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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?

• \(T_{O,i}\) - 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

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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.

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

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Part III: Travel Demand Modeling

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

Student Learning Outcomes

• 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.

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 = 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 .

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 .

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.

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):

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 =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:

•  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:

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.

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

Trip generation is traditionally the first step in 4-step travel models. Trip generation estimates for an individual traffic analysis zone (TAZs) the number of trip ends produced or attracted by that zone. Trip ends produced are called "trip productions", and trip ends attracted are called "trip attractions". A trip is produced where the need (or desire) for travel is located; and a trip is attracted where the need (or desire) is satisfied. A trip production can be either an origin or a destination, and a trip attraction can be either an origin or a destination. Productions and attractions are separately tabulated for each trip purpose (e.g., HBW, HBO, NHB).

One of the more popular methods of trip production calculation is cross-classification. Cross-classification is described in NCHRP Report 716, among many sources. A popular method of trip attraction calculation is by linear equations. Neither of these two methods use measures of mobility or accessibility . Inputs consists entirely of forecasted socioeconomic or demographic characteristics of zones.

A subsequent step, trip distribution , very often requires that the total of all productions equal the total of all attractions for each trip purpose. Consequently, an important part of trip generation is "balancing", where inconsistencies between productions and attractions are reconciled. Consistency can be achieved by holding productions constant and varying attractions or vice versa.

← Activity Based Models Trip distribution →

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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 distribution

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Trip distribution is another of the major aspects of the transportation simulation process and although generation, distribution and assignment are often discussed separately, it is important to realise that if human behaviour is to be effectively simulated then these three processes must be conceived as an interrelated whole.

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Salter, R.J. (1989). Trip distribution. In: Highway Traffic Analysis and Design. Palgrave, London. https://doi.org/10.1007/978-1-349-20014-6_7

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The term “field trip” has been known for decades in many sectors and it is a common term used in worldwide schools. It seems that a field trip is a favorite part of both teachers and students who are keen on learning and discovering. So, what is a field trip in education? Scroll down to find out the field trip definition and its many types.

Educational Field Trip Definition

A field trip or excursion is a journey taken by a group of people to a place away from their usual environment. In education, field trips are defined as visits to an outside area of the normal classroom and made by a teacher and students for purposes of firsthand observation. A field trip can be expressed in many terminologies. People call educational trips or school tours in the UK and New Zealand, and school tours in the Philippines. Field trips are a popular method carried out for students to introduce to the concepts, experiences, and ideas that cannot be given in a classroom environment. School tours can be considered as short-term learning activities providing students the opportunity to observe their chosen subject outside of a classroom setting. Exploring other cultures and customs, getting to the motherland of languages, uncovering pristine nature and experiencing fascinating local life are striking demonstrations of educational school trips

Types of Field Trips

Those listed field trip ideas that help to clear field trip meaning. Efficient educational tours can spark students’ imagination, give them valuable experiences and refresh their minds after days with pencils and papers. A school tour can be themed with one type of field trip or combined by various school trip ideas.

Sightseeing Field Trip

Students are definitely eager the most to sightseeing school trips enchanting them by a myriad of appealing attractions in their wish destination. Admire well-known attractions, explore historic structures, discover World Heritage Sites, unwind on spectacular landscapes and freshen in front of scenic vista are incredible activities that gain huge interests from students and strongly inspire them.

Language and Culture Educational Field Trip

For students learning foreign languages, field trips are very important and helpful to improve the language and explore the alluring indigenous culture. Join immersive activities, stay at a local homestay, take language lessons and visit local markets enable students to practice the language, get a deeper understanding of local culture and their captivating paces of life.

Gardening and Farming Field Trip

This might be an interesting activity attracts lots of students’ attention thanks to its strangeness to their usual life. Discover specialty farms that grow the normal crop and even irregular crops will surprise curious students. Learn how vegetables are produced, explore and give a try to do traditional farming techniques of local people leaves memorable experiences for students.

Manufacturing Facility Field Trip

Students can be guided to any factory where equipment, cars, tools, packaging or any other things are made. The mechanized facilities and assembly lines are interesting for students to learn about the production process, how raw materials are utilized and how workers use them to make the final product.

Eco-adventure Field Trip

Discover the natural world is a highly important perspective in the educational sector. Students can be entertained and refreshed by trekking through untouched natural beauties to inspect local plant life and wildlife animals. This opportunity also adds to local historical factors such as early life remnants.

Business Educational Tour

Take business study trips, your students will be delighted by bustling financial and business centers. Business study trips help process business theories in the classroom into life as students explore great commercial organizations. Business field trip gives students the chance to immerse in stimulating and dynamic environments. Visit a range of famed organizations and large corporations will perfect business school trips.

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Experiential learning & field trips at yale.

Experiential learning is a holistic learning model based on an integrative process where students first obtain knowledge, then perform an activity (generally with some “real-world” application), and finally reflect on the experience (Kolb 1984), often iteratively. Whereas study abroad programming has reduced its scope across American universities in recent years,  studies show that greater depth, breadth, and progressive iteration prove especially fruitful for student learning and skills growth (Coker, et. al, 2017).

“Experiential” can refer to any learning where instructors guide students to apply conceptual knowledge in actual problems or situations. Classic scenarios include activities and experiences held outside of class and/or off campus: service learning in order to better understand the course content or the methods of the discipline; fieldwork conducting research or practice at an off-campus site in direct contact with the entities or phenomena being studied; community-based research in cooperation with local nonprofits to conduct studies to meet the needs of a particular community; and clinical learning. But, it also includes more conventional active learning techniques used in the classroom, such as problem solving, simulations, case studies, peer-to-peer teaching, and material study. Coker, et. al. (2017) also include internships and leadership, given their real world pressures and cycle of experience, reflection, and action.

Field Trips

When constructed to include reflection, conceptualization, and activity, field trips can provide incredibly formative and impactful educational experiences for students. Yale professors typically plan field trips in three main forms:

• on-campus class outings, e.g. a visit to Yale University Art Gallery , Center for British Art , Beinecke Rare Book and Manuscript Library, Peabody Museum of Natural History , the Babylonian Collection , or a walk around New Haven
• optional off-campus activities, e.g. trip to New York City or Boston (both about 2-hour drives)
• required off-campus outings (including semi-optional but formally class-organized trips, such as Spring Break trips abroad led by the instructor)

The third category is strictly regulated by Yale College’s guidelines governing field trips: the Yale College Dean’s office defines an “academic field trip” as a “course-related activity that serves educational purposes and occurs outside of the classroom at a location other than on the campus at which the course is regularly taught.” In addition, if a course is regularly taught outside of a classroom or at locations away from campus (as in the case of fieldwork courses), these same recommended guidelines apply. Instructors planning academic field trips along the terms of the third category should contact Dean George Levesque, 203-432-2920.

Experiential learning in the form of field trips is not only pedagogically promising—it is also very popular among students. There is a general Yale student consensus that funded field trips make classes more attractive, and Yale administrators suggest considering October and Spring Break for class field trips. Numerous classes have boosted their enrollment numbers by optioning an international field trip, while others boost student motivation by reserving trip applications to students with an A average.

Other experiential learning opportunities include:

• A STEM undergraduate student conducts laboratory research for course credit.
• A geology class takes a field trip to Costa Rica to study volcanic processes.
• A foreign language class includes a home-stay component and required participation in other cultural activities.
• A political science class includes a research assignment requiring students to interview the city’s local aldermen.
• A refugee law class visits a refugee legal aid clinic, and students design projects to help meet the needs of the non-profit organization.

For more examples of field trips and other experiential learning activities, instructors can peruse Harvard’s “ Activity Database ,” a compendium of activity-based learning activities recently conducted in undergraduate courses.

Recommendations

• Follow Policy - Instructors should review Yale College’s Academic Field Trip Policies .
• Register - Instructors should review Yale’s International Toolkit and register your trip even if domestic (so that Yale can provide aid in the event of an emergency in a particular city).
• Scale Up - First time field-trip instructors might start with simple outings which fall under the first two categories discussed above. Instructors may also ask their departments for examples of successful field trips led by colleagues.
• Scale Down - Instructors should make any costs associated with these outings clear at the start of the course, verbally and in writing in the syllabus. They may also work to minimize costs to students, and avoid revising or changing costs, unless to reduce, wherever possible.
• Ask for Funding - Because funding for field trips is hard to come by (there is no central Yale funding for field trips), departments are the best sources of funding. Additionally, the CTL’s Faculty Teaching Initiatives offers $500 Instructional Enhancement Funds on a first come -first serve, competitive application basis. Funding for the program is replenished annually, at the beginning of the fall term.
• Consider Transport - Public transportation is encouraged, and transportation must meet Yale’s field trip transportation guidelines . For private transportation outside of Yale, Yale Transportation often recommends Dattco for instructors wishing to book vans, shuttles, and coach buses.
• Accessibility Awareness - Instructors considering travel or experiences including bodily interaction should be aware of student accessibility concerns, and provide dynamic policies to support students with travel restrictions and mobility disability.

Coker, J., Heiser, E., Taylor, L., & Book, C. (2017). Impacts of Experiential Learning Depth and Breadth on Student Outcomes. Journal of Experiential Education 40.1: 5-23.

Kolb, D. A. (1984).  Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall.

Moore, D. T. (2010). Forms and issues in experiential learning. In D. M. Qualters (Ed.) New Directions for Teaching and Learning (pp. 3-13). New York City, NY: Wiley.

Wurdinger, D. D., & Carlson, J. A. (2010).  Teaching for experiential learning:  Five approaches that work.  Lanham, MD: Rowman & Littlefield Education.

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Definition of field trip

Examples of field trip in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'field trip.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1926, in the meaning defined above

Dictionary Entries Near field trip

field trial

Cite this Entry

“Field trip.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/field%20trip. Accessed 7 Apr. 2024.

Kids Definition

Kids definition of field trip, more from merriam-webster on field trip.

Thesaurus: All synonyms and antonyms for field trip

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