. American Travel Survey (ATS) As stated elsewhere, the 1995 ATS is still seen as the most robust sample of long-distance travel behavior, in spite of its age. The ATS was entirely focused on long-distance travel, unlike the 2001 NHTS, which also surveyed typical daily urban and rural travel patterns. There are numerous ways to analyze the data. For this study, household frequencies and statistical means were cal- culated separately for all households and per capita as well. Trip rates and frequencies also were

trip Generation parameters and Benchmark Statistics 55 calculated separately for annual and daily conditions. The reason for this is that long-distance trips are not an âevery dayâ occurrence for most households. Trip rates were initially calculated on an annual basis using the ATS and then divided by 365 to provide daily trip rates as an option for users of this report. Since the ATS reported annual trips, long-distance trip characteristics in this Guidebook are likewise summarized as annual trips. Another consideration was whether to include weekdays and/or weekends, of which both were calculated. According to available documentation on the ATS âeach trip was classified as a weekend trip or as not a weekend trip. A weekend trip is a trip of one to five nights, includ- ing a Friday and/or Saturday night stay. Travelers who stay one or two nights away, including a Friday or Saturday night are defined as regular weekend travelers. Those who stay three to five nights away, including a Friday and/or Saturday night stay are defined as long weekend travelersâ (http://www.bts.gov/publications/1995_american_travel_survey/an_overview_ of_the_survey_design_and_methodology/index.html). Based on this description it was not practical to summarize only weekday trips because some of the âweekendâ trips were partially âweekdayâ trips. Furthermore, analysis of weekday-only trips resulted in a dramatic drop in long-distance trip rates that would be inconsistent with results from other long-distance surveys. Therefore, long-distance trip statistics found in this report include both weekday and weekend trips. Cross-classification was used to evaluate different attributes against one another, as well as to calculate trip rates based on socioeconomic characteristics. The latter used household size by income, which is consistent with one cross-classification scheme used and documented in the previously referenced NCHRP Report 716 on urban transferable parameters. It is important to note that the ATS did not include trips of 50â100 miles in length, and so there are no 50- to 100-mile trips in the ATS statistical tables in this chapter. Statistics reported in the next section on parameters from the 2001 NHTS do include 50- to 100-mile trips, consistent with the lower long-distance trip threshold used in that survey. Statistics for 50- to 100-mile trips are presented only for analysis of the 2001 NHTS survey. Table 4.9 is an assessment of household trip rates by trip purpose and the relevant trip dis- tance categories noted earlier. As shown in this table, a typical household generates 10.15 trips of over 100 miles, or 0.0278 daily long-distance trips (annual trip rate divided by 365 days). Trips were grouped into three purposes: business, pleasure, and personal business. Pleasure trips had the highest average trip rate for all three distance categories while the majority of trips were 100â300 miles for all trip purposes. Table 4.10 compares trip rates by household income level and mileage range. A review of this table shows that the highest-income group has the greatest long-distance trip rate for both mile- age categories. Trip rates show that the propensity of making long-distance trips, and the length of those trips, has a lot to do with household income. Purpose -300 miles > 300 miles Total 01 Business 1.37 0.91 2.28 02 Pleasure 4.08 2.13 6.21 03 Personal Business 1.19 0.47 1.66 Total 6.64 3.51 10.15 Source: 1995 ATS. Table 4.9. ATS annual trip rates by distance/purpose, round-trip.

56 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models Table 4.11 provides information from the 1995 ATS on weekday versus weekend long-distance travel based on three trip purposes. Not surprisingly, business and personal business trips are less likely to occur on weekends when compared to pleasure trips, of which over 50 percent involve weekend travel. As noted earlier, some portion of the âweekendâ trips in fact take place on weekdays, and so the term ânot weekendâ was chosen instead of âweekday.â Simply put, the ânot weekendâ trips are those that take place entirely on weekdays, with no portion of the long- distance trip taking place on a weekend. Table 4.12 presents the number of long-distance intermediate stops by trip/tour purpose. More than 90 percent of long-distance trips did not include any stops. The percent of intermedi- ate stops is highest for business trips and lowest for personal business trips. The trip, including all intermediate stops, could be analogous to the concept of a trip tour. 2001 National Household Travel Survey The 2001 NHTS database also was used to develop long-distance model parameters and benchmarks similar to those produced using the 1995 ATS. A primary reason for developing these statistics using the 2001 NHTS was to overcome concerns about the age of the 1995 ATS data. Use of the 2001 NHTS was considered acceptable for long-distance analysis because this survey included a targeted sample of long-distance trips, unlike the more recent 2009 NHTS database. Unfortunately, there are several shortcomings with the 2001 long-distance survey com- ponent, including the following: â¢ Much lower response rate using a telephone survey approach for the 2001 NHTS versus the panel survey approach used in the 1995 ATS; â¢ Shorter recall period of the 2001 NHTS also resulted in a much smaller sample size of long- distance trip-makers (45,000 in 2001 NHTS versus 550,000 in 1995 ATS); Income -300 miles > 300 mi Total 01 $0-$24,999 2.97 1.30 4.27 02 $25,000-$99,999 8.48 4.34 12.82 03 $ 100,000+ 13.78 12.49 26.26 Total 6.64 3.51 10.15 Source: 1995 ATS. Table 4.10. ATS annual trip rates by distance/income, round-trip. Purpose Not Weekend Weekend Total Percent Weekend 01 Business 85,261 33,910 119,171 28.45% 02 Pleasure 156,188 159,595 315,783 50.54% 03 Personal Business/Other 67,097 33,469 100,566 33.28% Total 308,546 226,974 535,520 Trip % 58% 42% 100% Source: 1995 ATS. Table 4.11. ATS annual frequency by purpose/weekend trip, round-trip.

trip Generation parameters and Benchmark Statistics 57 â¢ The impacts of 9/11 on travel resulted in a much lower share of air travel in the 2001 NHTS when compared against the 1995 ATS; and â¢ Thresholds used to define long-distance trips differ between the two surveys with 1995 ATS defined as 100 miles or greater and 2001 NHTS as more than 50 miles. Chapter 2 of this Guidebook described the latter difference as a potential strong point of NHTS 2001 as a way of obtaining information on these mid-range 50â100 mile trips. Analysis of 2001 NHTS long-distance trip data showed a sizeable sample of 50â100 mile trips (21,500 out of 45,000 trips, or nearly 48 percent of the 2001 NHTS long-distance sample), such that exclusion of these trips for NHTS 2001 statistics would be problematic. Hence, 50â100 mile trips were included for NHTS 2001 analyses. Table 4.13 depicts annual long-distance household trip rates from the 2001 NHTS, including 50â100 mile trips. The overall trip rate (excluding the 50â100 mile trips) is 12.32, about 21 percent higher than the 1995 ATS trip rate of 10.15. The patterns within the cross-classification table are relatively similar between the two surveys (i.e., which cells have higher or lower rates than others). It is worth noting that the percentages of trips by purpose are somewhat similar between the two long-distance surveys, as follows: â¢ Businessâ28.38 percent for NHTS 2001 versus 22.25 percent for ATS; â¢ Pleasureâ54.84 percent for NHTS 2001 versus 58.97 percent for ATS; and â¢ Personal Businessâ16.78 percent for NHTS 2001 versus 18.78 percent for ATS. Rural Typologies Identification of rural travel parameters took a different focus than long-distance travel param- eters. First, rural trip-making data are well represented in the recent 2009 NHTS. Therefore, the Purpose 2 3 4 Total Percent Stop/ Purpose 01 Business 106,212 2,059 7,515 3,385 119,171 10.87% 02 Pleasure 293,727 8,243 8,412 5,401 315,783 6.98% 03 Personal Business 94,888 2,470 1,998 1,210 100,566 5.65% Total 494,827 12,772 17,925 9,996 535,520 7.60% Percent Stops by Number 92.40% 2.38% 3.35% 1.87% 100.00% Source: 1995 ATS. Table 4.12. Annual frequency by stops from destination/purpose, round-trip. Purpose 50-100 Miles 100-300 Miles > 300 Miles Total 01 Business 4.04 1.85 0.97 6.85 02 Pleasure 5.71 5.08 2.39 13.17 03 Personal Business/Other 1.78 1.52 0.52 3.83 Total 11.53 8.45 3.87 23.85 100+ Mile Trip Rate 12.32 Source: 2001 NHTS. Table 4.13. 2001 NHTS annual trip rates by distance/purpose.

58 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models research team was able to focus primarily on this one survey database, unlike the multiple and considerably older survey databases used to identify long-distance travel parameters. Second, the points of reference are quite different for rural trips. Long-distance travel characteristics were generally summarized by different trip length categories, whereas rural travel parameters required establishing typologies for classification and comparison against comparable statistics on travel in urbanized areas. Finally, the temporal issues for rural travel are not as complex as long-distance trips. For example, the database does not deal with international travel or multiple stops, and the greater share of travel is on the weekdays, with a much smaller share of weekend travel than with long-distance trips. The first step in the assessment of rural travel parameters was the identification of rural typolo- gies and an exploration of how these different typologies can be used to describe the trip-making of rural households. This also includes the need to define what is and is not rural travel and how typical rural travel behavior differs from that in more urban settings. These efforts started with a focus on attributes contained within the NHTS 2009 âDOT versionâ of the database, including the Claritas attributes described earlier in this Guidebook. The following similar, yet not identical, attributes from the 2009 NHTS DOT version were used to identify potential rural typologies: â¢ URBANâIdentifies whether or not the home address is located in an urban area, typically defined as a concentrated area with a population of 50,000 or greater; â¢ URBRURâIdentifies whether or not the home address is located in a rural area; â¢ URBANSIZEâPopulation size of the urban area in which the home address is located; â¢ HBHURâUrban/Rural Indicator, appended to the NHTS by Claritas (http://nhts.ornl. gov/2009/pub/UsersGuideClaritas.pdf)âthis classification reflects the population density of a grid square into which the householdâs block falls; â¢ HBRESDNâThe number of housing units per square mile by block group; and â¢ HBPOPDNâThe population per square mile by block group. Additionally, the rural typologies recommended as part of NCHRP Project 25-36, âImpacts of Land Use Strategies on Travel Behavior in Small Communities and Rural Areasâ and described earlier also were considered in this effort. The four typologies recommended by NCHRP Proj- ect 25-36 were as follows, along with the study definitions of each, as quantified by âcommuting zonesâ developed by the USDAâs Economic Research Service: â¢ Population DensityâComputed as number of people divided by unit area of developed or developable land; â¢ Road DensityâCalculated as road length in miles per square mile of developed or develop- able land; â¢ Land-Use MixtureâA proxy of land-use mixture measuring how residents, jobs, and other activities are distributed in relation to each other; and â¢ Variation in Population DensityâVariation in population density distinguished where most residents are located in a relatively small set of concentrated areas at relatively high densities from locations where residents are spread more evenly. This project did not pursue full consideration of commuting zones, which are defined in NCHRP Project 25-36 as âmulticounty regions that convey the typical pattern of commuting trips in a spatially defined labor market: a much higher proportion of commuting trips have origins and destinations that are both inside the zone than those trips for which one end is outsideâ (Department of City and Regional Planning Center for Urban and Regional Studies, University of North Carolina at Chapel Hill, 2011). In place of data on commuting zones, the analysis presented here uses readily available data to simulate some of these typologies. Population Density already was an attribute included in

trip Generation parameters and Benchmark Statistics 59 the 2009 NHTS dataset so it was easily addressed. Road Density was calculated using the 2005 National Highway Planning Network and geographic information systems (GIS) tools, based on a simple formula of Road Length/Census Tract Area. The resulting Road Density was a con- tinuous variable, so a regression analysis was conducted and then the variable was re-coded as a categorical variable. There was no practical way to simulate land-use mixture or the variation in population density using the data readily available for this project. One additional typology analyzed was âurban proximityâ because the NCHRP Project 8-84 research team thought that the proximity to urban areas could impact the number and purpose of trips. Latitude/longitude address information was not stored for each household in the 2009 NHTS DOT database, which is necessary for accurate depiction in GIS. The database did have Census tract and block group information, and this information was appended to an NHTS Census tract/block group shapefile. Once the 2009 NHTS DOT database was joined to the NHTS CT/BG shapefile by a block group ID number, the households were spatially referenced to the block group. Figure 4.1 depicts a map of concentric rings formed during the proximity analysis, zoomed into north Florida/south Georgia, as an example. In cases where a block group was in proximity to multiple urban areas, distance to the closest urban area was applied. Unfortunately, Proximity to Urban Area did not show any clear trip rate trend, and so the analysis focused on the other measures. As noted previously, while it would have been ideal to use a national land coverage database to identify subcategories of rural areas such as exurban, agricultural, and recreational, the research Figure 4.1. Example map depicting proximity to urban area.

60 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models team had concerns over how to define and classify rural areas. It was thought that proximity to urbanized area, residential density, and roadway density allowed for a more objective classification of rural households. In order to narrow the number of rural typologies used in recommending transferable rural parameters, analysis of variance (ANOVA) and t-tests were conducted on each of the typolo- gies discussed below. The trending of trip rates up or down in relation to different settings for each attribute also was reviewed to further ascertain the explanatory power of each typology variable. In some cases, the number of categories was narrowed to assess the viability of each. Appendix G includes a separate page for each typology variable, along with t-test values for each category, analytical and trend observations, and revisions to the number of categories for each attribute. Four typologies were subsequently recommended for the purposes of calculating transferable rural trip production rates. These four typology variables were as follows: 1. HBHURâUrban/Rural Indicator reflecting population density of a grid square; 2. URBANâWhether or not the home address is located in an urban area; 3. URBRURâWhether or not the home address is located in a rural area; and 4. HBRESDNâNumber of housing units per square mile by block group. Table 4.14 depicts how these typology attributes could interact and be cross-classified into three dimensions for trip generation, focusing on the first three attributes to deal with different geog- raphies and the latter, housing units per square mile (HBRESDN), being applied against all rural and urban categories. The number of 2009 NHTS household samples by row and column also is provided in parentheses. Cells with âN/Aâ represent combinations of three attributes that should not exist. For example, rural areas should not also be classified into suburban, second city, or urban. This approach is further refined into a set of trip production rates cross-classified by socio- economic household characteristics, and specified by trip purpose, as described in a later section of this chapter. 2009 National Household Travel Survey With its large sample size of rural households, the 2009 NHTS was the principal source of rural travel model parameters described in this study. Based on the rural typology analysis described in the previous section, four trip production cross-classification matrix sets were prepared. As with long-distance rates, socioeconomic data used in the cross-classification scheme are income (three categories) by household size (five categories). Each set includes separate trip rates for home-based work, home-based nonwork, and nonhome-based purposes, as well as separate rates for each substrata included with the typology. The substrata for each cross-classification set are depicted below (the underlined strata reflect the rural components/subsets of each set): â¢ HBHURâTown and Country, Suburban Areas, Secondary City, Urban All; â¢ URBANâNot in Urbanized Area, Urbanized Area; â¢ URBRURâIn Rural Area, In Urban Area; and â¢ HBRESDNâLow Density (0â999 units/square mile), Medium Density (1,000â9,999 units/ square mile), High Density (10,000+ units/square mile). Although the definition of what is predominantly rural changes from one attribute/set to another, if the underlined categories above are compared, there is little difference in the trip rates. For example, the total number of person trips per household for all trip purposes is 9.72 for URBRUR, 9.83 for URBAN, and 9.56 for HBHUR. A more significant difference is shown using HBRESDN with a total trip rate of 11.76, although it is not clear how much of this lower housing unit density category consists of rural households. Appendix H depicts trip rates for

trip Generation parameters and Benchmark Statistics 61 URBRUR URBAN HBRESDN Housing units per sq mile â Block group 0-999 (75,937) 1,000-9,999 (52,450) 10,000-999,999 (2,120) Household in urban/rural area Home address in urbanized area Rural (38,014) Not in an urban area (38,014) (In an urban cluster â placeholder) N/A N/A N/A Urban (92,493) In an urban area (79,569) In an area surrounded by urban areas (51)a In an urban cluster (12,873) Household in urban/rural area Urban/Rural indicator â Block group Rural (38,014) Town and Country (38,014) Suburban (N/A) N/A N/A N/A Second City (N/A) N/A N/A N/A Urban (N/A) N/A N/A N/A Urban (92,493) Town and Country (24,227) N/A N/A N/A Suburban (30,491) Second City (23,550) Urban (14,225) Size of urban area in which home address is located Urban/Rural indicator â Block group Not in an urbanized area (50,938) Town and Country (50,938) Suburban (N/A) N/A N/A N/A Second City (N/A) N/A N/A N/A Urban (N/A) N/A N/A N/A All other categories combined â AKA urbanized (79,569) Town and Country (11,303) N/A N/A N/A Suburban (30,491) Second City (23,550) Urban (14,225) Note: Numbers in parentheses represent 2009 NHTS sample sizes for each category. Sample size numbers were found to be somewhat inconsistent among different urban/rural attributes and categories. âN/Aâ reflects an attribute combination that does not exist/would be illogical. a Probably should merge with âIn an urban areaâ due to small sample size. Table 4.14. Recommended rural typology variables.

62 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models each category of every variable analyzed for estimating transferable trip production rates. The tables in Appendix H include both rural and urban trip rates using definitions unique to each attribute/rate set. 4.3 Long-Distance Trip Generation Model Parameters For the purposes of recommending transferable long-distance parameters, it was decided to focus on the 1995 ATS due to its larger sample size and based on the similarity of trip frequencies by purpose between the ATS and 2001 NHTS long-distance component. Although there are a multitude of ways these parameters can be summarized, this report uses the following considerations in reporting long-distance model parameters: â¢ Include all days of the week (weekends and weekdays); â¢ Report parameters on an annual (rather than daily) basis; â¢ Exclude trips less than 100 miles in length; â¢ Limit analysis to domestic travel (no international trips); and â¢ Report at the household level rather than person level (per capita rates). The primary reason for including weekday and weekend travel is that ATS weekend travel includes weekday trips that include a weekend component. Furthermore, average annual daily traffic (AADT), by definition, includes both weekdays and weekends. Annual, rather than daily, trips are reported because this is how ATS trips were reported. Trips less than 100 miles in length were excluded because the ATS did not include these trips. International travel was excluded because these trips are not included in most statewide passenger models and these trips tend to skew trip length. Finally, household, rather than per capita, trip rates were selected because these are more commonly found in four-step travel demand models. All transferable parameters are calculated for the predominant three trip purposes (Business, Pleasure, and Personal Business) and total trips (All Purposes). The transferable parameters are described below and in subsequent chapters by model step. Trip Generation: Long-Distance Person Trip Production Rates For transferable long-distance trip production rates, it was decided to cross-classify socio- economic characteristics of each household in a comparable manner to NCHRP Report 716. The recommended cross-classification scheme for long-distance trip production rates is household income by household size. The correlation between income and long-distance trip-making is significant. For the purposes of cross-classification, household size is stratified into five categories, similar to NCHRP Report 716, whereas household income was collapsed into three categories. It also was decided to report annual trip rates since daily and monthly trip rates resulted in very low values. Table 4.15 depicts recommended long-distance person trip production rates for the three trip purposes. Since this study is primarily focused on passenger travel, the reader should refer to NCHRP Report 716 for a summary of sample truck trip generation rates derived from multiple sources. Appendix E of this report also provides metrics on rural versus urban truck travel. 4.4 Rural Trip Generation Model Parameters All rural travel parameters summarized in this section of the report were derived from sta- tistical analysis of 2009 NHTS datasets. As with long-distance parameters, the rural parameter discussion is divided into separate chapters reflecting each step in the typical model chain.

trip Generation parameters and Benchmark Statistics 63 Trip Generation: Rural Trip Production Rates From each of the four trip rate stratifications described in Section 4.2, URBAN showed the fewest trip rate anomalies (cells having higher or lower trip rates than expected compared to adjacent cells). Table 4.16 depicts unchained rural trip rates using URBAN as the 2009 NHTS attribute to differentiate between rural and urban households. Only two minor anomalies were identified in this table. HBW trip rates for highest-income four-person households and NHB 5+ person households were initially lower than found in adjacent trip rate cells (those with lower-income or household size). Rates were subsequently adjusted and smoothed for these two cells. The resulting total person trip rate per rural household using the URBAN attribute is 10.06, as opposed to an urbanized area trip rate of 9.91 as depicted in Appendix H. Urbanized area trip rates do vary by subcategory, such as secondary cities (9.50), suburban (10.34), and non suburban or second city urbanized (9.36). Some of these differences could possibly be minimized through testing of alternate socioeconomic cross-classification schemes. Trip rate comparisons found in Appendix G show a strong correlation between housing density and trip rates, with lowest hous- ing density trip rates being the highest at 9.60 and highest density housing trip rates the lowest at 7.77. In theory, opportunities for mixed-use development are more prevalent in higher density areas, thus reducing the trip rate, as opposed to lower density areas where mixed uses are less common, resulting in more trip-making to satisfy household needs. It is also possible that some of these differences might be explained by differences in household size, with testing of alterna- tive cross-classification schemes. Income by HH Size 1 2 3 4 5+ Total Business Trip Rates by Household Size/Income 01 <$25,000 0.44 0.80 0.88 0.99a 1.59 0.66 02 $25,000-$99,999 2.34 2.62 2.97 3.24 3.80 2.90 03 $100,000+ 3.70 8.00a 8.20 a 8.40 a 8.54 8.61 Total 0.99 2.35 2.70 3.24 3.51 2.28 Pleasure Trip Rates by Household Size/Income 01 <$25,000 1.70 3.49 3.50 a 5.10 5.15 2.77 02 $25,000-$99,999 3.88 6.70 8.42 9.77 11.93 7.84 03 $100,000+ 4.76 11.51 14.15 18.91 21.27 14.59 Total 2.32 6.03 7.48 9.46 10.77 6.21 Personal Business Trip Rates by Household Size/Income 01 <$25,000 0.41 1.12 1.16 1.51 2.05 0.84 02 $25,000-$99,999 1.03 1.63 2.15 2.66 3.75 2.08 03 $100,000+ 0.46 2.56 2.78 3.99 4.79 3.07 Total 0.58 1.53 1.94 2.53 3.38 1.66 Total Annual Trip Rates by Household Size/Income 01 <$25,000 2.54 5.41 5.48 7.28 8.79 4.27 02 $25,000-$99,999 7.25 10.95 13.54 15.67 19.48 12.82 03 $100,000+ 8.92 22.87 24.72 34.03 34.60 26.26 Total 3.89 9.91 12.12 15.22 17.66 10.15 a Indicates where estimated trip rates were manually adjusted and smoothed. Table 4.15. Annual long-distance person trip production rates.

64 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models Cross-classification matrices are also provided for auto availability and number of workers, con- sistent with cross-classification schemes documented in NCHRP Report 716. Rural trip production rates for auto availability by household size are found in Table 4.17, while number of workers by household size are depicted in Table 4.18. Overall, Table 4.17 (auto availability) has slightly lower total weekday trip rates than Table 4.16 (income). There are only three instances where the reverse is true, households with 5+ members for all purposes and NHB; and households with 3 members for HBW. Whereas Table 14.12 produced four trip rate anomalies, there are only two small anomalies in Table 14.13, depicted in underlined italics. HH Person Trip Rates: In Other (Rural) Areas, All Trip Purposes Trip Rates HH Size Income 1 2 3 4 5+ Total Less than $25,000 2.8 6.4 9.9 15.0 15.6 6.8 $25,000-$99,999 4.2 7.9 12.8 17.5 22.1 10.2 Above $100,000 5.1 8.8 14.0 20.1 26.2 14.0 Total 3.6 7.8 12.5 18.0 20.9 10.0 In Other (Rural) Areas HBW Trip Rates HH Size Income 1 2 3 4 5+ Total Less than $25,000 0.2 0.8 1.3 1.5 1.7 0.7 $25,000-$99,999 0.7 1.2 2.1 2.4 2.5 1.5 Above $100,000 0.9 1.6 2.3 2.4a 2.6 1.9 Total 0.5 1.2 2.0 2.2 2.3 1.4 In Other (Rural) Areas HBNW Trip Rates HH Size Income 1 2 3 4 5+ Total Less than $25,000 1.6 3.8 5.9 9.7 10.8 4.2 $25,000-$99,999 1.9 4.0 6.8 10.1 13.8 5.6 Above $100,000 2.2 4.0 7.2 11.4 15.2 7.4 Total 1.8 4.0 6.7 10.4 13.1 5.6 In Other (Rural) Areas NHB Trip Rates HH Size Income 1 2 3 4 5+ Total Less than $25,000 0.8 1.7 2.6 3.8 4.0a 1.7 $25,000-$99,999 1.5 2.6 3.9 4.9 5.7 3.1 Above $100,000 1.9 3.1 4.4 6.4 8.3 4.6 Total 1.3 2.5 3.8 5.2 5.4 3.0 Source: 2009 NHTS. a Indicates where estimated trip rates were manually adjusted and smoothed. Table 4.16. Rural person trip production rates: HH size by income, URBAN attribute identifies rural HHs.

trip Generation parameters and Benchmark Statistics 65 In Other (Rural) Areas, All Trip Purposes Trip Rates HH Size Autos/HH 1 2 3 4 5+ Total 0 Veh 2.3 6.2 9.1 12.0 13.5 4.9 1 Veh 3.8 6.9 11.4 14.4 15.8 6.4 2 Veh 4.6 7.9 12.4 18.2 21.4 11.8 3+ Veh 4.6 8.1 13.8 19.5 25.1 15.3 Total 3.6 7.6 12.4 17.8 20.9 9.7 In Other (Rural) Areas HBW Trip Rates HH Size Autos/HH 1 2 3 4 5+ Total 0 Veh 0.2 0.7 1.2 1.1 1.2* 0.5 1 Veh 0.5 0.8 1.2 1.5 1.4 0.8 2 Veh 0.6 1.4 2.0 2.1* 2.2 1.6 3+ Veh 1.0 1.5 2.6 3.0 3.3 2.5 Total 0.5 1.2 2.0 2.2 2.3 1.3 In Other (Rural) Areas HBNW Trip Rates HH Size Autos/HH 1 2 3 4 5+ Total 0 Veh 1.3 3.7 5.7 7.9 9.9 3.1 1 Veh 1.8 3.8 6.7 9.0 11.1 3.6 2 Veh 2.2 3.9 6.5 10.7 13.4 6.5 3+ Veh 2.3 3.9 7.1 10.8 14.6 8.3 Total 1.7 3.9 6.7 10.4 13.1 5.4 In Other (Rural) Areas NHB HH Size Autos/HH 1 2 3 4 5+ Total 0 Veh 0.7 1.7 2.2 2.9 3.0* 1.2 1 Veh 1.3 2.2 3.4 3.8 3.9* 2.0 2 Veh 1.7 2.5 3.8 5.4 5.7 3.6 3+ Veh 1.2 2.6 4.0 5.7 7.0 4.5 Total 1.2 2.4 3.7 5.2 5.4 2.9 Source: 2009 NHTS. * Indicates where estimated trip rates were manually adjusted and smoothed. Table 4.17. Rural person trip production rates: HH size by auto availability, URBAN attribute identifies rural HHs.

66 Long-Distance and rural travel transferable parameters for Statewide travel Forecasting Models In Other (Rural) Areas, All Trip Purposes Trip Rates HH Size HH Size 1 2 3 4 5+ Total 0 worker 2.796 5.863 8.456 11.721 12.494 4.952 1 worker 4.249 7.436 11.247 15.733 18.245 8.750 2 worker 0.000 9.438 14.136 19.545 23.806 14.610 3+ worker 0.000 0.000 16.316 23.564 27.678 22.544 Total 3.605 7.607 12.452 17.868 20.962 9.783 In Other (Rural) Areas HBW Trip Rates HH Size HH Size 1 2 3 4 5+ Total 0 worker 0.003 0.011 0.009 0.049 0.074 0.010 1 worker 0.966 1.148 1.298 1.460 1.500* 1.166 2 worker 0.000 2.403 2.752 2.665 2.688 2.580 3+ worker 0.000 0.000 4.993 4.808 5.414 5.063 Total 0.539 1.211 2.026 2.226 2.342 1.378 In Other (Rural) Areas HBNW Trip Rates HH Size HH Size 1 2 3 4 5+ Total 0 worker 1.931 4.088 5.603 8.807 9.688 3.483 1 worker 1.691 3.914 6.650 9.646 11.975 4.878 2 worker 0.000 3.825 7.125 11.152 15.113 7.655 3+ worker 0.000 0.000 6.637 11.773 14.417 10.963 Total 1.797 3.937 6.700 10.434 13.139 5.453 In Other (Rural) Areas NHB HH Size HH Size 1 2 3 4 5+ Total 0 worker 0.862 1.764 2.844 2.865 2.900* 1.459 1 worker 1.593 2.374 3.299 4.628 4.832 2.706 2 worker 0.000 3.211 4.259 5.728 6.005 4.376 3+ worker 0.000 0.000 4.687 6.983 7.847 6.518 Total 1.269 2.459 3.726 5.209 5.482 2.951 Source: 2009 NHTS. * Indicates where estimated trip rates were manually adjusted and smoothed. Table 4.18. Rural person trip production rates: HH size by number of workers, URBAN attribute identifies rural HHs.

trip Generation parameters and Benchmark Statistics 67 The percentage of rural trips by purpose could be a useful statistic for use in model valida- tion and reasonableness checking. Assuming the weighted number of surveys for 2009 NHTS adequately reflects the share of rural versus urban trips, the percentage of rural trips by purpose has been summarized in Table 4.19. Since the definition of rural varies somewhat from one NHTS attribute/typology to another, the number and percentage of trips by purpose was esti- mated for each of these typologies and subsequently averaged. Regardless of the attribute used to identify rural households, the results show a considerably smaller percentage of home-based work trips than commonly found in urban areas. This is not entirely surprising in a population that generally consists of a higher-than-average share of farmers, retirees, and unemployed, as well as above average household sizes (http://205.254.135.7/emeu/recs/recs2005/hc2005_tables/ hc3demographics/pdf/tablehc8.3.pdf). Typology/NHTS 2009 Attribute URBANR â Other (Not Urbanized) HBHUR â Town and Country HBRESDEN â 0-999 Units/ Square Mile URBRUR â Rural Areas Average Trip Purpose No. of Trips % No. of Trips % No. of Trips % No. of Trips % % Rural Home- Based Work 63,057 11.82 12.03 23,194 12.60 29,983 12.26 12.06 Rural Home- Based Nonwork 308,005 57.74 218,398 54.41 96,301 52.31 129,875 53.09 55.19 Rural Nonhome- Based 162,405 30.44 134,711 33.56 64,619 35.10 84,761 34.65 32.74 Non Urban Totals â All Purposes 533,467 100.00 401,388 100.00 184,114 100.00 244,619 100.00 100.00 Source: 2009 NHTS. Table 4.19. Rural trips by purpose.

TRB’s National Cooperative Highway Research Program (NCHRP) Report 735: Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models explores transferable parameters for long-distance and rural trip-making for statewide models.

Appendixes G, H, and I are not contained in print or PDF versions of the report but are available online. Appendix G presents a series of rural typology variables considered in stratifying model parameters and benchmarks and identifies the statistical significance of each. Appendix H contains rural trip production rates for several different cross-classification schemes and the trip rates associated with each. Finally, Appendix I provides additional information on auto occupancy rates.

NCHRP Report 735 is a supplement to NCHRP Report 716 : Travel Demand Forecasting: Parameters and Techniques , which focused on urban travel.

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Trip Generation Appendices

Tgm appendices.

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Pass-By Data and Rate Tables

Time-of-Day Distribution - Truck

Time-of-Day Distribution - Vehicle

Trip Generation Data Plots - Modal

Click to download in PDF

000s - Port and Terminal - Modal Data Plots 1

200s - Residential - Modal Data Plots

300s - Lodging - Modal Data Plots

400s - Recreational - Modal Data Plots

500s - Institutional - Modal Data Plots

600s - Lodging - Modal Data Plots

700s - Office - Modal Data Plots

800s - Retail - Modal Data Plots

900s - Services - Modal Data Plots

Land Uses with Modal Data Plots

Trip Generation Data Plots - Truck

100s - Industrial - Truck Data Plots

200s - Residential - Truck Data Plots

300s - Lodging - Truck Data Plots

500s - Institutional - Truck Data Plots

600s - Medical - Truck Data Plots

700s - Office - Truck Data Plots

800s - Retail - Truck Data Plots

900s - Services - Truck Data Plots

Land Uses with Truck Data Plots

Forecasting

3 comments

What Are Pass-by Trips?

By Mike Spack

March 20, 2009

JARGON ALERT – I am trying to clarify a concept here that is wrapped in traffic engineering jargon.

I have had some difficulty explaining pass-by trips to a client. I previously discussed how much traffic is generated by proposed developments in my post on trip generation . Please review that if you need to get up to speed on the topic.

Pass-by trips are a subset of trip generation that only apply to commercial/retail developments. They are the folks already on the road who the business hopes to suck into their site as they are driving by. Think about a gas station.

A "new trip" for a gas station is me running out of gas for the snow blower, jumping into the truck, going to fill up my gas can, and then going back home to use the snow blower. A "pass-by" trip is when I am driving to a meeting, I realize I am out of gas, and I pull into the next gas station driveway I pass. I was already on that road and I am going to continue my trip to the meeting after I fill up. The third type of trip is the "diverted" trip. I am driving to a meeting, I realize I am out of gas, I have a coupon for a gas station that is three blocks out of the way, I get off of my main path to go to the gas station, and then I go back to my main path after I fill up.

New trips and pass-by trips are straightforward. You are either going to the business and returning the way you came OR you happened to by driving by, you stop in because it is convenient, and then continue on your merry way.

The trouble comes in with diverted trips because they depend on how far out your study goes. Think about stopping in at Home Depot on the way home from work to grab a box of nails. You have to get off of the freeway and drive half a mile on a county road to get there. Then you'll get back on the freeway to go home. If we are only analyzing the intersection on the corner of the Home Depot, this trip is going to look like a new trip because you are coming and going in the same manner through the study intersection. Now if you are also analyzing the two ramp intersections at the freeway interchange, you have to treat it as a diverted trip. You are coming and going from the same direction at the intersection on the corner of the Home Depot, BUT you are continuing onto the freeway in the same direction you started from. You are not returning to the place where you originally got into your car.

Thankfully, diverted trips only come into play in traffic studies for large, regional shopping destinations.

Where do you get a pass-by trip percentage by uses? ITE Trip Generation Handbook doesn’t give the average percentages by uses, it only shows some specific studies.

Mike, As you now, the 10th Edition of the ITE does not provide daily pass-by trip rates for the land uses for which AM and/or PM peak hour pass-by rates are provided. What are your recommendations on estimating daily pass-by trip rates for those land uses? Thanks in advance for any help you can provide.

We use our judgment to apply the a.m. and p.m. rates to the daily trip rates, taking the lower/higher/average in different situations. We don’t leave it at zero though.

My mission is to help traffic engineers, transportation planners, and other transportation professionals improve our world.

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Part III: Travel Demand Modeling

9 Introduction to Transportation Modeling: Travel Demand Modeling and Data Collection

Chapter overview.

Chapter 9 serves as an introduction to travel demand modeling, a crucial aspect of transportation planning and policy analysis. As explained in previous chapters, the spatial distribution of activities such as employment centers, residential areas, and transportation systems mutually influence each other. The utilization of travel demand forecasting techniques leads to dynamic processes in urban areas. A comprehensive grasp of travel demand modeling is imperative for individuals involved in transportation planning and implementation.

This chapter covers the fundamentals of the traditional four-step travel demand modeling approach. It delves into the necessary procedures for applying the model, including establishing goals and criteria, defining scenarios, developing alternatives, collecting data, and conducting forecasting and evaluation.

Following this chapter, each of the four steps will be discussed in detail in Chapters 10 through 13.

Learning Objectives

Describe the need for travel demand modeling in urban transportation and relate it to the structure of the four-step model (FSM).
Summarize each step of FSM and the prerequisites for each in terms of data requirement and model calibration.
Summarize the available methods for each of the first three steps of FSM and compare their reliability.
Identify assumptions and limitations of each of the four steps and ways to improve the model.

Introduction

Transportation planning and policy analysis heavily rely on travel demand modeling to assess different policy scenarios and inform decision-making processes. Throughout our discussion, we have primarily explored the connection between urban activities, represented as land uses, and travel demands, represented by improvements and interventions in transportation infrastructure. Figure 9.1 provides a humorous yet insightful depiction of the transportation modeling process. In preceding chapters, we have delved into the relationship between land use and transportation systems, with the houses and factories in the figure symbolizing two crucial inputs into the transportation model: households and jobs. The output of this model comprises transportation plans, encompassing infrastructure enhancements and programs. Chapter 9 delves into a specific model—travel demand modeling. For further insights into transportation planning and programming, readers are encouraged to consult the UTA OERtransport book, “Transportation Planning, Policies, and History.”

A graphical representation of FSM input and outputs data in the process.

Travel demand models forecast how people will travel by processing thousands of individual travel decisions. These decisions are influenced by various factors, including living arrangements, the characteristics of the individual making the trip, available destination options, and choices regarding route and mode of transportation. Mathematical relationships are used to represent human behavior in these decisions based on existing data.

Through a sequential process, transportation modeling provides forecasts to address questions such as:

What will the future of the area look like?
What is the estimated population for the forecasting year?
How are job opportunities distributed by type and category?
What are the anticipated travel patterns in the future?
How many trips will people make? ( Trip Generation )
Where will these trips end? ( Trip Distribution )
Which transportation mode will be utilized? ( Mode Split )
What will be the demand for different corridors, highways, and streets? ( Traffic Assignment )
Lastly, what impact will this modeled travel demand have on our area? (Rahman, 2008).

9.2 Four-step Model

According to the questions above, Transportation modeling consists of two main stages, regarding the questions outlined above. Firstly, addressing the initial four questions involves demographic and land use analysis, which incorporates the community vision collected through citizen engagement and input. Secondly, the process moves on to the four-step travel demand modeling (FSM), which addresses questions 5 through 8. While FSM is generally accurate for aggregate calculations, it may occasionally falter in providing a reliable test for policy scenarios. The limitations of this model will be explored further in this chapter.

In the first stage, we develop an understanding of the study area from demographic information and urban form (land-use distribution pattern). These are important for all the reasons we discussed in this book. For instance, we must obtain the current age structure of the study area, based on which we can forecast future birth rates, death, and migrations (Beimborn & Kennedy, 1996).

Regarding economic forecasts, we must identify existing and future employment centers since they are the basis of work travel, shopping travel, or other travel purposes. Empirically speaking, employment often grows as the population grows, and the migration rate also depends on a region’s economic growth. A region should be able to generate new employment while sustaining the existing ones based upon past trends and form the basis for judgment for future trends (Mladenovic & Trifunovic, 2014).

After forecasting future population and employment, we must predict where people go (work, shop, school, or other locations). Land-use maps and plans are used in this stage to identify the activity concentrations in the study area. Future urban growth and land use can follow the same trend or change due to several factors, such as the availability of open land for development and local plans and zoning ordinances (Beimborn & Kennedy, 1996). Figure 9.3 shows different possible land-use patterns frequently seen in American cities.

This pictures shows 6 different land use patterns that are: (a) traditional grid, (b) post-war suburb, (c) traditional neighborhood design, (d) fused grid, (e) post-war suburb II, and (f) tranditional neighborhood design II.

Land-use pattern can also be forecasted through the integration of land use and transportation as we explored in previous chapters.

Figure 9.3 above shows a simple structure of the second stage of FSM.

This picture shows the sequence of the fours steps of FSM.

Once the number and types of trips are predicted, they are assigned to various destinations and modes. In the final step, these trips are allocated to the transportation network to compute the total demand for each road segment. During this second stage, additional choices such as the time of travel and whether to travel at all can be modeled using choice models (McNally, 2007). Travel forecasting involves simulating human behavior through mathematical series and calculations, capturing the sequence of decisions individuals make within an urban environment.

The first attempt at this type of analysis in the U.S. occurred during the post-war development period, driven by rapid economic growth. The influential study by Mitchell and Rapkin (1954) emphasized the need to establish a connection between travel and activities, highlighting the necessity for a comprehensive framework. Initial development models for trip generation, distribution, and diversion emerged in the 1950s, leading to the application of the four-step travel demand modeling (FSM) approach in a transportation study in the Chicago area. This model was primarily highway-oriented, aiming to compare new facility development and improved traffic engineering. In the 1960s, federal legislation mandated comprehensive and continuous transportation planning, formalizing the use of FSM. During the 1970s, scholars recognized the need to revise the model to address emerging concerns such as environmental issues and the rise of multimodal transportation systems. Consequently, enhancements were made, leading to the development of disaggregate travel demand forecasting and equilibrium assignment methods that complemented FSM. Today, FSM has been instrumental in forecasting travel demand for over 50 years (McNally, 2007; Weiner, 1997).

Initially outlined by Mannheim (1979), the basic structure of FSM was later expanded by Florian, Gaudry, and Lardinois (1988). Figure 9.3 illustrates various influential components of travel demand modeling. In this representation, “T” represents transportation, encompassing all elements related to the transportation system and its services. “A” denotes the activity system, defined according to land-use patterns and socio-demographic conditions. “P” refers to transportation network performance. “D,” which stands for demand, is generated based on the land-use pattern. According to Florian, Gaudry, and Lardinois (1988), “L” and “S” (location and supply procedures) are optional parts of FSM and are rarely integrated into the model.

This flowchart shows the relationship between various components of transportation network and their joint impact on traffic volume (flow) on the network.

A crucial aspect of the process involves understanding the input units, which are defined both spatially and temporally. Demand generates person trips, which encompass both time and space (e.g., person trips per household or peak-hour person trips per zone). Performance typically yields a level of service, defined as a link volume capacity ratio (e.g., freeway vehicle trips per hour or boardings per hour for a specific transit route segment). Demand is primarily defined at the zonal level, whereas performance is evaluated at the link level.

It is essential to recognize that travel forecasting models like FSM are continuous processes. Model generation takes time, and changes may occur in the study area during the analysis period.

Before proceeding with the four steps of FSM, defining the study area is crucial. Like most models discussed, FSM uses traffic analysis zones (TAZs) as the geographic unit of analysis. However, a higher number of TAZs generally yield more accurate results. The number of TAZs in the model can vary based on its purpose, data availability, and vintage. These zones are characterized or categorized by factors such as population and employment. For modeling simplicity, FSM assumes that trip-making begins at the center of a zone (zone centroid) and excludes very short trips that start and end within a TAZ, such as those made by bike or on foot.

Furthermore, highway systems and transit systems are considered as networks in the model. Highway or transit line segments are coded as links, while intersections are represented as nodes. Data regarding network conditions, including travel times, speeds, capacity, and directions, are utilized in the travel simulation process. Trips originate from trip generation zones, traverse a network of links and nodes, and conclude at trip attraction zones.

Trip Generation

Trip generation is the first step in the FSM model. This step defines the magnitude of daily travel in the study area for different trip purposes. It will also provide us with an estimate of the total trips to and from each zone, creating a trip production and attraction matrix for each trip’s purpose. Trip purposes are typically categorized as follows:

Home-based work trips (work trips that begin or end at home),
Home-based shopping trips,
Home-based other trips,
School trips,
Non-home-based trips (trips that neitherbeginnorendathome),
Trucktrips,and
Taxitrips(Ahmed,2012).

Trip attractions are based on the level of employment in a zone. In the trip generation step, the assumptions and limitations are listed below:

Independent decisions: Travel behavior is affected by many factors generated within a household; the model ignores most of these factors. For example, childcare may force people to change their travel plans.
Limited trip purposes: This model consists of a limited number of trip purposes for simplicity, giving rise to some model limitations. Take shopping trips, for example; they are all considered in the same weather conditions. Similarly, we generate home-based trips for various purposes (banking, visiting friends, medical reasons, or other purposes), all of which are affected by factors ignored by the model.
Trip combinations: Travelers are often willing to combine various trips into a chain of short trips. While this behavior creates a complex process, the FSM model treats this complexity in a limited way.
Feedback, cause, and effect problems: Trip generation often uses factors that are a function of the number of trips. For instance, for shopping trip attractions in the FSM model, we assume they are a retail employment function. However, it is logical to assume how many customers these retail centers attract. Alternatively, we can assume that the number of trips a household makes is affected by the number of private cars they own. Nevertheless, the activity levels of families determine the total number of cars.

As mentioned, trip generation process estimations are done separately for each trip purpose. Equations 1 and 2 show the function of trip generation and attraction:

$O_i = f(x_{i1}, x_{i2}, x_{i3}, \ldots)$

where Oi and Dj trip are generated and attracted respectively, x refers to socio-economic characteristics, and y refers to land-use properties.

Generally, FSM aggregates different trip purposes previously listed into three categories: home-based work trips (HBW) , home-based other (or non-work) trips (HBO) , and non-home-based trips (NHB) . Trip ends are either the origin (generation) or destination (attraction), and home-end trips comprise most trips in a study area. We can also model trips at different levels, such as zones, households, or person levels (activity-based models). Household-level models are the most common scale for trip productions, and zonal-level models are appropriate for trip attractions (McNally, 2007).

There are three main methods for a trip generation or attraction.

The first method is multiple regression based on population, jobs, and income variables.
The second method in this step is experience-based analysis, which can show us the ratio of trips generated frequently.
The third method is cross-classification . Cross-classification is like the experience-based analysis in that it uses trip rates but in an extended format for different categories of trips (home-based trips or non-home-based trips) and different attributes of households, such as car ownership or income.

Elaborating on the differences between these methods, category analysis models are more common for the trip generation model, while regression models demonstrate better performance for trip attractions (Meyer, 2016). Production models are recognized to be influenced by a range of explanatory and policy-sensitive variables (e.g., car ownership, household income, household size, and the number of workers). However, estimation is more problematic for attraction models because regional travel surveys are at the household level (thus providing more accurate data for production models) and not for nonresidential land uses (which is important for trip attraction). Additionally, estimation can be problematic because explanatory trip attraction variables may usually underperform (McNally, 2007). For these reasons, survey data factoring is required prior to relating sample trips to population-level attraction variables, typically achieved via regression analysis. Table 9.1 shows the advantages and disadvantages of each of these two models.

Trip Distribution

Thus far, the number of trips beginning or ending in a particular zone have been calculated. The second step explores how trips are distributed between zones and how many trips are exchanged between two zones. Imagine a shopping trip. There are multiple options for accessible shopping malls accessible. However, in the end, only one will be selected for the destination. This information is modeled in the second step as a distribution of trips. The second step results are usually a very large Origin-Destination (O-D) matrix for each trip purpose. The O-D matrix can look like the table below (9.2), in which sum of Tij by j shows us the total number of trips attracted in zone J and the sum of Tij by I yield the total number of trips produced in zone I.

Up to this point, we have calculated the number of trips originating from or terminating in a specific zone. The next step involves examining how these trips are distributed across different zones and how many trips are exchanged between pairs of zones. To illustrate, consider a shopping trip: there are various options for reaching shopping malls, but ultimately, only one option is chosen as the destination. This process is modeled in the second step as the distribution of trips. The outcome of this step typically yields a large Origin-Destination (O-D) matrix for each trip purpose. An O-D matrix might resemble the table below (9.2), where the sum of Tij by j indicates the total number of trips attracted to zone J, and the sum of Tij by I represents the total number of trips originating from zone I.

$T_{ij} = \frac{P(A_i F_{ij}(K_{ij}))}{\sum(A_x F_{ij}(k_{ix}))}$

T ij = trips produced at I and attracted at j

P i = total trip production at I

A j = total trip attraction at j

F ij = a calibration term for interchange ij , (friction factor) or travel

time factor ( F ij =C/t ij n )

C= calibration factor for the friction factor

K ij = a socioeconomic adjustment factor for interchange ij

i = origin zone

n = number of zones

Different methods (units) in the gravity model can be used to perform distance measurements. For instance, distance can be represented by time, network distance, or travel costs. For travel costs, auto travel cost is the most common and straightforward way of monetizing distance. A combination of different costs, such as travel time, toll payments, parking payments, etc., can also be used. Alternatively, a composite cost of both car and transit costs can be used (McNally, 2007).

Generalized travel costs can be a function of time divided into different segments. For instance, public transit time can be divided into the following segments: in-vehicle time, walking time, waiting time, interchange time, fare, etc. Since travelers perceive time value differently for each segment (like in-vehicle time vs. waiting time), weights are assigned based on the perceived value of time (VOT). Similarly, car travel costs can be categorized into in-vehicle travel time or distance, parking charge, tolls, etc.

As with the first step in the FSM model, the second step has assumptions and limitations that are briefly explained below.

Constant trip times: In order to utilize the model for prediction, it assumes that the duration of trips remains constant. This means that travel distances are measured by travel time, and the assumption is that enhancements in the transportation system, which reduce travel times, are counterbalanced by the separation of origins and destinations.
Automobile travel times to represent distance: We utilize travel time as a proxy for travel distance. In the gravity model, this primarily relies on private car travel time and excludes travel times via other modes like public transit. This leads to a broader distribution of trips.
Limited consideration of socio-economic and cultural factors: Another drawback of the gravity model is its neglect of certain socio-economic or cultural factors. Essentially, this model relies on trip production and attraction rates along with travel times between them for predictions. Consequently, it may overestimate trip rates between high-income groups and nearby low-income Traffic Analysis Zones (TAZs). Therefore, incorporating more socio-economic factors into the model would enhance accuracy.
Feedback issues: The gravity model’s reliance on travel times is heavily influenced by congestion levels on roads. However, measuring congestion proves challenging, as discussed in subsequent sections. Typically, travel times are initially assumed and later verified. If the assumed values deviate from actual values, they require adjustment, and the calculations need to be rerun.

Mode choice

FSM model’s third step is a mode-choice estimation that helps identify what types of transportation travelers use for different trip purposes to offer information about users’ travel behavior. This usually results in generating the share of each transportation mode (in percentages) from the total number of trips in a study area using the utility function (Ahmed, 2012). Performing mode-choice estimations is crucial as it determines the relative attractiveness and usage of various transportation modes, such as public transit, carpooling, or private cars. Modal split analysis helps evaluate improvement programs or proposals (e.g., congestion pricing or parking charges) aimed at enhancing accessibility or service levels. It is essential to identify the factors contributing to the utility and disutility of different modes for different travel demands (Beimborn & Kennedy, 1996). Comparing the disutility of different modes between two points aids in determining mode share. Disutility typically refers to the burdens of making a trip, such as time, costs (fuel, parking, tolls, etc.). Once disutility is modeled for different trip purposes between two points, trips can be assigned to various modes based on their utility. As discussed in Chapter 12, a mode’s advantage in terms of utility over another can result in a higher share of trips using that mode.

The assumptions and limitations for this step are outlined as follows:

Choices are only affected by travel time and cost: This model assumes that changes in mode choices occur solely if transportation cost or travel time in the transportation network or transit system is altered. For instance, a more convenient transit mode with the same travel time and cost does not affect the model’s results.
Omitted factors: Certain factors like crime, safety, and security, which are not included in the model, are assumed to have no effect, despite being considered in the calibration process. However, modes with different attributes regarding these omitted factors yield no difference in the results.
Simplified access times: The model typically overlooks factors related to the quality of access, such as neighborhood safety, walkability, and weather conditions. Consequently, considerations like walkability and the impact of a bike-sharing program on the attractiveness of different modes are not factored into the model.
Constant weights: The model assumes that the significance of travel time and cost remains constant for all trip purposes. However, given the diverse nature of trip purposes, travelers may prioritize travel time and cost differently depending on the purpose of their trip.

The most common framework for mode choice models is the nested logit model, which can accommodate various explanatory variables. However, before the final step, results need to be aggregated for each zone (Koppelman & Bhat, 2006).

A generalized modal split chart is depicted in Figure 9.5.

a simple decision tree for transportation mode choice between car, train, and walking.

In our analysis, we can use binary logit models (dummy variable for dependent variable) if we have two modes of transportation (like private cars and public transit only). A binary logit model in the FSM model shows us if changes in travel costs would occur, such as what portion of trips changes by a specific mode of transport. The mathematical form of this model is:

$P_ij^1=\frac{T_ij^1}{T_{ij}}\ =\frac{e^-bcij^1 }{e^(-bc_ij^1 )+e^(-bc_ij^2 )}$

where: P_ij 1= The proportion of trips between i and j by mode 1 . Tij 1= Trips between i and j by mode 1.

Cij 1= Generalized cost of travel between i and j by mode 1 .

Cij^2= Generalized cost of travel between i and j by mode 2 .

b= Dispersion Parameter measuring sensitivity to cost.

It is also possible to have a hierarchy of transportation modes for using a binary logit model. For instance, we can first conduct the analysis for the private car and public transit and then use the result of public transit to conduct a binary analysis between rail and bus.

Trip assignment

After breaking down trip counts by mode of transportation, we analyze the routes commuters take from their starting point to their destination, especially for private car trips. This process is known as trip assignment and is the most intricate stage within the FSM model. Initially, the minimum path assigns trips for each origin-destination pair based on either travel costs or time. Subsequently, the assigned volume of trips is compared to the capacity of the route to determine if congestion would occur. If congestion does happen (meaning that traffic volume exceeds capacity), the speed of the route needs to be decreased, resulting in increased travel costs or time. When the Volume/Capacity ratio (v/c ratio) changes due to congestion, it can lead to alterations in both speed and the shortest path. This characteristic of the model necessitates an iterative process until equilibrium is achieved.

The process for public transit is similar, but with one distinction: instead of adjusting travel times, headways are adjusted. Headway refers to the time between successive arrivals of a vehicle at a stop. The duration of headways directly impacts the capacity and volume for each transit vehicle. Understanding the concept of equilibrium in the trip assignment step is crucial because it guides the iterative process of the model. The conclusion of this process is marked by equilibrium, a concept known as Wardrop equilibrium. In Wardrop equilibrium, traffic naturally organizes itself in congested networks so that individual commuters do not switch routes to reduce travel time or costs. Additionally, another crucial factor in this step is the time of day.

Like previous steps, the following assumptions and limitations are pertinent to the trip assignment step:

1. Delays on links: Most traffic assignment models assume that delays occur on the links, not the intersections. For highways with extensive intersections, this can be problematic because intersections involve highly complex movements. Intersections are excessively simplified if the assignment process does not modify control systems to reach an equilibrium.

2. Points and links are only for trips: This model assumes that all trips begin and finish at a single point in a zone (centroids), and commuters only use the links considered in the model network. However, these points and links can vary in the real world, and other arterials or streets might be used for commutes.

3. Roadway capacities: In this model, a simple assumption helps determine roadways’ capacity. Capacity is found based on the number of lanes a roadway provides and the type of road (highway or arterial).

4. Time of the day variations: Traffic volume varies greatly throughout the day and week. In this model, a typical workday of the week is considered and converted to peak hour conditions. A factor used for this step is called the hour adjustment factor. This value is critical because a small number can result in a massive difference in the congestion level forecasted on the model.

5. Emphasis on peak hour travel: The model forecasts for the peak hour but does not forecast for the rest of the day. The models make forecasts for a typical weekday but neglect specific conditions of that time of the year. After completing the fourth step, precise approximations of travel demand or traffic count on each road are achieved. Further models can be used to simulate transportation’s negative or positive externalities. These externalities include air pollution, updated travel times, delays, congestion, car accidents, toll revenues, etc. These need independent models such as emission rate models (Beimborn & Kennedy, 1996).

The basic equilibrium condition point calculation is an algorithm that involves the computation of minimum paths using an all-or-nothing (AON) assignment model to these paths. However, to reach equilibrium, multiple iterations are needed. In AON, it is assumed that the network is empty, and a free flow is possible. The first iteration of the AON assignment requires loading the traffic by finding the shortest path. Due to congestion and delayed travel times, the

previous shortest paths may no longer be the best minimum path for a pair of O-D. If we observe a notable decrease in travel time or cost in subsequent iterations, then it means the equilibrium point has not been reached, and we must continue the estimation. Typically, the following factors affect private car travel times: distance, free flow speed on links, link capacity, link speed capacity, and speed flow relationship .

The relationship between the traffic flow and travel time equation used in the fourth step is:

$t = t_0 + a v^n, \quad v < c$

t= link travel time per length unit

t 0 =free-flow travel time

v=link flow

c=link capacity

a, b, and n are model (calibrated) parameters

Model improvement

Improvements to FSM continue to generate more accurate results. Since transportation dynamics in urban and regional areas are under the complex influence of various factors, the existing models may not be able to incorporate all of them. These can be employer-based trip reduction programs, walking and biking improvement schemes, a shift in departure (time of the day), or more detailed information on socio-demographic and land-use-related factors. However, incorporating some of these variables is difficult and can require minor or even significant modifications to the model and/or computational capacities or software improvements. The following section identifies some areas believed to improve the FSM model performance and accuracy.

• Better data: An effective way of improving the model accuracy is to gather a complete dataset that represents the general characteristics of the population and travel pattern. If the data is out- of-date or incomplete, we will get poor results.

• Better modal split: As you saw in previous sections, the only modes incorporated into the model are private car and public transit trips, while in some cities, a considerable fraction of trips are made by bicycle or by walking. We can improve our models by producing methods to consider these trips in the first and third steps.

• Auto occupancy: In contemporary transportation planning practices, especially in the US, some new policies are emerging for carpooling. We can calculate auto occupancy rates using different mode types, such as carpooling, sensitive to private car trips’ disutility, parking costs, or introducing a new HOV lane.

• Time of the day: In this chapter, the FSM framework discussed is oriented toward peak hour (single time of the day) travel patterns. Nonetheless, understanding the nature of congestion in other hours of the day is also helpful for understanding how travelers choose their travel time.

• A broader trip purpose: Additional trip purposes may provide a better understanding of the

factors affecting different trip purposes and trip-chaining behaviors. We can improve accuracy by having more trip purposes (more disaggregate input and output for the model).

The concept of access: As discussed, land-use policies that encourage public transit use or create amenities for more convenient walking are not present in the model. Developing factors or indices that reflect such improvements in areas with high demand for non-private vehicles and incorporating them in choice models can be a good improvement.
Land use feedback: To better understand interactions between land use and travel demand, a land-use simulation model can be added to these steps to determine how a proposed transportation change will lead to a change in land use.
Intersection delays: As mentioned in the fourth step, intersections in major highways create significant delays. Incorporating models that calculate delays at these intersections, such as stop signs, could be another improvement to the model.

A Simple Example of the FSM model

An example of FSM is provided in this section to illustrate a typical application of this model in the U.S. In the first phase, the specifications about the transportation network and household data are needed. In this hypothetical example, 5 percent of households in each TAZ were sampled and surveyed, which generated 1,955 trips in 200 households. As a hypothetical case study, this sample falls below the standard required for statistical significance but is relevant to demonstrate FSM.

A home interview survey was carried out to gather data from a five percent sample of households in each TAZ. This survey resulted in 1,852 trips from 200 households. It is important to note that the sample size in this example falls below the minimum required for statistical significance, as it is intended for learning purposes only.

Table 9.3 provides network information such as speed limits, number of lanes, and capacity. Table 9.4 displays the total number of households and jobs in three industry sectors for each zone. Additionally, Table 9.5 breaks down the household data into three car ownership groups, which is one of the most significant factors influencing trip making.

In the first step (trip generation), a category model (i.e., cross-classification) helped estimate trips. The sampled population’s sociodemographic and trip data for different purposes helped calculate this estimate. Since research has shown the significant effect of auto ownership on private car trip- making (Ben-Akiva & Lerman, 1974), disaggregating the population based on the number of private cars generates accurate results. Table 9.7 shows the trip-making rate for different income and auto ownership groups.

Also, as mentioned in previous sections, multiple regression estimation analysis can be used to generate the results for the attraction model. Table 9.7 shows the equations for each of the trip purposes.

After estimating production and attraction, the models are used for population data to generate results for the first step. Next, comparing the results of trip production and attraction, we can observe that the total number of trips for each purpose is different. This can be due to using different methods for production and attraction. Since the production method is more reliable, attraction is typically normalized by production. Also, some external zones in our study area are either attracting trips from our zones or generating them. In this case, another alternative is to extend the boundary of the study area and include more zones.

As mentioned, the total number of trips produced and attracted are different in these results. To address this mismatch, we can use a balance factor to come up with the same trip generation and attraction numbers if we want to keep the number of zones within our study area. Alternatively, we can consider some external stations in addition to designated zones. In this example, using the latter seems more rational because, as we saw in Table 9.4, there are more jobs than the number of households aggregately, and our zone may attract trips from external locations.

For the trip distribution step, we use the gravity model. For internal trips, the gravity model is:

$T_{ij} = a_i b_j P_i A_j f(t_{ij})$

and f(tij) is some function of network level of service (LOS)

To apply the gravity model, we need to calculate the impedance function first, which is represented here by travel cost. Table 9.9 shows the minimum travel path between each pair of zones ( skim tree ) in a matrix format in which each cell represents travel time required to travel between the corresponding row and column of that cell.

Table 9.9-Travel cost table (skim tree)

Note. Table adapted from “The Four-Step Model” by M. McNally, In D. A. Hensher, & K. J. Button (Eds.), Handbook of transport modelling , Volume1, p. 5, Bingley, UK: Emerald Publishing. Copyright 2007 by Emerald Publishing.

With having minimum travel costs between each pair of zones, we can calculate the impedance function for each trip purpose using the formula

$f(t_{ij}) = a \cdot t_{ij} \cdot b \cdot e^{ct_{ij}}$

Table 9.10 shows the model parameters for calculating the impedance function for different trip purposes:

After calculating the impedance function , we can calculate the result of the trip distribution. This stage generates trip matrices since we calculate trips between each zone pair. These matrices are usually in “Origin-Destination” (OD) format and can be disaggregated by the time of day. Field surveys help us develop a base-year trip distribution for different periods and trip purposes. Later, these empirical results will help forecast trip distribution. When processing the surveys, the proportion of trips from the production zone to the attraction zone (P-A) is also generated. This example can be seen in Table 9.11. Looking at a specific example, the first row in table is for the 2-hour morning peak commute time period. The table documents that the production to attraction factor for the home-based work trip is 0.3. Unsurprisingly, the opposite direction, attraction to production zone is 0.0 for this time of day. Additionally, the table shows that the factor for HBO and NHB trips are low but do occur during this time period. This could represent shopping trips or trips to school. Table 9.11 table also contains the information for average occupancy levels of vehicles from surveys. This information can be used to convert person trips to vehicle trips or vice versa.

Table 9.11 Trip distribution rates for different time of the day and trip purposes

The O-D trip table is calculated by adding the multiplication of the P-to-A factor by corresponding cell of the P-A trip table and adding the corresponding cell of the transposed P-A trip table multiplied by the A-to-P factor. These results, which are the final output of second step, are shown in Table 9.12.

Once the Production-Attraction (P-A) table is transformed into Origin-Destination (O-D) format and the complete O-D matrix is computed, the outcomes will be aggregated for mode choice and traffic assignment modeling. Further elaboration on these two steps will be provided in Chapters 11 and 12.

In this chapter, we provided a comprehensive yet concise overview of four-step travel demand modeling including the process, the interrelationships and input data, modeling part and extraction of outputs. The complex nature of cities and regions in terms of travel behavior, the connection to the built environment and constantly growing nature of urban landscape, necessitate building models that are able to forecast travel patterns for better anticipate and prepare for future conditions from multiple perspectives such as environmental preservation, equitable distribution of benefits, safety, or efficiency planning. As we explored in this book, nearly all the land-use/transportation models embed a transportation demand module or sub model for translating magnitude of activities and interconnections into travel demand such as VMT, ridership, congestion, toll usage, etc. Four-step models can be categorized as gravity-based, equilibrium-based models from the traditional approaches. To improve these models, several new extensions has been developed such as simultaneous mode and destination choice, multimodality (more options for mode choice with utility), or microsimulation models that improve granularity of models by representing individuals or agents rather than zones or neighborhoods.

Travel demand modeling are models that predicts the flow of traffic or travel demand between zones in a city using a sequence of steps.

Intermodality refers to the concept of utilizing two or more travel modes for a trip such as biking to a transit station and riding the light rail.
Multimodality is a type of transportation network in which a variety of modes such as public transit, rail, biking networks, etc. are offered.

Zoning ordinances is legal categorization of land use policies that permits or prohibits certain built environment factors such as density.

Volume capacity ratio is ratio that divides the demand on a link by the capacity to determine the level of service.

Zone centroid is usually the geometric center of a zone in modeling process where all trips originate and end.

Home-based work trips (HBW) are the trips that originates from home location to work location usually in the AM peak.

Home-based other (or non-work) trips (HBO) are the trips that originates from home to destinations other than work like shopping or leisure.

Non-home-based trips (NHB) are the trips that neither origin nor the destination are home or they are part of a linked trip.

Cross-classification is a method for trip production estimation that disaggregates trip rates in an extended format for different categories of trips like home-based trips or non-home-based trips and different attributes of households such as car ownership or income.

Generalized travel costs is a function of time divided into sections such as in vehicle time vs. waiting time or transfer time in a transit trip.

Binary logit models is a type of logit model where the dependent variable can take only a value of 0 or 1.

Wardrop equilibrium is a state in traffic assignment model where are drivers are reluctant to change their path because the average travel time is at a minimum.

All-or-nothing (AON) assignment model is a model that assumes all trips between two zones uses the shortest path regardless of volume.

Speed flow relationship is a function that determines the speed based on the volume (flow)

skim tree is structure of travel time by defining minimum cost path for each section of a trip.

Key Takeaways

In this chapter, we covered:

What travel demand modeling is for and what the common methods are to do that.
How FSM is structured sequentially, what the relationships between different steps are, and what the outputs are.
What the advantages and disadvantages of different methods and assumptions in each step are.
What certain data collection and preparation for trip generation and distribution are needed through a hypothetical example.

Prep/quiz/assessments

What is the need for regular travel demand forecasting, and what are its two major components?
Describe what data we require for each of the four steps.
What are the advantages and disadvantages of regression and cross-classification methods for a trip generation?
What is the most common modeling framework for mode choice, and what result will it provide us?
What are the main limitations of FSM, and how can they be addressed? Describe the need for travel demand modeling in urban transportation and relate it to the structure of the four-step model (FSM).

Ahmed, B. (2012). The traditional four steps transportation modeling using a simplified transport network: A case study of Dhaka City, Bangladesh. International Journal of Advanced Scientific Engineering and Technological Research , 1 (1), 19–40. https://discovery.ucl.ac.uk/id/eprint/1418961/

ALMEC, C . (2015). The Project for capacity development on transportation planning and database management in the republic of the Philippines: MMUTIS update and enhancement project (MUCEP) : Project Completion Report . Japan International Cooperation Agency. (JICA) Department of Transportation and Communications (DOTC) . https://books.google.com/books?id=VajqswEACAAJ .

Beimborn, E., and Kennedy, R. (1996). Inside the black box: Making transportation models work for livable communities . Washington, DC: Citizens for a Better Environment and the Environmental Defense Fund. https://www.piercecountywa.gov/DocumentCenter/View/755/A-GuideToModeling?bidId

Ben-Akiva, M., & Lerman, S. R. (1974). Some estimation results of a simultaneous model of auto ownership and mode choice to work. Transportation , 3 (4), 357–376. https://doi.org/10.1007/bf00167966

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association , 76 (3), 265–294. https://doi.org/10.1080/01944361003766766

Florian, M., Gaudry, M., & Lardinois, C. (1988). A two-dimensional framework for the understanding of transportation planning models. Transportation Research Part B: Methodological , 22 (6), 411–419. https://doi.org/10.1016/0191-2615(88)90022-7

Hadi, M., Ozen, H., & Shabanian, S. (2012). Use of dynamic traffic assignment in FSUTMS in support of transportation planning in Florida. Florida International University Lehman Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/24925

Hansen, W. (1959). How accessibility shapes land use.” Journal of the American Institute of Planners 25 (2): 73–76. https://doi.org/10.1080/01944365908978307

Gavu, E. K. (2010). Network based indicators for prioritising the location of a new urban transport connection: Case study Istanbul, Turkey (Master’s thesis, University of Twente). International Institute for Geo-Information Science and Earth Observation Enschede. http://essay.utwente.nl/90752/1/Emmanuel%20Kofi%20Gavu-22239.pdf

Karner, A., London, J., Rowangould, D., & Manaugh, K. (2020). From transportation equity to transportation justice: Within, through, and beyond the state. Journal of Planning Literature , 35 (4), 440–459. https://doi.org/10.1177/0885412220927691

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Koppelman, Frank S, and Chandra Bhat. (2006). A self instructing course in mode choice modeling: multinomial and nested logit models. U.S. Department of Transportation Federal Transit Administration https://www.caee.utexas.edu/prof/bhat/COURSES/LM_Draft_060131Final-060630.pdf

‌Manheim, M. L. (1979). Fundamentals of transportation systems analysis. Volume 1: Basic Concepts . The MIT Press https://mitpress.mit.edu/9780262632898/fundamentals-of-transportation-systems-analysis/

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Home-based other (or non-work) trips (HBO) are the trips that originates from home to destinations other than work like shopping or leisure.

gravity model is a type of accessibility measurement in which the employment in destination and population in the origin defines thee degree of accessibility between the two zones.

Impedance function is a function that convert travel costs (usually time or distance) to the level of difficulty of getting from one location to the other.

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TrafficInfraTech Magazine Linking People Places & Progress

Trip generation manual for indian cities.

June 23, 2023 in Urban transport

The Government of India has implemented several initiatives to enhance core infrastructure and improve the quality of mobility, accurate estimation of trip rates is crucial for effectively predicting motorized and non-motorized vehicular traffic in India and proposing appropriate policy and infrastructure guidelines, write Prof. Manoranjan Parida, Director, CSIR-Central Road Research Institute and Dr. Ch. Ravi Sekhar, Chief Scientist & Head TPE, CRRI

Household passenger trip rates refer to the number of trips taken by a household in certain period (day/week). These trip rates can vary depending on various factors such as number of household members, their age, gender, land use, purpose of trip etc.

Trip Generation is the initial stage of the four-stage transportation planning process. These four stages include trip generation, trip distribution, mode choice and trip assignment. Trip assignment plays a vital role in determining the impact of land use developments. However, existing studies in India primarily focus on purpose-driven trips and incorporate limited explanatory variables in model development. As a result, these models exhibit poor prediction accuracy, leading to underestimation or overestimation of traffic.

It is important to note that, to date, no manual or guideline exists in the country for the ready-made estimation of trips based on evolved per capita trip rates across various modes. Recognizing this research gap, the development of a systematic approach based on Per Capita Trip Rates (PCTR) specific to Indian cities becomes crucial. Such an approach would ultimately facilitate the evolution of suitable Transport Demand Management (TDM) policies.

Overview on Existing Manuals

The ITE Trip Generation Manual, 10th edition (2017), provides nationally collected trip generation data, but its usefulness is limited as it may not accurately represent all geographic areas and land use variations. To address this issue, some states and municipalities in the USA have conducted their own trip generation studies to obtain local and regional data, which offer more accurate estimates for trip generation within their respective areas.

Department of Transportation (2012), presents an analysis of trip generation in an emirate. The manual categorizes the emirate into different types of areas such as CBD, Non-CBD, and others. It further classifies the land use into nine major types with sub-classifications using a three-digit code. The manual utilizes an arithmetic formula (not specified in the manual) to calculate trip and parking rates. The Vermont Agency of Transportation (VTrans) conducted a comprehensive study from 2008-2010 to measure trip generation for various types of development commonly proposed in Vermont. The outcome of the research was the Vermont Trip Generation Manual, which offers more accurate estimates for traffic impact studies within the state.

The Southern New Hampshire Planning Commission (SNHPC) conducted a regional trip generation study to develop local trip generation rates for land use types where data was lacking in the ITE Trip Generation, 8th Edition. The study also aimed to compare locally gathered data with the ITE national average data.

The existing literature demonstrates the importance of conducting localized trip generation studies to account for specific regional characteristics and land use variations. Such studies provide more accurate data for estimating trip generation and are essential for effective transportation planning and impact assessments.

Indian Trip Generation Manual by CSIR-CRRI

The Indian Trip Generation Manual is currently being developed by CSIR-CRRI under the sponsorship of the CSIR-FBR Project. This comprehensive manual aims to provide guidelines for estimating trip generation rates for various land uses in urban areas of India. Its development involved extensive data collection and analysis from 32 cities across the country, with the support of eight academic institutes including IIT Jammu, SPA Delhi, NIT Nagpur, SVNIT Surat, NIT Surathkal, NIT Tiruchirapalli, MNIT Bhopal, and NIT Warangal.

The manual serves as an invaluable resource for urban planners, transportation engineers, and professionals involved in the planning and design of urban transportation systems in India.

It offers detailed information on the methodology employed for trip generation, encompassing the definition of different land uses, trip generation rates, and factors that influence trip generation. The covered land uses include residential, commercial, office, educational, and recreational facilities, among others. The household passenger trip rates were estimated independent variable wise (floor area, household size and type of house), purpose wise per capita trip rates, age wise per capita trip rates and gender wise per capita trip rates.

The graph below represents the trip rates for private vehicles based on the type of house (BHK) and population size categories. It includes the trip rates for both 2-wheeler and car usage. In the table, the “Per Household Trip Rate” indicates the average number of trips made by each household. The trip rates for 2-wheeler and car usage are specified for each type of house (BHK) and population size categories.

Table 1 displays the trip rates based on the type of house. For the population categories of less than 2 million and 2-4 million, the car trip rates show an upward trend as the house size increases from 1 BHK to 3 BHK, with rates of 0.27 trips per household and 0.44 trips per household respectively. However, in the case of 3 BHK households, the 2W (two-wheeler) trip rate experiences a decline. In the population category of 4-8 million, the car trip rate increases to 0.28 trips per household for 3 BHK households.

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Please visit www.gsa.gov/perdiem to find the rates. Click on a state on the map to view that state's rates or enter the location in the search box. Even though some cities are listed for your lookup convenience, not all cities can or will be listed. To look up the county a destination is located in, visit the Census Geocoder . If neither the city nor county you are looking for is listed on the GSA per diem rate page, then the standard CONUS rate applies.

Non-standard areas (NSAs) are frequently traveled by the federal community and are reviewed on an annual basis. Standard CONUS locations are less frequently traveled by the federal community and are not specifically listed on our website.

Per diem rates are set based upon contractor-provided average daily rate (ADR) data of local lodging properties. The properties must be fire-safe and have a FEMA ID number. The ADR is a travel industry metric that divides room sales rental revenue by the number of rooms sold. All rates are evaluated to ensure that they are fair and equitable in the GSA and Office of Management and Budget approval process. For more detailed information, visit the Factors Influencing Lodging Rates page.

5 U.S.C § 5702 gives the Administrator of the U.S. General Services Administration (GSA) the authority to establish the system of reimbursing Federal employees for the subsistence expenses (lodging, meals, and incidentals) of official travel. The law governs how GSA sets rates today, and allows the GSA Administrator to establish locality-based allowances for these expenses with a reporting requirement back to Congress. The law was established to protect Federal employees by fairly reimbursing them for travel expenses. In addition, if a Federal employee cannot find a room within the established per diem rates, the travel policy allows the agency to reimburse the actual hotel charges up to 300 percent of the established per diem rates.

The per diem program has several standards that it follows in its systematic structured per diem methodology. The first level is having a "standard rate" that applies to approximately 85 percent of counties in the continental United States.

It is GSA's policy that, if and when a Federal agency, on behalf of its employees, requests that the standard rate is not adequate in a specific area to cover costs of travel as intended by the law, GSA will study the locality to determine whether the locality under study should become a "non-standard area." If the study recommends a change, a change will be implemented as deemed appropriate. GSA has implemented a process to review and update both the standard and non-standard areas annually.

The standard "boundary line" for where non-standard areas apply is generally one county. This is the case for approximately 85 percent of the non-standard rates that GSA sets. However, in some cases, agencies have requested that the rate apply to an area larger than one county, such as a metropolitan area. In a very small number of cases, an agency can and has requested that a rate apply to just a city and not the entire county. In some rural areas, a rate sometimes applies to more than one county due to lack of an adequate data sample to set a rate otherwise.

GSA uses the Federal Information Processing Series (FIPS) code standard for its apply areas. While GSA often uses ZIP codes to select hotel data samples, the apply area is coded by a FIPS code, unless a Federal agency only wants the rate to apply to certain ZIP codes. These codes are managed by the American National Standards Institute (ANSI) to ensure uniform identification of geographic entities through all federal government agencies.

In order for GSA to conduct a "special" review of a non-standard area (NSA) during the current fiscal year, a Federal Agency Travel Manager or an equivalent individual in grade or title must submit a signed letter on agency letterhead or stationery stating that the present per diem rate is inadequate. The request should contain the following information:

The geographical areas you want us to study, especially ZIP codes.
The property names (including addresses, ZIP codes, and rates) where your federal travelers stay while on temporary duty travel and those properties (including addresses, ZIP codes, and rates) that will not honor the federal lodging per diem rate.
The number of times actual expenses were used and/or federal travelers had to use another lodging facility to stay within the maximum allowable lodging per diem rate, which resulted in additional transportation expenses (rental car, taxi) being incurred.

All valid requests postmarked no later than 12/31 will be eligible for this review. All valid requests received after 12/31, but before 4/1 will be evaluated during the following fiscal year's annual review cycle. After all the requirements are submitted, GSA will obtain updated data from our contractor to determine whether a per diem rate should be increased, decreased or remain unchanged. We will conduct no more than one "special" review for a particular NSA annually.

Letters should be sent to: General Services Administration, Office of Government-wide Policy, 1800 F St. NW., Washington, DC 20405. For more direct service, please also scan and email your request (a signed letter on agency letterhead must be attached) to [email protected] .

The procedure and the request deadline are the same as FAQ #6. However, requests received after 3/31 will not be included in the following fiscal year's annual review cycle because the annual review will have already begun.

If a city is not listed, check to ensure that the county within which it is located is also not listed. Visit the Census Geocoder to determine the county a destination is located in. If the city is not listed, but the county is, then the per diem rate is the rate for that entire county. If the city and the county are not listed, then that area receives the standard CONUS location rate.

Hotels are not required to honor the federal per diem rates. It is each property’s business decision whether or not to offer the rate. Hotels also may or may not choose to extend the rate to other individuals, such as government contractors.

Hotels sometimes offer a "GOV" rate, which might be different than the federal per diem rate. If it is higher, you need to receive approval for actual expense prior to travel in order to receive full reimbursement. It is the traveler’s responsibility to know the federal per diem reimbursement rates, and should not assume a GOV rate is the same as the federal per diem rate. See the FTR Chapter 301, Subpart D-Actual Expense and follow your agency's guidelines.

Lodging taxes are not included in the CONUS per diem rate. The Federal Travel Regulation 301-11.27 states that in CONUS, lodging taxes paid by the federal traveler are reimbursable as a miscellaneous travel expense limited to the taxes on reimbursable lodging costs. For foreign areas, lodging taxes have not been removed from the foreign per diem rates established by the Department of State. Separate claims for lodging taxes incurred in foreign areas not allowed. Some states and local governments may exempt federal travelers from the payment of taxes. For more information regarding tax exempt status, travelers should visit the State Tax Forms page.

Yes, the meals and incidental expense (M&IE) rate does include taxes and tips in the rate, so travelers will not be reimbursed separately for those items.

The Federal Travel Regulation Chapter 300, Part 300-3 , under Per Diem Allowance, describes incidental expenses as: Fees and tips given to porters, baggage carriers, hotel staff, and staff on ships.

An M&IE study has traditionally been conducted every three to five years. Based upon the recommendations of the Governmentwide Travel Advisory Committee, GSA began reviewing rates every three years starting with rates for FY 2016.

On the first and last travel day, Federal employees are only eligible for 75 percent of the total M&IE rate for their temporary duty travel location (not the official duty station location). For your convenience, the M&IE breakdown page has a table showing the calculated amount for the "First and Last Day of Travel."

For federal employees, the Federal Travel Regulation (FTR) does not make a provision for "mixing and matching" reimbursement rates. The lodging per diem rates are a maximum amount; the traveler only receives actual lodging costs up to that maximum rate. Therefore, there is no "extra" lodging per diem to add to the M&IE rate. Likewise, the M&IE per diem cannot be given up or transferred to lodging costs. See FTR 301-11.100 and 301-11.101 for more information.

For any official temporary travel destination, you must provide a receipt to substantiate your claimed travel expenses for lodging and receipts for any authorized expenses incurred costing over $75, or a reason acceptable to your agency explaining why you are unable to provide the necessary receipt (see Federal Travel Regulation 301-11.25 ).

You may ask your agency to authorize the actual expense allowance provision. The Federal Travel Regulation (FTR) 301-11.300 through 306 notes that if lodging is not available at your temporary duty location, your agency may authorize or approve the maximum per diem rate of up to 300% of per diem for the location where lodging is obtained. You should also ensure you have checked www.fedrooms.com to confirm there are no rooms available at per diem in the area where you need to travel.

According to the Federal Travel Regulation (FTR), travelers are entitled to 75% of the prescribed meals and incidental expenses for one day travel away from your official station if it is longer than 12 hours. Please see FTR 301-11.101 .

GSA establishes per diem rates and related policies for federal travelers on official travel only, and cannot address specific inquiries concerning the payment of contractors. If the contractor is on a federal contract, check with the contracting officer to see what is stated in their contract. Contractors should also check the travel regulations of their company.

GSA establishes per diem rates, along with its policies for federal employees on official travel only. Truck-related questions should be addressed either to the Department of Transportation ( www.dot.gov ) or the Internal Revenue Service ( www.irs.gov ).

PER DIEM LOOK-UP

1 choose a location.

Error, The Per Diem API is not responding. Please try again later.

No results could be found for the location you've entered.

Rates for Alaska, Hawaii, U.S. Territories and Possessions are set by the Department of Defense .

Rates for foreign countries are set by the State Department .

2 Choose a date

Rates are available between 10/1/2021 and 09/30/2024.

The End Date of your trip can not occur before the Start Date.

Traveler reimbursement is based on the location of the work activities and not the accommodations, unless lodging is not available at the work activity, then the agency may authorize the rate where lodging is obtained.

Unless otherwise specified, the per diem locality is defined as "all locations within, or entirely surrounded by, the corporate limits of the key city, including independent entities located within those boundaries."

Per diem localities with county definitions shall include "all locations within, or entirely surrounded by, the corporate limits of the key city as well as the boundaries of the listed counties, including independent entities located within the boundaries of the key city and the listed counties (unless otherwise listed separately)."

When a military installation or Government - related facility(whether or not specifically named) is located partially within more than one city or county boundary, the applicable per diem rate for the entire installation or facility is the higher of the rates which apply to the cities and / or counties, even though part(s) of such activities may be located outside the defined per diem locality.

IMAGES

Trip rate calculations
Types of Itineraries
Trip rate calculations
Trip rate calculations
Trip rate calculations
Trip rate calculations

VIDEO

Trip
Hummer H3 up Hells Gate, Hells Revenge, Moab Utah
Trip Generation: Trip Rate analysis: Cross Classification: Regression Model by Prof Dr Asim Farooq
여행하다를 영어로💡travel / trip / take a trip? / take a travel?
trip meaning in Hindi
Display Refresh Rate Meaning || 90Hz, 120Hz, 144Hz ||

COMMENTS

3.4: Trip Generation
Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.
How to Determine Trip Generation Types
Pass-By and Diverted Number of Trips. Use either local data or ITE data to determine a percentage of the reduced trip generation that is pass-by or diverted. Similar to the ITE Trip Generation data, both pass-by and diverted trip percentages are available by average rate or an equation for many land uses. Use this percentage to calculate the ...
Trip Rate Definition
Trip Rate means number of trips per unit of related independent variable. Trip Rate. As stated in the Agreement between CBSD and CBSD Transportation Association Appendix "A", Section II, Letter B. All sport, field, special trips and wraparound trips or any other run not otherwise included in this salary schedule shall be compensated at the ...
Trip generation
Trip generation is the first step in the conventional four-step transportation forecasting process used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone (TAZ). Trip generation analysis focuses on residences and residential trip generation is thought of as a function of the social and economic attributes of households.
First Step of Four Step Modeling (Trip Generation)
Trip generation rates: The trip generation model uses annual or daily trip generation rates calculated using activities, population, and employment. We can estimate trip generation rates by calculating the average weekday peak-hour trips generated by a particular land use. The trip generation rate for each land-use type is the total number of ...
Fundamentals of Transportation/Trip Generation
Trip Generation is the first step in the conventional four-step transportation forecasting process (followed by Destination Choice, Mode Choice, and Route Choice), widely used for forecasting travel demands.It predicts the number of trips originating in or destined for a particular traffic analysis zone. Every trip has two ends, and we need to know where both of them are. The first part is ...
Trip generation in Transport Planning
Trip generation is estimated in three ways: (i) traditionally by linear and multiple regression. (ii) by aggregating the trip generating capability of a household or car and aggregating the total according to the distribution of each selected category in the zones, and. (iii) by household classification method through a catalogue of the ...
Trip Generation Definition
Trip Generation Definition . Trip Generation is a type of transportation forecasting that predicts the number of trips originating in or destined for a particular traffic analysis zone. The purpose of a trip generation is to assist the Director of Traffic in determining whether a Traffic Impact Analysis will be required; although,
trip-rate, n. meanings, etymology and more
What does the noun trip-rate mean? There is one meaning in OED's entry for the noun trip-rate. See 'Meaning & use' for definition, usage, and quotation evidence. Entry status. OED is undergoing a continuous programme of revision to modernize and improve definitions.
Trip and Parking Generation
Trip and Parking Generation. Click here to access information on Trip Generation. Parking Generation, 6th Edition - October 2023. The ITE Parking Generation Manual, 6th Edition is an educational tool for transportation professionals, zoning boards and others who are interested in estimating parking demand of a proposed development.The Parking Generation web app—ITEParkGen allows electronic ...
Chapter 4
Mean trip length statistics, both in miles and min- utes, also were estimated from the survey databases for use as benchmarks in future statewide model validation efforts; however, the survey analysis for this study did not include the calculation of state-by-state trip lengths. ... Trip rate comparisons found in Appendix G show a strong ...
A comprehensive review of trip generation models based on land use
The main objective of this paper was to provide a comprehensive review of the trip generation model associated with land use characteristics and advanced technologies in travel data collection. Further, various modelling approaches used in the literature were examined. Though socio-demographic, built environment, and land use characteristics ...
A comprehensive review of trip generation models based on land use
The trip rates corresponding to vehicular travel were identified and compared with those maintained in the ITE manual. Findings from the study concluded that healthcare and institutional land use had the maximum trip rates and a significant difference was observed when these obtained rates were compared with ITE rates.
Comparative analysis of trip generation models: results using home
Trip rates, or the dependent variable, clearly show a discrete nature. In addition, a very large proportion of trip rates by households are zeros or small values. ... Even though this result does not directly mean that the better-performance model should become the standard framework for trip generation forecasting, the conventional models can ...
Trip Generation Appendices
Trip Generation Appendices TGM Appendices. Click to download in Excel. Pass-By Data and Rate Tables. Time-of-Day Distribution - Truck. Time-of-Day Distribution - Vehicle . Trip Generation Data Plots - Modal. Click to download in PDF. 000s - Port and Terminal - Modal Data Plots 1. 200s - Residential - Modal Data Plots. 300s - Lodging - Modal ...
PDF Institute of Transportation Engineers Common Trip Generation Rates (Pm
Trip Generation Manual. is an area associated with almost homogeneous vehicle-centered access. Nearly all person trips that enter or exit a development site are by personal passenger or commercial vehicle. The area can be fully developed (or nearly so) at low-medium density with a mix of residential and commercial uses. The commercial land uses
Chapter 12. Spurious Operation and Spurious Trips
Step 1: First consider that any of the n elements may fail: k-out-of-n. k or more spurious operation failures leads to a spurious trip. Step 2: Calculate the spurious trip rate with respect to spurious operation (SO) failures: : The first element fails with a spurious operation rate n. λSO.
What Are Pass-by Trips?
Please review that if you need to get up to speed on the topic. Pass-by trips are a subset of trip generation that only apply to commercial/retail developments. They are the folks already on the road who the business hopes to suck into their site as they are driving by. Think about a gas station. A "new trip" for a gas station is me running out ...
Introduction to Transportation Modeling: Travel Demand Modeling and
Consequently, it may overestimate trip rates between high-income groups and nearby low-income Traffic Analysis Zones (TAZs). Therefore, incorporating more socio-economic factors into the model would enhance accuracy. ... If congestion does happen (meaning that traffic volume exceeds capacity), the speed of the route needs to be decreased ...
Trip generation rate Definition
The. Trip generation rate means the number of average weekday peak-hour trips generated by a particular land use. The land use categories correspond to those used in the regional travel model maintained by MCAG. The trip generation rate for each land- use category is the rate published by the Institute of Transportation Engineers, 6th Edition.
PDF Trip Generation Analysis in a Developing Country Context
Trip Generation Analysis in a Developing Country Context ISAAC K. T AKYI A household trip rate analysis that uses the cross-classification method and applies in a developing country context is presented. The importance of choosing, defining, and classifying variables and using an appropriate analytic technique related to the socio
Trip Generation Manual for Indian Cities
It includes the trip rates for both 2-wheeler and car usage. In the table, the "Per Household Trip Rate" indicates the average number of trips made by each household. The trip rates for 2-wheeler and car usage are specified for each type of house (BHK) and population size categories. Table 1 displays the trip rates based on the type of house.
Frequently asked questions, per diem
The U.S. General Services Administration (GSA) establishes the per diem reimbursement rates that federal agencies use to reimburse their employees for subsistence expenses incurred while on official travel within the continental U.S. (CONUS), which includes the 48 contiguous states and the District of Columbia.