trip generation equation

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3.4: Trip Generation

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Page ID 47326

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?

Forecasting

7 comments

Traffic Impact Study Improvements: Part 5 – When is a Trip Not a Trip?

By Mike Spack

October 27, 2015

Guest Post by Bryant Ficek, PE, PTOE, Vice President at Spack Consulting

Earlier this year, I detailed how our standard process for a Traffic Impact Study has several points of assumptions at best or guesses at worst. This post continues that discussion. Check out the “ Top 6 Ways to Pick Apart a Traffic Study ” for more on the general topic and expect more posts to follow on this subject.

Trip generation is the process of estimating the amount of traffic a proposed development will have once it is built and operating. Trip distribution is the process by which we take the raw projected traffic for a development (trip generation) and add it to the existing volumes on the transportation network. The step in-between is determining whether all the trip generation will be new to the roadway.

To start with, there are several types of trips as follows (with definition summarized from the Institute of Transportation Engineers or ITE). The figure below illustrates the different types of trips.

Primary or New. Traffic with the specific purpose of visiting the site being studied.
Pass-By. Traffic already on the way from an origin to a primary trip destination that will make an intermediate stop at the site being studied without a route diversion.
Diverted. Traffic attracted to the site being studied from adjacent facilities without direct access to the site. A diverted trip example is a through trip on a freeway that diverts to an exit and a development, adding traffic to the local road but removing traffic from the freeway.
Internal. Traffic associated with multi-use developments where trips among various land uses can be made on the site being studied without using the major street system. These trips can be made either by walking or by vehicles using internal roadways.

These different trip generation options, combined with so many different types of land uses, leads to virtually limitless possibilities for the amount and type of traffic a particular site could generate on the roadway system. As with our trip distribution column, we initially thought about testing multiple scenarios, which would be relatively easy with today’s software. At least, theoretically. To restate our collective conclusion – While interesting on a pure research level, a thicker actual traffic impact study report covering multiple results leads us down a path no one wants to go.

Furthermore, sub-dividing the raw trip generation into parts is not something that can be quantified into a “one-size fits all” equation. Given the possibilities and the limits of our collective traffic research to date, ITE provides the best procedure to follow. So this article is dedicated to reviewing that procedure, which is spelled out in ITE’s Trip Generation Handbook and Trip Generation Manual, Volume 1 . That step-by-step process is as follows:

Raw Trip Generation. Using ITE or other land use information (try tripgeneration.org !), calculate the raw trip generation for the site.
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 total pass-by and diverted trips for the site.
Pass-By and Diverted Trip Patterns. Use the existing traffic to determine how the pass-by and diverted trips will access the site.
Pass-By and Diverted Trip Volume Adjustment. Apply the existing traffic patterns to the pass-by and diverted trips to establish the impact on the roadway system for these trips.
Remaining Primary/New trips. Determine the remaining trip generation after reducing for internal trips and then removing the pass-by and diverted trips.
Primary/New Trip Pattern. We discussed factors to consider for the primary/new trip distribution in Part 4 of this series.
Primary/New Trip Volume Adjustment . Apply the trip distribution to the primary/new trips to determine the impact on the roadway system for these trips.
Final Volumes. Combine the pass-by, diverted, and primary/new trips at each study intersection to determine the final impact of the site being studied.

We can demonstrate this process on a theoretical study site with the following characteristics:

17,000 square feet of office, 3,000 square feet of fast food with a drive-thru, and 10 vehicle fueling positions at a gas station with convenience market
One driveway accesses the site off a busy road (1,000 vehicles in the p.m. peak hour)
A highway interchange with the busy road is located just east of the site
Trip Generation (PM Peak)
General Office, Land Use 710 – 98 raw trips
Fast Food with Drive Thru, Land Use 934 – 98 raw trips
Gas Station with Convenience Market, Land Use 945 – 136 raw trips
Internal Trips

4. Pass-By and Diverted Patterns (per the theoretical roadway data)

6. Remaining Primary/New trips:

Office – (98 raw – 5 internal – 0 pass-by – 0 diverted) = 93 primary/new trips
Fast Food – (98 raw – 21 internal – 43 pass-by – 23 diverted) = 11 primary/new trips
Gas Station – (136 raw – 22 internal – 57 pass-by – 26 diverted) = 31 primary/new trips

7. Primary/New Trip Pattern (per knowledge of theoretical area)

8. Primary/New Trip Volume Adjustment

9. Final Volumes (add the pass-by, diverted, and primary/new trips together)

We don’t include all of the above example graphs in our reports. Instead, our short-hand method is a trip generation table that looks like this:

As a final note, the internal, pass-by, diverted, and new percentages are often adjusted from the base ITE information. ITE itself notes the limited amount of data available and the inherent variability in surveyed sites. The best approach, if possible, is to discuss the percentages with the governing agency to achieve agreement and buy-in before you get too far down the path in your analysis.

Did you miss the other installments of the Traffic Impact Study Improvements series? Here are the links to the other articles:

Part 1 – Traffic Counts
Part 2 – Would Multiple Results Help Us?
Part 3 – All Trips are Equal, But Some Trips are More Equal Than Others

At what point would you apply a reduction for non-SOV trips such as transit? It looks like ITE would have you apply internal capture, transit and then pass-by.

very precious and informative post…thanks so much..I had found the answer of one of my question in another post of yours..thanks for sharing your experiences

Sometimes I drive right by a coffee shop and then think, hmm, I could really use a good cup of black coffee and then I turn around and go back. Is this a pass-by trip or a diverted trip (or both?). What about a cappuccino?

If the need for java hit as you approached the site, and you turned right in, this would be a standard diversion. But since you passed the driveway and turned around, ….you need to take public transit and stop burning up our resources.

Hey Mike, After you do trip gen. calcs (let’s say for a built-up year 2029) but you want to consider design year (typical 20 years) and you want to design/improve nearby roads for the design year 2049 (2029 + 20 years); do you apply same growth rate on trip generated as you would do for existing traffic? or you apply the growth rate on existing traffic, and use trip gen numbers without applying a growth rate?

No – the growth rate is not applied to the trip generation. For instance, the trips generated by a single family home isn’t going to keep growing over time. They’re assumed to be static.

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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

11 Second Step of Four Step Modeling (Trip Distribution)

Chapter overview.

This chapter describes the second step, trip distribution, of the four-step travel demand modeling (FSM). This step focuses on the procedure that distributes the trips after trip generation has been modeled, meaning after trips generated from or attracted to each zone in the study area are understood. The input for this step of FSM is the output from the previous step discussed in Chapter 10, trip generation plus the interzonal transportation costs introduced in this chapter. Based on the concepts of the gravity model, the trip flows between pairs of zones can be calculated as an origin-to-destination (O-D) matrix. The essential concepts and techniques, such as growth factors and calibration methods, for this step are also discussed in this chapter.

Learning Objectives

Explain trip distribution and how to relate it to the first step (trip generation) results.
Summarize the factors that determine the level of attractiveness of zones in a travel demand model.
Summarize and compare different methods for trip distribution estimation within FSM.
Complete the trip distribution step by balancing total trip productions and attractions after the trip distribution step.

Introduction

This chapter delves into trip distribution, which is the second step of the Four-Step Model (FSM). After generating trip productions and attractions (P-A trips) by zone in the first step, the next task is to compute the number of trips between each pair of zones, referred to as trip distribution. These outputs are commonly known as Origin-Destination pairs (O-D pairs or Tij, as discussed in Chapter 9), indicating the number of trips between Zone i (origin) and Zone j (destination) (Levine, 2010). Essentially, trip distribution transforms the outcomes of the first FSM step into a comprehensive matrix detailing origins and destinations in Traffic Analysis Zones (TAZs). It also considers travel impedance factors, such as travel time or cost, for each O-D pair. Figure 11.1 illustrates the input (P-A trips) and outputs (trip tables) of this step in the model, highlighting the role of impedance functions initially introduced in Chapter 3 and discussed further in this chapter.

trip attractions and productions for each trip purposes that becomes trip distribution (matrix) for each trip purpose.

Recall from Chapter 10, that each step of the FSM answers a question specific to the step but central to model determining travel demand in a study area. For the trip distribution step, the main question is “What portion of trips produced in or attracted to a zone would go to each of the other zones?” There are several methods typically used to estimate trip distribution. While growth factor models and the intervening opportunities model are used , the gravity model is the most common.

There are a few foundational components to consider prior to calculating trip distribution. It is important to note that these components are independent of the FSM framework, or the methods used for trip distribution estimation. However, they serve as inputs for estimating trip distribution. As previously mentioned, trip distribution constitutes the second step of the FSM, where trip productions are allocated to all other zones. The outcomes yield a matrix that displays the number of both intrazonal and interzonal trips in a single table (Lincoln MPO, 2011).

The attractiveness of a zone is influenced by several factors (Cesario, 1973):

Uniqueness : This factor indicates how unique a service or employment center is and thus attracts more trips regardless of distance.
Distance: The spatial separation, distance, between two zones plays an impedance role, meaning that the further the two zones are from each other, the fewer trips will be distributed between them.
Closeness to other services: The assumption is that proximity to other desirable services will result in more trips to that zone within an urban area.
Urban or rural area : The assumption is that the attraction rate for a zone differs based on its urban or rural classification, while controlling for other factors.

In addition to the destination’s attractiveness factors, the origin’ has an emissivity factor, which is usually represented by population, employment, or income (Cesario, 1973). With a general understanding of the factors affecting trip distribution from origin and destination, we can now proceed with an introduction to methodology.

Gravity Model

As we discussed, the gravity model is the most common method used to estimate trip distribution. Gravity models are easy to understand and very accurate, and they can also accommodate varied factors such as population, employment, socio-demographics, and transportation systems. Almost all U.S. Departments of Transportation (DOTs) use gravity models. In contrast, the Growth Factor Model, discussed in a subsequent section, requires additional data about trip distribution in the base year and an estimate of the number of future trips in each zone, which is only sometimes available (Meyer, 2016).

It is important to remember that the Gravity Model is built based on the number of trips made between two zones. The number of trips is directly linked to the total number of attractions in the destination zone and inversely proportional to a function of cost, which may be represented by travel time or trip cost (Council, 2006). The formula gets its name from Newton’s Law of Gravity , which states that the attractiveness between two bodies is related to their mass (positive attraction) and the distance between them (negative attraction) (Verlinde, 2011). In transportation modeling, the two main factors are trip production and attraction, along with the time duration of travel or the cost of travel. While using the gravity model is simple, determining the optimal value for the impedance factor can be difficult. This value is highly dependent on the context and can vary by circumstances.

Equation below shows the fundamental equation of trip distribution:

Trips between TAZ1 and TAZ2=Trips prodduced in TAZ1*(Attractiveness of TAZ2 /Attractiveness of all TAZs

As equation (1) shows, the total trips between zones are equal to the products of the trips produced in a zone, a ratio of the attractiveness of the destination zone, and the total attractiveness of all zones. We can represent the gravity model in several different ways. Remodifying equation the original equation, the gravity model can be rewritten as:

Trips ij =Productions i *(Attractions j *FF ij *k ij /∑Attractions j *FF ij *k ij )

Where Trips ij is the number of trips between zone i and zone j , Prouctionsi is trip production in zone i , Attractionsj is total trips attracted to zone j , FF ij is the friction factor (travel impedance) between i and j , and K ij are the socio-economic factors of zones i and j . These values will be elaborated later in this chapter.

From the above equations, the mathematical format of gravity model can be seen in equation below:

$T_ij=P_i\ [(A_j\ F_ij\ K_ij)/(\sum_l\ A_j\ F_ij\ K_ij\ )]$

T ij = number of trips that are produced in zone i and attracted to zone j

P i = total number of trips produced in zone i

A j = number of trips attracted to zone j

F ij = a value which is an inverse function of travel time

K ij = socioeconomic adjustment factor for interchange ij

Recall that the Pi and Aj values are determined through the trip generation process (refer to Chapter 10), and the sum of all productions and attractions should be equal (PE, 2017). Numerous studies confirm that people value travel time differently based on the purpose of the trip (like work trips vs. recreational trips) (Hansen, 1962; Allen, 1984; Thill & Kim, 2005). Therefore, it is rational to compute the gravity model for each trip purpose using different impedance factors (Meyer, 2016).

Impedance Factor

The impedance factor (aka friction factor) is a value that varies for different trip purposes because, with the FSM model, the assumption is that travel behavior depends on trip purpose. Impedance captures the spatial separation between two zones, represented as travel time or cost.

Friction factors (FF) can be estimated using different measure, as follows:

A simple measure of friction is the travel time between the zones.
Another method is adopting an exponential formula with the 1/exp (m × Tij) friction factor, where m is the average travel time calculated using empirical data.
Gamma distribution uses scaling factors to estimate distribution (Cambridge Systematics, 2010; Meyer, 2016).

The impedance factor reflects the difficulty of traveling between two zones. The friction factor is higher when accessibility between two zones is easy and is zero if no individual is willing to travel between two zones.

There is also a calibration step in the friction factor estimation process. For calibration, trip generation and attraction values are distributed between O-D pairs using the gravity model. Next, the number of trips is compared with a particular amount of time to the results of the O-D survey (observed data). If the numbers do not match, calibration adjusts for the friction factor. When using travel time as the measure of impedance, the relationship between the friction factor and time in the is represented as t-1, t–2, e– t (Ashford & Covault, 1969).

The friction factor is estimated for the entire analysis area. However, such an assumption is limiting because travel costs or time has different implications for different households. For example, a toll on a specific highway may result in disparate use. The cost burden or friction factor may be too high for low-income individuals or households, meaning a factor greater than zero, but negligible for higher-income households. In this case the friction factor for higher-income commuters is zero.

The friction factors for different trip purposes can also be specified. The figure below (Figure 11.2) shows the function of the friction factor appropriate to the time and for different trip purposes. As the figure shows, there is a direct relationship between friction factor and travel time. According to this figure, for each trip purpose, there is a perception about the length or impedance of trip. Beyond certain length, friction factor approaches zero, meaning a high level of disutility of the trip and this threshold is different for each trip purpose. In very general terms, a friction factor Fij that is an inverse function of travel impedance Wij is used in trip distribution to plug in the travelers’ willingness to travel between zone i and zone j .

This figure shows the curve of impedance function calibrated for each trip purpose.

In very general terms, a friction factor F ij that is an inverse function of travel impedance W ij is used in trip distribution to plug in the travelers’ willingness to travel between zone i and zone j .

$F_{ij} = \frac{1}{W_{ij}}$

Travel demand modeling is influenced by various socio-economic factors that affect travel behavior and demand for different purposes. Chapter 10 highlights the most significant factors in travel demand modeling: income, auto ownership, availability of multimodal transportation systems, age, and job type (Pan et al., 2020). Therefore, the K-Factor method was developed and plugged into the gravity model to represent variation in socio-economic factors and adjust interzonal trips accordingly. For example, a blue-collar employee working in a low-income suburb may exhibit different travel behaviors (in terms of mode choice and frequency of travel) compared to a white-collar employee working in the central city with a higher income. The K-factor is determined and plugged into the gravity formula to accommodate such differences.

Recall the classic land use models presented in Chapter 4. Based on the proximity to employment centers or the central business districts, different neighborhoods offer housing and job options tailored to individuals in different income brackets. For example, the earnings of employees in chain restaurants significantly contrast with those working in Central Business District (CBD) headquarters. Prevailing land use policies and the typical American development pattern heighten this disparity. Consequently, these groups are likely to inhabit geographically distant areas in a country like the United States. Furthermore, people of varying income levels or social statuses may respond differently to travel impediments, such as travel time or cost.

Calibration of K values is determined by comparing the estimated results and observed data for the base year (Tawfik & Rakha, 2012). The K numeric value will be above one (>1) if the socio-economic factors contribute to more travel and less than one (< 1) if otherwise (Meyer, 2016). Figure 11.3 shows the mean number of trips for different age groups (K-factor) and various trip purposes. Accordingly, calculating friction factors and K-factors for different purposes and socio-economic groups yields a better fit to the data.

number of trips by age for 4 trip purposes (work, shopping, family and social) for three years (1990, 2001, 2009).

11.2.3 Example 1

Consider a small area with three zones (TAZs). Table 11.1 shows the trip generation results for each zone, and Table 11.2 shows the travel time for each pair of zones. The friction factor is also given in this example as a function of travel time in Table 11.3. The intrazonal travel time for zone 1 is larger than that of most other inter-zone times because of the geographical characteristics of the zone and lack of access within the area. Using this information, please calculate the number of trips for each pair of zones.

For the calculation of trip distribution between the three zones, the trip generation and attraction table from step one of the FSM model is the input data, and then the gravity model is used for calculation. Tables 11.1, 11.2, and 11.3 represent the trip generated and attracted for each zone, travel time between each pair of zones, and friction factor derived from the travel time.

Now with this information, we can start the calculation process. First, we have to estimate the attractiveness of each zone using the equation (1)

For example, for zone 1 we have:

Attractiveness1= 210*26=5460

Attractiveness2= 210*35=7350

Attractiveness3= 350*35=12250

Now, we use the pivotal formula of the gravity model (equation 2). Accordingly, we have (K-factor set to 1):

$T_{1-1}=220\times\frac{210\times26}{(210\times26) (270\times41) (350\times52)}$

The result of the calculation is summarized in Table 11.4:

However, our calculations’ results do not match the already existing and observed data. The mentioned mismatch is why calibration and balancing of the matrix are needed. In other words, we must perform more than one iteration of the model to generate more accurate results. For performing a double or triple iteration, we use a formula discussed at the end of this chapter (example adapted from: Garber & Hoel, 2018).

Growth Factor Model

After successfully calibrating and validating the data we have estimated, we can also apply the gravity model to forecast future travel behavior or travel patterns in our study area. Future trip distributions can be predicted by using the change in land-use data, socioeconomic data, or any other changes in the whole system. We can calculate trip distribution from the O-D table for either base or forecasting year when the friction factor and K-factor data are unavailable or unsatisfactorily calibrated. Depending on historical trends and data, growth factor models are limited if an observed O-D table is unavailable. Similar to the trip generation step, growth factor models cannot incorporate updated travel time as the change in travel time between zones can highly affect travel patterns (Qsim, 2016).

Fratar method

One of the most common mathematical formulas of the growth factor model is the Fratar method, shown in the following equation. Through his method, the future distribution of trips from one zone is equal to the present distribution multiplied by the growth factor of the destination zone between now and the forecasting year (Heanue & Pyers, 1966). The formula to calculate future trip values is shown in equation below:

$T_{ij}=\left(t_iG_i\right)\frac{t_{ij}G_j}{\sum_{x}\hairsp\hairsp t_{ix}G_x}$

T ij =number of trips estimated from zone to zone t i =present trip generation in zone G x =growth factor of zone T i =future trip generation in zone t ix =number of trips between zone and other zones t ij =present trips between zone and zone G j =growth factor of zone

The following section will discuss an example illustrating the application of the Fratar method.

The case study area of this example consists of four TAZs. Table 11.5 shows the current trip distributions. Assuming the growth rate for each TAZ is shown in Table 11.6, the next step is to calculate the number of trips between each two TAZs in the future year.

To solve this problem, apply the Fratar Method using the required two estimates for each pair. These estimates should be averaged; the resulting value will be the final T ij . Based on the formula, calculations are as follows:

Based on the calculations, the first iteration of the method will yield the following table:

To estimate future trip rates between zones, use the Fratar formula, as shown in Table 11.7. However, there is a problem with the estimated total number of trips generated in each zone not being equal to the actual trip generation. Therefore, a second iteration is necessary. In this second iteration, we use the new O-D matrix as the input to calculate new growth ratios. The trip generation is estimated to occur in the next five years based on the preceding calculation. As an exercise, you can conduct as many iterations as needed to bring the estimated and actual trip generations into alignment.

In a hypothetical area, we are interested in determining the number of trips attracted by three different shopping malls at various distances from a university campus that generates about 2,000 trips per day. In Figure 11.4, the hypothetical area, the number of trips generated by the campus, and the total number of trips attracted for each zone are presented:

This figure shows the trip generator and the three possible destination with their travel time.

socioeconomic adj. Factor K=1.0
Calibration factor C=2.0

As the first step, we need to calculate the friction factor for each pair of zones based on travel time (t). Given is the following formula with which we calculate friction factor:

$F_{1j} = t_{ij}^{-2}$

Next, using the friction factor, we use the gravity model to calculate the relative attractiveness of each zone. In Table 11.8 , you can see how calculations are being carried out for each zone.

Next, with having relative attractiveness of each zone (or probability of attracting trips), we plug in the trip generation rate for the campus (6,000) to finally estimate the number of trips attracted from the campus to each zone. Figure 11.5 shows the final results.

This figure shows the results of example 3 graphically.

Model Calibration and Validation

Model validation is an integral part of all simulation and modeling procedures. One of the most essential steps in FSM modeling is developing a procedure to calibrate its final outputs (predictions) with actual and observed data. To do this, model parameters are adjusted so that the observed data and estimations have fewer mismatches (Meyer, 2016). After such adjustments, the model with calibrated parameters can help in simulation and future scenario analyses.

After completing the trip distribution step, it is important to compare model calibration and adjustment results in each category (i.e., by trip purpose) with recorded real-world trips from the O-D survey.

If the two values are not identical, model parameters, like FF or K-factors, are reassigned and re-run the gravity model. The process continues until the observed data and estimations are very close (ratio between 0.9 and 1.1).

The following example shows the process of trip distribution step with calibration.

11.4.1 Example 4

This example demonstrates the calibration process. The first step is to identify the model’s inputs, which are the outcomes of the trip generation process. The following tables show the results obtained from surveys and actual trip data, as well as the travel time between each pair of zones (represented by the friction factor (FF)) and the socioeconomic conditions (Tables 11.10, 11.11, and 11.12 ). The column with the heading “A’ “in Table 11.11 represents observed generated trips.

here are several formulas, such as negative exponential or inverse power function, that can be used to calculate friction factors from impeding factors like travel cost or time, as discussed in previous sections. To estimate the number of trips between each pair of zones, we use the gravity formula and input the necessary data. Table 11.14 shows the results of trip distribution for each pair of zones. However, the total number of trips attracted from our calculations is different from observed data.

Now, by looking to the last table we can see that the total number of trips produced is exactly matching to the results of the trip generation table. But the total attractions and actual data have a mismatch. In the next step, we apply the calibration methods in order to make our final results more accurate.

In the first iteration of calibration, we have to generate a value called column factor, which is the result of dividing actual data attraction by estimated attractions. Then we apply this number for each pair in the same column. In Table 11.15, we can observe that the sum of attractions is now the same as the actual data, but the sum of generation amounts is now different from actual data generation. In this step, we perform another iteration, the same as the first iteration but instead of column factor, we plug in row factor value, which is the result of dividing actual data trip generation by estimated generation.

A third iteration is needed because the sum of attraction is still different from the actual data, and we must generate another column factor. The results are shown in Table 11.17.

Based on the results of the third iteration results, we see the attractions are now accurate, and trip generations have very insignificant differences with actual data. At this point, we can stop the calibration. However, the procedure can continue to calibrate results to decrease the difference as much as possible. In other words, the sensitivity of the calibration, the threshold for the row and column factors, can be adjusted by the modeler.

In this chapter, we demonstrated the procedure, application and other details of the second step of FSM modeling framework. Using the concept of gravity-based accessibility, we saw how the production and attraction table can be transformed into a trip distribution matrix. By using simple numerical examples, we showed how different methods can be applied to calculate trips between pair of zones. Assumption of homogenous behavior, assumption of static and sequential behavior, aggregation biases, and less emphasis on lots of social and physical barriers. Dynamic modeling (concurrent mode and destination choice), micro-simulation, agent-based models or newer methods such machine learning have made several enhancement to the traditional model. Collection of real-time data as well as increase in computational capacity has opened such prospects in travel demand modeling and trip distribution studies.

Emissivity is a quantity that represents the trip production rate of a neighborhood, similar to attractiveness for trip attraction.
Intra zonal trips are those trips that both ends of the trip is in the same zone.
Interzonal Trips are those trips where one end of the trip is in a different zone.
Uniqueness is a quantity defined for a TAZ that indicates how unique that zone or trip attraction center is.
Gamma distribution is a probability distribution that is used for converting travel times into impedance functions

Blue-collar employee is a worker who usually performs manual and low-skill duties for their work.

White-collar employee is a worker who is high-skill and performs professional, or administrative work.

Zonal Emissivity refers to a quantity that represents the trip making rates for that zone. Factors affecting this feature can be population, employment, income level, vehicle ownership, etc.

Key Takeaways

In this chapter, we covered:

What trip distribution is and the factors that determine attractiveness of zones for travel demand.
Different modeling frameworks appropriate for trip distribution and their mathematical formulation.
What advantages and disadvantages of different methods and assumptions in trip distribution are.
How to perform a trip distribution manually using simplified transportation networks.

Prep/quiz/assessments

What factors affect the attractiveness of the zones in trip distribution, and what input data is needed to measure such attractiveness?
What are the advantages and disadvantages of the three trip distribution methods (gravity model, intervening opportunities, and Fratar model)?
What are the friction factor and K-factor in trip distribution, and how do they help to calibrate model results?
How should we balance trip attraction and production after performing trip distribution? Explain.

Allen, B. (1984). Trip distribution using composite impedance. Transportation Research Record , 944 , 118–127.

Seggerman, KE. (2010). Increasing the integration of TDM into the land use and development process. Fairfax County (Virginia) Department of Transportation, May. Department of Transportation.

Cesario, F. J. (1973). A generalized trip distribution model. Regional Science Journal , 13 (2), 1973-08

Council, A. T. (2006). National guidelines for transport system management in Australia 2006 . Australia Transportation Council. https://www.atap.gov.au/sites/default/files/National_Guidelines_Volume_1.pdf

Garber, N. J., & Hoel, L. A. (2018). Traffic and highway engineering . Cengage Learning.

Hansen, W. G. (1962). Evaluation of gravity model trip distribution procedures . Highway Research Board Bulletin, 347 . https://onlinepubs.trb.org/Onlinepubs/hrbbulletin/347/347-007.pdf

Ned Levine (2015). CrimeStat : A spatial statistics program for the analysis of crime incident locations (v 4.02). Ned Levine & Associates, Houston, Texas, and the National Institute of Justice, Washington, D.C. August.

Lima & Associates. (2011). Lincoln travel demand model . Lincoln Metropolitan Planning Organization. (2011). https://www.lincoln.ne.gov/files/sharedassets/public/planning/mpo/projects-amp-reports/tdm11.pdf

Meyer, M. D. (2016). Transportation planning handbook . John Wiley & Sons.

NHI. (2005). Introduction to Urban Travel Demand Forecasting . In National Highway Administration (Ed.), Introduction to Urban Travel Demand Forecasting. American University. . National Highway Institute : Search for Courses (dot.gov)

Pan, Q., Jin, Z., & Liu, X. (2020). Measuring the effects of job competition and matching on employment accessibility. Transportation Research Part D: Transport and Environment , 87 , 102535. https://doi.org/10.1016/j.trd.2020.102535

PE Lindeburg, M. R. (2017). PPI FE civil review – A comprehensive FE civil review manual (First edition). PPI, a Kaplan Company.

Qasim, G. (2015). Travel demand modeling: AL-Amarah city as a case study . [Unpublished Doctoral dissertation , the Engineering College University of Baghdad]

Tawfik, A. M., & Rakha, H. A. (2012). Human aspects of route choice behavior: Incorporating perceptions, learning trends, latent classes, and personality traits in the modeling of driver heterogeneity in route choice behavior . Virginia Tech Transportation Institute . Blacksburg, Virginia https://vtechworks.lib.vt.edu/handle/10919/55070

Thill, J.-C., & Kim, M. (2005). Trip making, induced travel demand, and accessibility. Journal of Geographical Systems , 7 (2), 229–248. https://doi.org/10.1007/s10109-005-0158-3

Verlinde, E. (2011). On the origin of gravity and the laws of Newton. Journal of High Energy Physics , 2011 (4), 1–27. https://link.springer.com/content/pdf/10.1007/JHEP04(2011)029.pdf

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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 Generation

Trip Generation is the first step in the Sequential Demand Modelling arrangement which is also called as the Four Step Transportation Planning Process(FSTP) as mentioned earlier. In order to carry out modelling, the variable consists of total number of person-trips generated by a zone as a dependent variable and the independent variable consists of household and socio- economic factors which influence the trip making behaviour of the person. The data for the independent variable should be attained from an analyst. The output thus obtained consists of trip making or trip ends for each zone within a region.

In contemporary transportation planning language, A Trip is defined as a one way person movement by a mechanized mode of transport, having two trip ends. The start of the trip is called as origin and the end of trip is called as destination. Trip is classified as Production or Origin and Attraction or Destination. It should be note that the terminologies used are not identical. To understand with an example consider a single worker on a typical working day making a trip from his house which is in zone P to his office in Zone Q . Thus his trip origin will be zone P and trip destination will be zone Q . For the return trip from office to house his trip origin will be zone Q and trip Destination will be Zone P . Thus from the above Example it can be understood that the term Origin and Destination are defined in terms of direction of the trip while Production and Attraction in terms of land use associated with each trip end. Trip Production is the home end of home based trip and is the origin of trip of non home based trip. Trip Attraction is the non home end of home based trip and is the destination of a non home based trip.

It has been found that better trip generation models can be obtained if the trips by different purpose are identified and modelled separately.The trips can be classified as given below:

1. Home Based Trip: One of the trip end is home.

Example: A trip from home to office.

Following are the list of home based trips that is trip purpose which are classified into five categories:

a. Work Trips

b. School Trips

c. Shopping Trips

d. Social- recreational Trips

e. Other Trips

The first two trips are mandatory trips while other trips are discretional trips. The other trip class encompasses all the trips made for less routine purpose such as health bureaucracy etc.

2. Non Home based trips: None of the trip end is home.

Example: A trip from office to Shopping Mall.

3. Time based trips

The proportion of journey is different by different purposes usually varies with time of the day. Thus the classification is often given as Peak and Off Peak Period Trip.

4. Person-type based trips

The travel behaviour of an individual is mainly dependent on its Socio-Economic attributes. Following are the categories which are usually employed.

a. Income Level- Poor, Middle Class, Rich

b. Car Ownership- 0,1,2,3

c. Household Size- 1,2,3,4... etc

a. No. of workers in a household.

b. No. of Students.

c. Household size and composition.

d. The household income.

e. Some proxy of income such as number of cars etc.

a. Floor area and number of employment opportunities in retail trade, service, offices manufacturing and wholesale areas.

b. School and college enrolment

c. Other activity centres like transport terminals, sports stadium, major recreational/ cultural/religious places

Table below represents base year data of Trip Production for exact zone.

Similarly Trip Attraction Table is obtained with respect to its influencing variables.

Trip generation study typically involves the application of residential trip production which contains variable that defines the demographic makeup of zonal population and trip attraction that captures the activity of non residential activities within the zone.

In the example given below the zones are connected by a two way link. Each zone will have its own demographic and non residential characteristics depending on which the Trip Generation table is represented below.

Modelling basically relates the dependent variable ie trips produced by a zone for aggregated model or household trip production rate for household based models to the corresponding Independent variables characterised by the whole zone or household characteristic respectively. Calibration is done based on the set of observations obtained corresponding to the zones for aggregate model and for disaggregate model employs a number of base year observations corresponding to an individual household in a sample of household drawn randomly from the region. Thus we first need to identify what are the relevant variables: a. Home end b. Work End c. Shop End

Analytical tools used for Trip Generation Modelling are given below: 1. Regression Model (Regression Analysis) 2. Cross Classification Model (Category Analysis)

The purpose of trip generation is to estimate the number of trip ends for each zones for the targeted year. The trip end is calculated for different travel purpose within the zone. These trips are represented as residential trip production obtained from household based cross classification tables or non residential trip attractions which is obtained by projection of land use. Trip generation Models that are often used are Multiple Linear Regression Model or Cross Classification Model or involves combination of both.

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TRID the TRIS and ITRD database

TRIP GENERATION

This is the sixth edition of the Institute of Transportation Engineers' trip generation rates, plots, and equations. It is presented in three volumes. Volume 1 is organized according to the following land uses: Port and Terminal; Industrial/Agricultural; Residential; Lodging; and Recreational. Volume 2 continues with the following land uses: Institutional; Medical; Office; Retail; and Services. Volume 3 is a User's Guide containing general introductory, instructional, and appendix material. Users are encouraged to review and become familiar with the User's Guide prior to using the data contained in volumes 1 and 2. Together these volumes are an educational tool for planners, transportation professionals, zoning boards, and others who are interested in estimating the number of vehicle trips generated by a proposed development. This document is based on more than 3,750 trip generation studies submitted to the Institute by public agencies, developers, consulting firms, and associations. This edition includes several significant changes in format and content as compared to the fifth edition.

Find a library where document is available. Order URL: http://worldcat.org/isbn/0935403094

Institute of Transportation Engineers (ITE)

Publication Date: 1997
Pagination: 1700 p.

Subject/Index Terms

TRT Terms: Business districts ; Equations ; Farming ; Industrial areas ; Industrial buildings ; Intermodal terminals ; Ports ; Rates ; Recreational facilities ; Residential areas ; Transportation planning ; Trip generation ; Zoning
Old TRIS Terms: Agricultural land
Subject Areas: Highways; Planning and Forecasting; Terminals and Facilities; I72: Traffic and Transport Planning;

Filing Info

Accession Number: 00746624
Record Type: Publication
ISBN: 0935403094
Report/Paper Numbers: 6th Edition, 3 Volumes
Files: TRIS
Created Date: Mar 4 2002 12:00AM

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5.2 Accuracy . Transoft will make reasonable efforts to ensure that Personal Information is accurate and complete. The Subscriber may request a correction to their Personal Information in order to ensure its accuracy and completeness. A request to correct Personal Information must be made in writing and provide sufficient detail to identify the Personal Information and the correction being sought.

5.3 Security . Transoft is committed to ensuring the security of Personal Information in order to protect it from unauthorized access, collection, use, disclosure, copying, modification or disposal. Transoft has implemented the following security measures:

(a) limiting access to files to only those employees of Transoft who require the Personal Information in order to perform services for Transoft;

(b) using user IDs, passwords, encryption, and firewalls;

(c) when destroying Personal Information, using methods such as shredding documents and deleting electronically stored information; and

(d) storing Personal Information on secure servers.

6.1 Request . Subscribers may request in writing access to their Personal Information, except where:

(a) the Personal Information is protected by solicitor-client privilege;

(b) the disclosure of the information would reveal confidential information of Transoft; and

6.2 Delivery . Within 30 days of a request pursuant to paragraph 6.1 above, Transoft will:

(a) make the requested information available; or

(b) provide written notice of an extension where additional time is required to fulfill the request.

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terms and conditions

ONLINE SOFTWARE SUBLICENSE AGREEMENT

IMPORTANT! PLEASE CAREFULLY READ THE FOLLOWING AGREEMENT!

This is a legal agreement between you, as End User, on the one part (hereinafter referred to as “End User”, “You” or “Your”), and the INSTITUTE OF TRANSPORTATION ENGINEERS, a Connecticut non-profit corporation with its office at 1627 Eye Street, NW, Suite 600, Washington, DC 20006, United States hereinafter referred to as “ITE”) on the other part. This Agreement concerns and governs Your use of certain Licensed Products made available by ITE.

COPYING OR USE OF THE LICENSED PRODUCTS (AS DEFINED IN THIS AGREEMENT) EXCEPT AS EXPRESSLY PERMITTED BY THIS AGREEMENT IS STRICTLY PROHIBITED AND CONSTITUTES A MATERIAL BREACH OF THIS AGREEMENT AND AN INFRINGEMENT OF COPYRIGHT AND OTHER INTELLECTUAL PROPERTY RIGHTS IN AND TO THE LICENSED PRODUCTS

IF YOU COPY OR USE ALL OR ANY PORTION OF THE LICENSED PRODUCTS WITHOUT ACCEPTING AND ENTERING INTO THIS AGREEMENT OR OTHERWISE OBTAINING WRITTEN PERMISSION OF ITE, YOU ARE VIOLATING COPYRIGHT AND OTHER INTELLECTUAL PROPERTY LAWS AND YOU MAY BE LIABLE TO ITE AND ITS LICENSORS FOR DAMAGES. YOU MAY ALSO BE SUBJECT TO PROSECUTION UNDER APPLICABLE LAWS.

ACCEPTANCE OF AGREEMENT BEFORE USE.

BEFORE YOU USE OR SIGN UP FOR THE USE OF ANY PORTION OF THE LICENSED PRODUCTS, YOU MUST ACCEPT THE TERMS AND CONDITIONS OF THIS AGREEMENT BY CLICKING “I AGREE” By communicating to ITE Your acceptance of this Agreement when You use or sign up for the use of the Licensed Products, you are entering into a legal and binding contract with ITE and agree to be bound by the terms and conditions of this Agreement. If you are entering into this Agreement on behalf of a company or other legal entity, your acceptance represents that you have the authority to bind such entity to these terms, in which case the words “End User”, “You”, or “Your” shall refer to such company or other legal entity. If you do not agree with this Agreement, or if you do not have the authority to bind your entity, then you, the “End User” and “You”, will not be authorized to use the Licensed Products.

1.1 “Account” means the part of the Software that is personal to the use of the End User or an individual permitted to use the Software in accordance with section 2.7

1.2 “Commencement Date” means the date that the End User has accepted the terms of this Agreement and paid the License Fee;

1.3 “Confidential Information” means data, information, documents, knowledge, designs, products, services, systems, programs, plans, inventions, research, discoveries, developments, strategies, trade secrets, processes, technical information, production methods, marketing activities, personal information, or any information concerning the organization, business, finances, transactions, affairs of the Disclosing Party which may come to the Recipient’s knowledge pursuant to the terms of this Agreement. Notwithstanding the generality of the foregoing, Confidential Information does not include information that

(a) is already in possession of the Recipient or any of its parent, subsidiary or affiliated companies and was obtained without an obligation of confidence.

(b) is independently developed by the Recipient or any of its parent, subsidiary or affiliated companies;

(d) is acquired by the Recipient from a third party who provides the information without breaking any express or implied obligations or duties to the Disclosing Party;

(e) is released for disclosure by the Disclosing Party; or

(f) is disclosed in response to a valid order of a court or other governmental body of Canada or the United States of America or any political subdivisions thereof legally authorized to order the disclosure of certain information (“Authorized Entity”); provided, however, that the Recipient will first have given notice to the Disclosing Party, unless this is forbidden by the Authorized Entity, and made a reasonable effort to obtain a protective order requiring that the information or documents so disclosed be used only for the purposes for which the order was issued limit the disclosure and use of Confidential Information to the minimum required by the Authorized Entity.

1.4 “Desk Reference” means the Trip Generation 10th Edition Desk Reference whether in electronic or hardcopy, published by ITE. The Desk Reference provides detailed descriptions of the new urban and person-based data, key instructional information, sample plots and identifies significant changes from the previous edition.

1.5 “Disclosing Party” means a party disclosing Confidential Information;

1.6 “Documentation” means any documentation (whether in electronic or printed or printed form) developed by Transoft and provided to or made available to the End User by ITE or through Transoft for the purposes of identifying the terms and conditions of access, use or operation of the Software, explaining or describing the Software, providing instructions as to the manner of permitted access or use of the Software, assisting the End User with problems or corrections to the Software or otherwise intended to assist the End User in the effective and permitted access, use and operation of the Software, and includes but is not limited to user guides, manuals, Help Menus and text, FAQ (frequently asked questions) files, license files, license specifications and details but documentation does not include the ITE Products which are provided by ITE to the End User by way of separate agreement or purchase..

1.7 “License Term” is the term of this Agreement referred to in Section 3.1;

1.8 “ITE Data” means the trip generation data that forms the basis of the TGM, and as it pertains to this agreement includes 10th Edition trip generation data (including the 10th Edition Supplement);

1.9 “ITE Products” means the Desk Reference, the ITE Data, TGH, TGM and TGM plots

1.10 “Licensed Products” means the Software and the Documentation;

1.11 “License Fee” means the fee plus all applicable taxes payable by the End User to ITE as a condition of the End User’s use of the Licensed Products during the License Term.

1.12 “Login Details” means the details required to login and use the Licensed Products, such as a username and password;

1.13 “Recipient” means a party receiving Confidential Information;

1.14 “Software” means the online web application known as “ ITETripGen ” that will interface with ITE Data, be capable of supporting three databases (being all United States and Canada data, United States data only, and Canadian data only) and include features to look up specific data in either imperial or metric units, plots of vehicle and person trips in two-dimensional coloured graphs, and export such graphic plots in high-quality PDF ;

1.15 “TGH” means the Trip Generation Handbook, whether electronic or hardcopy, published by ITE from time to time which is currently in its 3rd Edition providing guidance on proper techniques for estimating person and vehicular trip generation rates; guidance for the evaluation of mixed use developments and the establishment of local trip generation rates; and pass-by trip and truck trip generation data.

1.16 “TGM” means the Trip Generation Manual , whether electronic or hardcopy, published by ITE from time to time and which is currently in its 10th Edition (including the 10th Edition Supplement), including land use descriptions, trip generation rates, equations and data plots and which are prepared, gathered, assembled and formatted by or on behalf of ITE from time to time for reference and use by transportation professionals conducting site impact studies, determining on-site circulation patterns, performing access management studies, determining traffic signal timing, conducting environmental assessments and other transportation related uses and activities;

1.17 “TGM plots” refers to the full set of data plots for all land uses distributed, published by ITE in electronic format. Subsets of the TGM plots, referred to as “Land Use Packages” are also published by ITE in electronic format.

1.18 “Transoft” means Transoft Solutions Inc. 350-13700 International Place Richmond, BC, Canada V6V 2X8, the owner and developer of the Software and the Documentation.

1.19 “Value Added Taxes” means such sum as will be levied upon the License Fee or any other fees payable pursuant to this Agreement by the Federal or any Provincial or Territorial Government and is computed as a percentage of the fees and includes Goods and Services Tax, Harmonized Sales Tax and any similar tax, the payment or collection of which, by the legislation imposing such tax, is an obligation of ITE.

2.0 LICENSE AND RESTRICTIONS

2.1 Software and Services. Pursuant to ITE’s license agreement with Transoft, ITE grants by way of a limited sub-license to the End User, subject to the terms and conditions of this Agreement the non-exclusive, non-transferable and non-assignable right to use the Licensed Products during the Term for the purpose of its business. ITE, through its subcontractor Transoft will host the Software, including providing data support, backup, and recovery and access to the Documentation. ITE will provide to you the ITE Products by way of separate agreement and purchase arrangements directly with ITE.

2.2 Additional Terms and Restrictions. ITE reserves the right to at any time change any of the terms of this Agreement. Without limiting the generality of the foregoing, ITE may include additional terms with respect to connection time, may limit the number of projects or look ups by the End User, and may set a minimum hardware and browser requirement.

2.3 Ownership. The Licensed Products are owned by Transoft and provided by way of sub-license to the End User through ITE. The ITE Products are owned by ITE and provided by way of license to the End User. The Licensed Products and the ITE Products are protected by Canadian Copyright law, U.S. Copyright law, the copyright laws of other nations, and international treaty provisions. It is an express term of this Agreement that the End User will not acquire title or ownership to the Licensed Products and only has a limited use sub-license and End User will not acquire title or ownership to the ITE Products and only has a limited use license

2.4 Reservation. Each of Transoft and ITE reserves all rights not expressly granted to the End User under this Agreement. Without limiting the generality of the foregoing, the End User acknowledges that the Licensed Products and the ITE Products contain trade secrets and agrees that the End User will not do or permit to be done any of the following in relation to the whole or any part of the Licensed Products:

(a) copy them;

(b) modify, adapt, translate or alter them;

(d) take any steps to produce a source language statement of the Software; or

(e) use the Software to develop any derivative works of functionally compatible or competitive computer programs to the Software or create derivative works based on the Software.

2.5 Restrictions. Notwithstanding the generality of this Agreement, the End User will not, and will not permit others to, transmit, convey, license, sublicense, distribute, sell, resell, transfer or otherwise dispose of the Licensed Products or the ITE Products to any other persons or organizations. The End User further agrees that the Software will not be accessed or used in any manner prohibited by the United States Export Administration Act or any other United States laws or any applicable national or international export laws, restrictions or regulations (collectively, the “Export Laws”). In addition, if the Software is identified as an export controlled item under the Export Laws, the End User represents and warrants that it is not a citizen of, or located within, an embargoed or otherwise restricted nation and that the End User is not otherwise prohibited under the Export Laws from receiving the Software. All rights to use the Software are granted on the condition that such rights are forfeited if You fail to comply with the terms of this Agreement.

2.6 Survival. The provisions of this Article 2.0 will survive termination of this Agreement.

2.7 One Individual User. Without limiting the generality of this Article 2.0, the End User acknowledges and agrees that the account and login ID assigned to the End User pursuant to the terms of this Agreement may not be used by any person other than one individual who is the intended user (the “Intended User”). Without limiting the generality of the foregoing, the End User will:

(a) not at any time authorize any person other than the Intended User to use the account or login ID of the Intended User; and

(b) keep secure Login Details for the Intended User, and will ensure that no person other than the Intended User has access to the Login Details.

Unless ITE has explicitly agreed and determined otherwise in writing, a license or sub-license is required and must be paid by End User for each Intended User. If You are uncertain about the applicable scope of the license, the number of permitted Intended Users or have other inquiries, kindly contact ITE or see the licensing tab in the program settings of the Software.

2.8 Data Collection and Cookies. The End User agrees that Transoft in the course of hosting or making available the Software for use by the End User may collect and use technical data and related information, including the collection of data and related information by the use of cookies. Data and information collected by Transoft may include, but is not limited to technical information about your use of the Software, Internet Protocols, hardware identification, operating system, network and application software, peripherals, and non-personally identifiable Software usage statistics that is gathered periodically to facilitate the provision of products, software updates, upgrades, fixes, product support services and other services to the End User (if any) related to the Software. However, Transoft will only collect data and information in a form that does not personally identify the End User. The collection of data and cookies will otherwise be made in accordance with Transoft’s cookie and privacy policies, as amended from time to time and made available at https://www.transoftsolutions.com/privacy/ . The End User may at any time opt out of the arrangement by blocking permission allowing Transoft to use cookies. However, the End User acknowledges and agrees that a failure to allow the use of cookies by Transoft may impair the End User’s user experience and the use, benefit and functionality of the Software may be diminished and neither ITE nor Transoft will be responsible for any such impairment or diminishment.

3.0 TERM AND TERMINATION

3.1 Term. The term of this Agreement will commence on the Commencement Date, and will remain in effect until the release of TGM Edition 11 or August 31, 2021 whichever comes first.

3.2 Termination. ITE will have the right to terminate this Agreement:

(a) Immediately upon written notice; or

(b) Immediately upon written notice at any time if:

(i) the End User is in material breach of any warranty, term, condition or covenant of the End User pursuant to this Agreement and fails to cure that breach within 5 days after written notice of that breach and of ITE’s intention to terminate;

(ii) notwithstanding paragraph 3.3(b) (i), and without limiting any of the other the rights of ITE pursuant to this Agreement or at law, if the End User fails to pay any amount owing to ITE in accordance with the terms of this Agreement or otherwise, within 15 days after written notice of that failure to pay and of ITE’s intention to terminate; or

Termination under paragraph 3.2(b) (i) and 3.2(b) (ii) above will in the absence of a cure become effective automatically upon expiration of the cure period set out in the applicable paragraph.

3.3 Upon termination of this Agreement:

(a) the End User will immediately cease using the Licensed Products and the sub-license herein and all rights to use the Licensed Products will expire; and

(b) the End User will immediately pay to ITE any amounts owing to ITE by the End User pursuant to the terms of this Agreement.

3.4 Deletion of Data and User ID. Within 30 days of expiry or termination of this Agreement for any reason ITE will have the right to delete the End User’s ID profile and all of the End User’s data (if any) stored on any storage systems of ITE or its subcontractor Transoft.

4.0 FEES, PAYMENT, AND INTEREST

4.1 Additional Services. Services outside of the scope of the description in this Agreement (the “Additional Services”) will not be included in the License Fee. Upon request for Additional Services by the End User, ITE will (or will request that its subcontractor Transoft) provide a quote for the cost for such Additional Services based on the hourly rates chargeable by ITE or Transoft, as the case may be, at the time of the request. ITE will perform the Additional Services on the written request of the End User, and the End User will pay for the Additional Services plus Value Added Taxes, if applicable within 30 days of receipt of an invoice for such Additional Services.

4.2 Interest. Any amounts not paid when due to ITE or Transoft, as the case may be, pursuant to the terms of this Agreement will bear interest at a rate of 24% per annum.

5.0 LIMITED WARRANTY AND DISCLAIMER

5.1 Limited Warranty. ITE (and by extension Transoft as the developer of the Software) warrants that the Software will perform substantially in accordance with the description set out at http://www.ite.org/tripgeneration/appdescription.pdf (the “Performance Description”). The End User will within 30 days of commencing use of the Software, give written notice to ITE of any perceived inconsistency with the Performance Description. ITE will use commercially reasonable efforts to correct any defects or deficiencies in the Software resulting in inconsistency with the Performance Description for which it has received notice in accordance with this paragraph 5.1. This limited warranty is void if a defect or deficiency has resulted from:

(a) use of the Software by the End User in any manner not contemplated in this Agreement;

(b) alteration, modification, or misuse of the Software by the End User or its agents or employees;

(i) malfunction of the End User’s equipment or operating system; or

(ii) software not developed by ITE in conjunction with its subcontractor Transoft.

5.2 Acknowledgement. The End User acknowledges and agrees that except for the limited warranty pursuant to paragraph 5.1, the Software is provided “as is” and each of ITE and Transoft makes no warranty, representation or guarantee, expressed implied or statutory, with respect to the Software whether as to the accuracy, reliability, suitability, function, absence of errors, or otherwise whatsoever and each of ITE and Transoft specifically disclaims any warranty of merchantability or fitness for a particular purpose.

5.3 Disclaimer. In no event will ITE or its affiliated companies, directors, employees, or contractors, including without limitation, or Transoft or its affiliated companies, directors, employees, or contractors (collectively the "Representatives") be liable for any damages arising from the End User’s use or inability to use the Software or for any loss or damage whether caused or alleged to be caused directly or indirectly by the Software including, but not limited to, any interruption of service, loss of business or anticipated profits, loss of goodwill, loss of data, computer failure, lost savings, or incidental, special, punitive or consequential damages resulting from the use or operation of the Software even if caused by the negligence of ITE, Transoft or any of the Representatives and even if ITE, Transoft or any of the Representatives had the knowledge of the possibility of such liability, loss, or damage. Notwithstanding the generality of the foregoing, any liability of ITE, Transoft and any of the Representatives is limited exclusively to the provisions of paragraph 5.1.

5.4 Limitation. Without limiting the generality of Sections 5.2 and 5.3, under no circumstances will ITE, Transoft or any of the Representatives become responsible for any costs, payments, claims or damages, other than to refund to the End User.

5.5 Access. Without limiting the generality of Section 5.3, the End User acknowledges and agrees that the Software may be unavailable from time to time as a result of scheduled and unscheduled maintenance or other circumstances beyond the control of ITE and Transoft.

5.6 Data. Without limiting the generality of Section 5.3, the End User acknowledges and agrees that the results produced by the Software is compiled from the ITE Data. Each of ITE and Transoft makes no representation as to the accuracy or reliability of the data or information produced by the Software. If at any time there is a discrepancy between the data produced by the Software and the ITE Data, the ITE Data will prevail.

6.0 CONFIDENTIALITY

6.1 Confidentiality. Each party agrees that, it will:

(a) keep the Disclosing Party’s Confidential Information in complete secrecy; and

(b) except with the written consent of the other party not use or disclose the Disclosing Party’s Confidential Information for any purpose.

Without limiting the generality of the foregoing, neither party will not use or attempt to use the Disclosing Party’s Confidential Information in any manner which may injure or cause loss either directly or indirectly to the Disclosing Party or its End Users or suppliers.

6.2 Survival. The provisions of this Article 6.0 will survive termination of this Agreement.

7.0 GENERAL

7.1 Notice. Any notice required or permitted to be given under this Agreement will be in writing, and be delivered to the address first above written or such other address as the parties may, from time to time, designate. Notice will be delivered by personal delivery, courier, registered mail, via facsimile transmission or via confirmed electronic mail. The delivery of a notice will be deemed effective upon receipt, if delivered personally or by courier, or five (5) business days from sending, if delivered by registered mail or the date of transmission, if delivered by facsimile or upon acknowledged receipt by the recipient if delivered by electronic mail.

7.2 Entire Agreement. This Agreement contains the entire agreement between the parties respecting the subject matter, and supersedes all other agreements whether written, or oral between the parties, it being expressly understood that there are no other representations, terms, warranties, conditions, guarantees, promises, agreements, collateral contracts or collateral agreements express or implied, or statutory, other than those contained in this Agreement and that this Agreement represents the whole of the Agreement between the parties, and no alteration, modification or amendment hereof will be binding unless made in writing and signed by the parties hereto.

7.3 Additional Acts. The parties will do such additional acts and execute and deliver such further documents as may be requisite to give full effect to the terms of this Agreement.

7.4 Severability. The invalidity of any particular portion, section or paragraph of this Agreement will not affect the validity of any other provision herein and, in such event, such invalid provision will be severable from this Agreement and the remainder of this Agreement will be construed as if such invalid provision was omitted.

7.5 No Waiver. No waiver by any party hereto of any breach of any covenant, representation, warranty, proviso, condition or stipulation herein contained whether express or implied or negative or positive in form by any other party hereto will have any effect or be binding upon any party hereto unless same will be in writing and under the authority of such party, and any waiver whatsoever will extend only to the particular breach so waived, and will not limit or affect the right of any party with respect to any other or further breach.

7.6 Governing Law. This Agreement shall be construed, governed, interpreted, and applied in accordance with the laws of the State of Delaware, without giving effect to the principles of conflict of laws. In the event that any action is filed in relation to this Agreement, the party which does not prevail in such action shall pay the reasonable attorneys’ fees and other costs and expenses, including investigation costs, incurred by the prevailing party in such proceedings.

7.7 Counterparts. This Agreement may be executed in any number of counterparts, each of which will be deemed to be an original, but all of which together will constitute one and the same document.

7.8 Gender. Whenever the singular or the masculine is used herein, same will be deemed to include reference to the plural, feminine and body corporate as necessary.

7.9 Binding Effect. This Agreement will ensure to the benefit of and be binding upon the parties hereto and their respective heirs, executors, administrators, successors and permitted assigns.

BEFORE YOU USE OR SIGN UP FOR THE USE OF ANY PORTION OF THE LICENSED PRODUCTS, YOU MUST ACCEPT THE TERMS AND CONDITIONS OF THIS AGREEMENT BY CLICKING “I AGREE” By communicating to ITE Your acceptance of this Agreement when You use or sign up for the use of the Licensed Products, You are entering into a legal and binding contract with ITE and agree to be bound by the terms and conditions of this Agreement. If you are entering into this Agreement on behalf of a company or other legal entity, your acceptance represents and warrants that you have the authority to bind such entity to these terms, in which case the words “End User”, “You”, or “Your” shall refer to such company or other legal entity. If you do not agree with this Agreement, or if you do not have the authority to bind your entity, then you, the “End User” and “You”, will not be authorized to use the Licensed Products.

(a) is already in possession of the Recipient or any of its parent, subsidiary or affiliated companies and was obtained without an obligation of confidence;

1.4 “Disclosing Party” means a party disclosing Confidential Information;

1.5 “Documentation” means any documentation (whether in electronic or printed or printed form) developed by Transoft and provided to or made available to the End User by ITE or through Transoft for the purposes of identifying the terms and conditions of access, use or operation of the Software, explaining or describing the Software, providing instructions as to the manner of permitted access or use of the Software, assisting the End User with problems or corrections to the Software or otherwise intended to assist the End User in the effective and permitted access, use and operation of the Software, and includes but is not limited to user guides, manuals, Help Menus and text, FAQ (frequently asked questions) files, license files, license specifications and details but documentation does not include the ITE Products which are provided by ITE to the End User by way of separate agreement or purchase..

1.6 “License Term” is the term of this Agreement referred to in Section 3.1;

1.7 “ITE Data” means the trip generation data that forms the basis of the TGM, and as it pertains to this agreement includes Edition 11th trip generation data.

1.8 “ITE Products” means the ITE Data, TGH, TGM and TGM plots

1.9 “Licensed Products” means the Software and the Documentation;

1.10 “License Fee” means the fee plus all applicable taxes payable by the End User to ITE as a condition of the End User’s use of the Licensed Products during the License Term.

1.11 “Login Details” means the details required to login and use the Licensed Products, such as a username and password;

1.12 “Recipient” means a party receiving Confidential Information;

1.13 “Software” means the online web application known as “ ITETripGen ” that will interface with ITE Data, be capable of supporting three databases (being all United States and Canada data, United States data only, and Canadian data only) and include features to look up specific data in either imperial or metric units, plots of vehicle and person trips in two-dimensional coloured graphs, and export such graphic plots in high-quality PDF ;

1.14 “TGH” means the Trip Generation Handbook, whether electronic or hardcopy, published by ITE from time to time which is currently in its 3rd Edition providing guidance on proper techniques for estimating person and vehicular trip generation rates; guidance for the evaluation of mixed use developments and the establishment of local trip generation rates; and pass-by trip and truck trip generation data.

1.15 “TGM” means the Trip Generation Manual , whether electronic or hardcopy, published by ITE from time to time and which is currently in its 10th Edition (including the 10th Edition Supplement), including land use descriptions, trip generation rates, equations and data plots and which are prepared, gathered, assembled and formatted by or on behalf of ITE from time to time for reference and use by transportation professionals conducting site impact studies, determining on-site circulation patterns, performing access management studies, determining traffic signal timing, conducting environmental assessments and other transportation related uses and activities;

1.16 “TGM plots” refers to the full set of data plots for all land uses distributed, published by ITE in electronic format. Subsets of the TGM plots, referred to as “Land Use Packages” are also published by ITE in electronic format.

1.17 “Transoft” means Transoft Solutions Inc. 350 - 13700 International Place, Richmond, BC, Canada V6V 2X8, the owner and developer of the Software and the Documentation.

1.18 “Value Added Taxes” means such sum as will be levied upon the License Fee or any other fees payable pursuant to this Agreement by the Federal or any Provincial or Territorial Government and is computed as a percentage of the fees and includes Goods and Services Tax, Harmonized Sales Tax and any similar tax, the payment or collection of which, by the legislation imposing such tax, is an obligation of ITE.

2.1 Software and Services. Pursuant to ITE’s license agreement with Transoft, ITE grants by way of a limited sub-license to the End User, subject to the terms and conditions of this Agreement the non-exclusive, non-transferable, non-assignable and non-sublicensable right to use the Licensed Products during the Term for the purpose of its business. ITE, through its subcontractor Transoft will host the Software, including providing data support, backup, and recovery and access to the Documentation. ITE will provide to you the ITE Products by way of separate agreement and purchase arrangements directly with ITE.

(b) modify, adapt, translate or alter them or otherwise create derivative works;

2.5 Restrictions. Notwithstanding the generality of this Agreement, the End User will not, and will not permit others to, transmit, convey, license, sublicense, distribute, sell, resell, transfer or otherwise dispose of the Licensed Products or the ITE Products to any other persons or organizations. The End User further agrees that the Software will not be accessed or used in any manner prohibited by the United States Export Administration Act or any other United States laws or any applicable national or international export laws, restrictions or regulations (collectively, the “Export Laws”). In addition, if the Software is identified as an export controlled item under the Export Laws, the End User represents and warrants that it is not a citizen of, or located within, an embargoed or otherwise restricted nation and that the End User is not otherwise prohibited under the Export Laws from receiving the Software. All rights to use the Software or the ITE Products are granted on the condition that such rights are forfeited if You fail to comply with the terms of this Agreement.

3.1 Term. The term of this Agreement will commence on the Commencement Date, and will remain in effect until December 31, 2025. Existing TGM 10th Edition users will have access extended to December 31, 2022.

(a) the End User will immediately cease using the Licensed Products, the ITE Products and the sub-license herein and all rights to use the Licensed Products and ITE Products will expire; and

4.1 Additional Services. Services outside of the scope of the description in this Agreement (the “Additional Services”) will not be included in the License Fee. Upon request for Additional Services by the End User, ITE will (or will request that its subcontractor Transoft) provide a quote for the cost for such Additional Services based on the hourly rates chargeable by ITE or Transoft, as the case may be, at the time of the request. If ITE accepts the End User’s request to perform Additional Services, ITE will perform the Additional Services and the End User will pay for the Additional Services plus Value Added Taxes, if applicable within 30 days of receipt of an invoice for such Additional Services.

7.1 Notice. Any notice required or permitted to be given under this Agreement will be in writing, and be delivered to the address first above written or such other address as the parties may, from time to time, designate. Notice will be delivered by personal delivery, courier, registered mail, via confirmed facsimile transmission or via confirmed electronic mail. The delivery of a notice will be deemed effective: (i) upon receipt, if delivered personally or by courier; (ii) or five (5) business days from sending, if delivered by registered mail; (iii) or the date of transmission, if delivered by facsimile or upon acknowledged receipt by the recipient if delivered by electronic mail and a confirmation copy is sent by first class mail.

7.6 Governing Law. This Agreement shall be construed, governed, interpreted, and applied in accordance with the laws of the District of Columbia, without giving effect to the principles of conflict of laws. Any dispute relating to this Agreement shall be resolved in the municipal and federal courts serving the District of Columbia. and each party hereto waives any objection to venue and hereby submits to the personal jurisdiction of such courts.

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If you have any questions related to the purchase of the Trip Generation Manual, 11th Edition:

Contact Person: Sallie Dollins Institute of Transportation Engineers 1627 Eye Street, NW, Suite 600 Washington DC 20006 USA Phone: 202-785-0060 (149) Fax: 202-785-0609 Email: [email protected]

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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 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

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IMAGES

Trip generation multiple regression equations [trips/day]
PPT
Trip generation multiple regression equations [trips/day]
Solved Problem 1: Trip Generation (25 marks) Multiple linear
Trip Generation
Lecture 02 Trip Generation and Trip Distribution

VIDEO

Lec 01 Project "Route Selection and Trip Generation"
Numerical Based on Trip Generation
1- Transportation Planning
1- Transportation Planning
TTE332 Lec3_S21: Trip Generation Step
Trip Generation 1

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. ... or equations. First, we need to ...
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 ...
10 First Step of Four Step Modeling (Trip Generation)
The previous chapter introduces the four-step travel demand model (FSM), provides a real-world application, and outlines the data required to carry out each of the model steps. Chapter 10 focuses on the first step of the FSM, which is trip generation. This step involves predicting the total number of trips generated by each zone in a study area ...
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 ...
11 Second Step of Four Step Modeling (Trip Distribution)
Figure 11.4 Schematic Orientation of Trip Generation zone and Trip Attracting Zones. socioeconomic adj. Factor K=1.0 Calibration factor C=2.0; Solution. As the first step, we need to calculate the friction factor for each pair of zones based on travel time (t). Given is the following formula with which we calculate friction factor:
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.
Trip Generation Analysis
Trip Generation Analysis. The following excerpt was taken from the Transportation Planning Handbook published in 1992 by the Institute of Transportation Engineers (pp. 108-112). ... Trip generation equations developed by regression are still used by some planning agencies, more commonly for attraction models than for production models. ...
Trip Generation
Trip Generation Introduction. Trip generation is the first step in the traditional, sequential four step process of transportation modeling. It establishes a relationship between land use, socioeconomic and demographic data and trip productions and attractions.
Comparative analysis of trip generation models: results using home
Regression analysis in trip generation functionalizes the relationship between trip generation rates, or the dependent variable, and a set of independent variables (1) where is the trip generation rate, n indexes the nth observation (which is households in this study), β is the vector of parameters that should be estimated, x is the vector of ...
A comprehensive review of trip generation models based on land use
Trip generation modelling has traditionally relied largely upon trip diaries, intercept surveys, and household interview surveys to obtain data about mobility patterns. ... However, Structural equation modelling approaches are capable of simultaneously exploring the complex relationships among the observed variables based on the predefined mode ...
Trip generation in Transport Planning
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 ...
Trip Generation Manual, 9th Edition, Volumes 1, 2 and 3
Trip Generation Manual, 9th Edition, Volumes 1, 2 and 3. This multi-volume manual presents a summary of the trip generation data that have been voluntarily collected and submitted to the Institute of Transportation Engineers (ITE). This is the ninth edition and includes data from the previous eight editions as well as the supplementary ...
PDF Trip Generation
Since trip productions and attractions are calculated independently of each other, the total numbers will likely be different. May get 10,000 HBO productions and 9,000 HBO attractions. Most of the time will want to balance to productions (household estimates are more reliable than commercial land use estimates) To balance to productions, will ...
PDF Trip Generation
Curve Equations Per ITE Trip Generation Manual Handbook 3rd Edition (Figure 4.2) 20 or more data points, Fitted Curve Equation Five or less data points, collect local data Additional data with every new edition of ITE Trip Generation Previously accepted practices could be revisited
Trip Generation: Trip Generation Rates, Plots, and Equations
The sixth edition of Trip generation includes several significant changes in format and content as compared to the fifth edition. To facilitate use of the document, the overall publication has been repackaged into three volumes: Volumes 1 and 2, containing land use descriptions and data plots, and a User's guide, containing the general introductory, instructional and appendix material.
Trip and Parking Generation
This new edition of the Trip Generation Manual enhances the 10th edition's modernized content, data set, and contemporary delivery ... land use characteristics, independent variable and time period. Then, choose to apply the average rate or fitted curve equation and view the results in Vistro's trip generation table and on the ITE formatted ...
Trip Generation
Trip Generation is the first step in the Sequential Demand Modelling arrangement which is also called as the Four Step Transportation Planning Process(FSTP) as mentioned earlier. In order to carry out modelling, the variable consists of total number of person-trips generated by a zone as a dependent variable and the independent variable ...
TRIP GENERATION
TRIP GENERATION. This is the sixth edition of the Institute of Transportation Engineers' trip generation rates, plots, and equations. It is presented in three volumes. Volume 1 is organized according to the following land uses: Port and Terminal; Industrial/Agricultural; Residential; Lodging; and Recreational.
PDF Single-Family Attached Housing (215)
Walk Trip Generation per Dwelling Unit Average Rate Range of Rates Standard Deviation 0.16 0.08 - 0.29 0.08 Data Plot and Equation 0 100 200 300 0 10 20 30 40 Study Site Fitted Curve Average Rate Fitted Curve Equation: Ln(T) = 0.65 Ln(X) - 0.32 R²= 0.70 X = Number of Dwelling Units T = Trips Ends
ITETripGen Web-based App
1.15 "TGM" means the Trip Generation Manual, whether electronic or hardcopy, published by ITE from time to time and which is currently in its 10th Edition (including the 10th Edition Supplement), including land use descriptions, trip generation rates, equations and data plots and which are prepared, gathered, assembled and formatted by or ...
Trip Generation: Trip Generation Rates, Plots, and Equations
The sixth edition of Trip generation includes several significant changes in format and content as compared to the fifth edition. To facilitate use of the document, the overall publication has been repackaged into three volumes: Volumes 1 and 2, containing land use descriptions and data plots, and a User's guide, containing the general introductory, instructional and appendix material.
Trip Generation
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.
PDF Trip Generation Worksheet
Use Regression Equation Use Weighted Average Rate Contact Traffic Staff Contact Traffic Staff No No 1 or 2 3-5 6+ 3-5 Yes Yes No Yes No No Yes No Yes Yes From ITE Trip Generation Handbook City of McAllen Traffic Operations Division 210 N. 20th St. McAllen, TX 78501-0220 P.O. Box 220 McAllen, TX 78501 (956) 688-3420 (956) 688-3432 (fax)
Greenhouse Gas Emissions Standards for Heavy-Duty Vehicles-Phase 3
For example, Daimler Truck North America is partnering with electric power generation company NextEra Energy Resources and BlackRock Renewable Power to collectively invest $650 million to create a nationwide U.S. charging network for commercial vehicles with a later phase of the project also supporting hydrogen fueling stations.