how to calculate trip generation rates

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3.4: Trip Generation

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• Page ID 47326

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

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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?

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10 First Step of Four Step Modeling (Trip Generation)

Chapter overview.

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 and the trips attracted to each zone based on their specific purpose. The chapter delves deeper into this process, providing detailed insights into the factors influencing trip generation and how they can inform transportation planning decisions. Trip generation is a function of land use, accessibility, and socioeconomic factors, such as income, race, and vehicle ownership. This chapter also illustrates how to incorporate these inputs to estimate trips generated from and attracted to each zone using regression methods, cross-classification models (tables), and rates based on activity units as specified by the Institute of Transportation Engineers (ITE). It also provides examples to demonstrate the model applications.

The essential concepts and techniques for this step, such as growth factors and calibration methods, are also discussed in this chapter.

Learning Objectives

• Explain what trip generation is and summarize what factors contribute to trip generation.
• Recognize the data components needed for trip generation estimation and ways to prepare them for estimation.
• Summarize and compare different methods for conducting trip generation estimation and ways to interpret their results

Introduction

The Four-Step Model (FSM) is comprised of four consecutive steps, each addressing a specific question, ultimately contributing to an enhanced comprehension of travel demand. The questions are:

• Trip generation (Chapter 10) – How many total trips are estimated? What is the demand (total trips)?
• Trip distribution (Chapter 11) – Where are the trip destinations? What are the destinations of the trips?
• Modal split (Chapter 12) – What modes are used to complete those trips?
• Trip assignment (Chapter 13): What routes will be selected to complete the trips? (Meyer, 2016).

Figure 10.1 shows how the model is structured. It shows what kinds of data we provide as input for the model, and what steps we take to generate outputs.

Key Concepts

Link-diverted trips: Trips produced as a result of congestion near the generator and require a diversion; new traffic will be added to the streets adjacent to the site. In other words, these are trips with multiple destinations within one area and do not require road access between destinations.

Diverted trips:  Travel changes in time and route are known as diverted trips. For example, when a trip is diverted or re-routed from the original travel path due to the traffic on nearby roadways, new traffic on surrounding streets results, but the trip attraction remains the same.

Pass-by trips (see below) do not include link-diverted trips.

Pass-by trips: This type of trip is described as a trip for which the destination is not a final but a stop along the way by using the connecting roads. Passing-by traffic volume in a zone depends on the type and size of development or available activities.  A gas station with higher prices near an employment center may receive many pass-by trips for gas compared to other gas stations (Where up to 50 % of all trips to a service station are travelers passing by rather than people who made a special trip to the gas station)

A gas station located in close proximity to an employment center and charging higher prices might experience a higher number of pass-by trips for gas, in contrast to other gas stations. It is observed that up to 50% of all trips to a service station are by travelers passing by, rather than individuals specifically making a deliberate trip to that gas station.  (Meyer, 2016).

Traditional FSM Zonal Analysis   : After inputting the required data for the model, FSM calculates the number of trips generated by or attracted to each zone using the primary input using data from travel surveys from census data. While one limitation of the trip generation model is reduced accuracy due to aggregated data, the model offers a straightforward and easily accessible set of data requirements. Typically, by utilizing basic socio-economic information like population, job figures, vehicle availability, income, and similar metrics, one can calculate trip generation and distribution.

 Activity-based Analysis: There are also other (newer) methods for travel demand modeling in which individual trips are modeled based on individuals’ behaviors and activities in a disaggregated manner. The methods that use activity-based models can estimate travel demand based on a basic premise—the demand to accomplish personal activities during the day (for example, work, school, personal business, and so forth) produces a demand for travel that is often connected (Glickman et al., 2015). However, activity-based models have extensive data requirements as individuals, rather than traffic analysis zones, are the unit of analysis. Detailed information on each individual’s daily activity and socioeconomic information is needed.

Travel diaries (tours) are one source of such information (Ettema et al., 1996; Malayath & Verma, 2013). Because of travel demand modeling, additional information can be learned about the study area. For example, the detailed data may reveal information about areas with or without minimum accessibility, underserved populations, transportation inequity, or congested corridors (Park et al., 2020).

Several scholars have compared the two models – traditional zonal models and activity-based models – to assess factors such as forecasting ability, accuracy, and policy sensitivity. Despite initial expectations, the findings from some studies show no improvement in the accuracy of activity-based models over traditional models (Ferdous et al., 2011). However, considering the complexity of decision-making, activity-based models can be used to minimize the unrealistic assumptions and aggregation bias inherent in FSM models. Still, the applicability and accuracy of activity-based models should be independently assessed for each context analysis to determine which is the most effective approach.

In transportation analysis, trips are typically classified based on the origin (O)and destination (D) location. As mentioned in previous sections, for a more accurate and better estimation of trip generation results, it would be better to identify a wide range of trip categories and have disaggregate results by trip purposes. The following lists typical trip classifications:

• Home-based work (HBW) : If one of the trip origins is home and the destination is the workplace, then we can define the trip purpose as home-based work (HBW). These trips usually happen in the morning (to work) and in the evening (from work to home).
• Home-based non-work (HBNW) : If from the two ends of the trips, one is home and the other one is not workplace, the trip purpose is home-based-non-work (HBNW). Sometimes this trip purpose is called home-se is called home-based other ( HBO ). Examples of these are going to services like a restaurant or hospital.
• Non-home-based (NHB) : If neither the origin nor the destination is home, we can classify the trip as a non-home-based (NHB) purpose. One typical example is a lunch break trip from the workplace to a shopping mall.

While the above categories include only one origin and one destination, most individual trips are more complex due to chaining different trips into one tour. For instance, a person may stop for coffee or drop their child at daycare on the way to work, leave on lunch break for shopping, and then pick up their child from daycare on the way home. A tour is a continuous chain of trips an individual takes daily to complete their chores, which activity-based models can simulate (Ben-Akiva & Bowman, 1998).  Figure 10.2 illustrates the different trip purposes and differences between FSM and activity-based models in trip classification.

It is important to note that home-based trips can be work, school, shopping, recreational, and others. While the first two are usually mandatory and made daily, the rest are less regular or discretionary.

Trips can also be classified based on the time of day that they are generated or attracted, as traffic volumes on various corridors vary throughout the day. Essentially, the proportion of different trip purposes in the total trips is more pronounced during specific times of the day, usually categorized as peak and off-peak hours (Alkaissi, 2021).

Lastly, another factor to consider is the socio-economic characteristics and behaviors of the trip makers. An understanding of these factors is crucial for classifying trips, as some possess significant influence on travel behavior (Giuliano, 2003; Jahanshahi et al., 2009; Mauch & Taylor, 1997), such as, income level, car ownership, and household size.

Trip generation

Recall from the previous chapter, a comprehensive analysis of travel demand should include trip generation and attractions for different zones. These values should be balanced to produce an equal number of trips. In general, trip generation helps predict the number of trips for different purposes generated by and attracted to every zone in a study area.

Additionally, the number of trip ends – the total number of trips entering and leaving a specific land use or site over a designated period – can be calculated in the trip generation step (New Jersey Transit, 1994). Despite recent trends for remote work, most people do not live and work in the same area. Daily round trips to work or shopping centers originate from different locations. In this regard, the distribution of activities, like job centers, can help us to understand daily travel patterns (Wang & Hofe, 2020).

After generating an overview of the distribution of activities and land uses, we must identify the factors or conditions affectingtripgeneration. Over the years, studieshaveexaminedfactorsthatarenow accepted as standard:income,autoownership,familysize,ordensity(Ewingetal.,1996;Sharpeetal.,1958).Using a zonal level analysis, population, number of jobs, and availability of modes can affect trip generation (Wang&Hofe,2020).Similarly,thetypeandsizeofretailstores canalsoaffectthenumberoftrips.

Additionally, the predominant travel mode chosen by the population for their daily trips is a vital factor to consider. Because of the interconnectedness of land use and transportation, the primary mode influences the distribution of services, employment centers, and the overall structure and boundaries of the city. In summary, the type and intensity of land use in combination with transportation mode play crucial roles in trip generation.

The table below shows 5 hypothetical cities where the predominant mode of transportation is different for each case. According to the speed of each mode, the extent to which activities are dispersed, determines the size of the city. For instance, a city where rail is the frequent mode of transportation, the speed (21 mph) and travel time (43 mins), the catchment (distance) would be 12 miles. Using this distance as a radius, we can estimate the size of the city.

Table 10.1 Hypothetical cities with different transportation modes

According to the discussion here, the following categories can be identified as contributors to trip generation (McNally, 2007).

• Land-use types
• Land-use Intensity
• Location/accessibility
• Travel time
• Travel mode (transit, auto, walking …)
• Households’ income level
• Auto ownership rate
• Workers per household

Trip Generation Calibration

Traffic Analysis Zones (TAZs) connected by transportation networks and facilities are used to model the study area. TAZs are the smallest units of analysis in FSM. They are typically bounded by transportation networks or natural boundaries such as rivers.

Prior to estimating trip generators and attractions, calibrate the model as follows:

• Determine the regional population and the employment rate for the forecasting year to estimate the total number of interactions and possible future patterns.
• Allocate population and economic activities to each TAZ to prepare the study area for the modeling framework.
• Specify the significant variables and a proper method for creating the travel demand model (trip generation step). This step can be called model specification.

Calibration is an essential process in travel demand modeling. It involves collecting actual traffic flow data and calculating model parameters to verify the accuracy of the model for a specific region. The purpose of calibration is to match predicted outcomes with observed data, ensuring that model results are reliable and trustworthy (Wang & Hofe, 2020).

FSM MODELING UNITS

As discussed previously, the unit of analysis used for the model varies by model type.  The unit of analysis is important as it guides data collection. Traditional zonal analysis, like FSM, typically uses TAZs.  Activity-based models typically use data at the level of the individual person or household. There are three general methods for trip generation estimations:

1.     Growth factor model,

2.     regression methods,

3.     cross-classification models (tables),

4.     and rates based on activity units (ITE).

Generally, the trip generation step requires two types of data – household-based and zonal-based. Household-based data is more suitable for cross-classification analysis , and zonal-based data is more applicable for regression method analysis (the following sections will discuss these methods).

The third method is based on rates by which each land use type generates trips. The very general process for this method is identifying land use types, estimating trip generation according to ITE manuals, calculate total generation, and finally modifying based on specific characteristics such as proximity or location of land use. In this chapter, we do not wish to illustrate the third model, instead we focus on regression and cross-classification models since they are more data-oriented methods, more realistic and more frequently used in real-world.

The zonal analysis consists of areas divided into smaller units (zones), from which an estimate of trips generated in each zone is obtained (aggregate model). Household-based analysis decomposes zones into smaller units based on households with similar characteristics. In transportation travel demand modeling, we estimate zonal trips for various purposes, such as work, school, shopping, and social or recreational trips. As said, a zone is an area with homogeneous characteristics of land use, population, income, vehicle ownership, and the same access path outside of the zone.

In many cases, however, sufficient data at this resolution is unavailable (available at Census Tracts, Blocks, and Block Groups). In these conditions, the modeler should assess if the lower-resolution data is sufficient for their purpose. If not, using appropriate GIS-based data conversion methods, the data from a higher level (such as Census Tract) can be migrated to lower-level units (such as TAZ).

GROWTH FACTOR MODELING

A straightforward approach for estimating future trip generation volumes is to translate trends from the past into the future based on a linear growth trend of effective factors such as population or income. This method projects past data into the future by assuming a constant growth rate between two historical points. We can use this method when trip production and attraction in the base year are available, but the cost function (like travel time) is not. While this method is commonly used, it is important to note that it is insensitive to the distance between zones, which affects the estimated future data (Meyer, 2016).

In this model, the future number of trips equals the number of current trips times the growth factor.

Equation below is the method’s mathematical format:

T i is the number of trips in the zone in the forecasting year

t i is the current number of trips in that zone

f i is a growth factor

The growth factor itself consists of a number of explanatory variables that we acknowledge have impact on trip generation such as population, income (I), and ownership (V). To calculate a single growth factor with all these variables, the below equation is useful:

P i d is the population in the design year

P i c is the population in the current year

I i d is the income level in the design year

I i c is the income level in the current year

V i d is the vehicle ownership rate in the design year

V i c is the vehicle ownership rate in the current year

In a small neighborhood, 630 households reside, out of which 300 households have cars and 330 are without cars. Assuming population and income remain constant, and all households have one car in the forecasting year, calculate the total trips generated in the forecasting year and the growth factor (trip generation rate for 1-car: 2.8; 0-car:1.1). Assume that a zone has 275 households with cars and 275 without cars, and the average trip generation rates for the two groups are 5.0 and 2.5 trips per day.

Assuming all households will have a car in the future, find the growth factor and the future generated trips from that zone, keeping population and income constant.

• Current trip rate ti=300 × 2.8 + 330 × 1.1 = ? (Trips/day)
• Growth factor Fi=Vdi/Vc =630/300= ?
• Number of future trips Ti = Fiti = 2.1 × 1203 = ? (Trips / day)

Regression Analysis

Regression analysis begins with the classification of populations or zones using the socio-economic data of different groups (like low-income, middle-income, and high-income households). Trip generation can be calculated for each category and the total generated trips by each socio-economic group such as income groups and auto ownership groups using linear regression modeling. The reason for disaggregating different trip making groups is that as we discussed, travel behavior can significantly vary based on income, vehicle availability and other capabilities. Thus, in order to generate accurate trip generations using linear models such as OLS (Ordinary Least Squares) regression, we have to develop different models with different trip making rates and multipliers for different groups. This classification is also employed in cross-classification models, which is discussed next. While the initial process for regression analysis is similar to cross-classification models, one should not confuse the two methods, as the regression models attempts to fit the data to a linear model to estimate trip generation, while cross-classification disaggregates the study area based on characteristics using curves and then attributes trips to each group without building predictive models.

Alternatively, the number of total trips attracted to each zone would be determined using regression analysis on employment data and land-use attraction rates. The coefficients for the prediction model in linear regression analysis can be derived. The prediction model has a zone’s trip production or attraction as a dependent variable, and independent variables are socio-economic data aggregated by zone. Below, we illustrate a general formula for the regression type analysis:

Trip Production= f (median family income, residential density, mean number of automobiles per household)

The estimation method in this regression analysis is OLS (Ordinary Least Squares). After zonal variable data for the entire study area are collected, linear regression analysis is applied to derive the coefficients for the prediction model. A major shortcoming associated with this model is that aggregate data may not reflect the precise effect of data on trip production. For instance, individuals in two zones with an identical vehicle ownership rate may have very different access levels to private cars, thus having different trip productions. The cross-classification model described in the next section helps address this limitation (McNally, 2007).

Equation below shows the typical mathematical format of the trip generation regression model:

where X i is the independent variable and a i is the associated coefficient.

In a residential zone, trip production is assumed to be explained by the vehicle ownership rate of households. For each household type, the trip-making rates are shown in Table 10.2). Using this information, derive a fitted line. Table 10.2 documents 12 data points. Each corresponds to one family and the number of trips per day. For instance, for a 1-vehicle family, we have (1,1) (1,3), and (1,4).

Table 10.2 Sample vehicle ownership data for trip generation

The linear equation will have the form: y = bx + a. Where: y is the trip rate, and x is the household vehicle ownership, and a and b are the coefficients. For a best fit, b is given by the equation:

Based on the input table, we have:

Σx = 3 × 1 + 3 × 1 + 3 × 3 + 3 × 3 = 24 Σx2 = 3 × (1 2 ) + 3 × (2 2 ) + 3 × (3 2 ) + 3 × (4 2 ) = 90 Σy = 8 + 14 + 21 + 28 = 71 Σxy = 1 × 1 + 1 × 1 + 1 × 3 + 1 × 3 + 2 × 2 + 2 × 3 + 2 × 4 + 2 × 5 + 3 × 5 + 3 × 4 + 3 × 5 + 3 × 7 + 4 × 7 + 4 × 5 + 4 × 8 + 4 × 8 = 211

y‾ = 71/12 = 5.91 x‾ = 30/12 = 2.5 b = (nΣxy − ΣxΣy)/[(nΣx 2 − (Σx) 2 ] =((16 × 211) − (24 × 71))/((16 × 90) − (24) 2 ) = 1.93 a = y‾ − b x‾ = 17.75 – 1.93 × 2.5 = 12.925 y= 1.93X + 12.925

Cross Classification Models

This type of model estimates trip generation by classifying households into zones based on similarities in socio-economic attributes such as income level or auto ownership rate. Since the estimated values are separate for each group or category of households, this model aligns with our presumption that households with similar characteristics are likely to have similar travel patterns (Mathew & Rao, 2006). The first step in this approach is to disaggregate the data based on household characteristics and then calculate trip generations for each class. Aggregate all calculated rates together in the final step to generate total zonal trip generations. Typically, there are three to four variables for household classification, and each variable includes a few discrete categories. This model’s standard variables or attributes are income categories, auto ownership, trip rate/auto, and trip purpose.

The cross-classification method involves grouping households based on different characteristics such as income and family size. For each group, the trip generation rate can be calculated by dividing the total number of trips made by families in that group by the total number of households in that group within each zone (Aloc & Amar, 2013).

The following are some of the advantages of the cross-classification model:

• Groupings are independent of the TAZ system of the study area.
• No need to assume linearity as it disaggregates the data.
• It can be used for modal split.
• It is simple to run and understand. Furthermore, some of the model’s disadvantages are:
• It does not permit extrapolation beyond its calibration strata.
• No measure of goodness of fit is identifiable.
• It requires large sample sizes (25 households per cell); otherwise, cell values will vary.

After exploring the general definitions and features of the cross-classification model for trip generation estimations, we present a specific example and show how to perform each model step in detail.

Suppose there is a TAZ that contains 500 households, and the average income for this TAZ is

$35000. We are to develop the family of cross-classification curves and determine the number of trips produced by purpose. The low, medium, and high income are $15,000, $25,000, and $55,000, respectively (Note: this data is extracted from 1990 and is therefore out of date. Current rates for income categories may be higher.) (Adapted from: NHI, 2005). For the first step, we should develop the family of cross-class curves for the income levels and find the number of households in each income category.

If we divide the households by six income ranges, we have the table below, derived from the survey.

Based on this table, we can plot the curves in the following format:

If you look at the vertical line on the $40,000 income level, you can find that the intersection of this line with three income range categories shows the percentage of households in that range. Thus, to find the number of total households in each group we have to find the intersection of the curves with average income level ($35,000). In the above plot, the orange line shows these three values, and the table below can be generated according to that:

2. In the second step, after deriving the number of households in each income category, we follow the same procedure for other variables: vehicle ownership. In other words, now we find trips per household in each auto ownership/income group “class.” Again, from the survey, we have the following table, and we can generate the plot of the curves according to that:

Like the previous step, the intersection of four auto ownership curves with low, medium, and high-income level lines determine the share of each auto ownership rate in each income level group:

3. After calculating the number of households in each income level category and auto ownership rate, the next step in the trip generation estimation procedure is to find the number of trips per household based on income level and auto ownership rate. The table below shows the trip generation rate for different income levels:

In Figure 10.3, the meeting point of three income levels and auto ownership status with trip rates yields us the following table:

4. In the fourth step, we must incorporate the trip purpose into the model. To that end, we have trip purposes ratios based on income level from the survey. Like the previous steps, we plot the table on a graph to visualize the curves and find the intersection points of the curves with our three low, medium, and high-income levels:

Based on the findings of this plot, we can now generate the table below, in which the percentage of trips by purpose and income level is illustrated:

Now, we have all the information we need for calculating the total number of trips by household income level and trip purpose.5.

5. In the next step, we calculate the total number of households in each income group based on the number of cars they own. Multiplying the number of households in each income group (00) to the percent of families with a certain number of cars (A) will give us the mentioned results.

6. Once we have the total number of households in each group of income based on auto ownership, we multiply the results to the trips rate (B) so that we have the total number of trips for each group.

7. In the next step, we sum the results of the number of trips by the auto ownership number to have the total number of trips for each income group (∑(00xAxB)).

8. Finally, the results from the above table (416, 3474, 1395) will be multiplied by the percentage of trip purposes for each income group in order to estimate the number of trips by trip purposes for each income group. The table below shows these results as the final trip generation results (example adapted from: NHI, 2005).

Cx∑(00xAxB):

Trip Attraction in the Cross-Classification Model

In the previous section, we modeled trips generated from different households and zones, and calculated their total number by purpose. However, in trip generation, trip attractions play a crucial role, along with trip production. To measure the attractiveness of zones, we can use an easy and straightforward method, which is to determine the size of each zone and the land use types within it, such as square feet of floor space or the number of employees. We can then derive trip generation rates for different attractions from surveys. Trip attractions refer to the number of trips that end in one zone. Typically, we express trip generation rates for different attractions in terms of the number of vehicle trips per household or unit area of non-residential land use. For instance, Table 10.13 provides trip attraction rates for residential and some non-residential land uses. The number 0.074 for HBW trips means that each household can attract 0.074 HBW vehicle trips per day. For non-residential land uses, the numbers are also dependent on the type and size of land uses. As shown in Table 10.13, the retail sector is more attractive than the basic sector.

Table 10.13 shows that the retail sector is more attractive than the basic sector.

After collecting the necessary data from surveys or other appropriate sources, a regression analysis can be used to determine the attraction rates for each land-use category. Then, the HBW vehicle trips attracted to a zone are then calculated as:

TA HBW_H = home-based work vehicle trip attractiveness of the zone by households

N hh = number of household in the zone

TAR _R = trip attraction rate by households

In a similar way, the HBW trips attracted by retail are calculated from the size of retail land use and the retail trip attraction rates.

TA HBW_NR = home-based work vehicle trip attractiveness of the zone

A _NR = non-residential land use size in the zone

TAR _NR = trip attraction rate of the non-residential land use

Assume that Table 10-14 is derived from survey data in a hypothetical city and attractiveness of each land use by trip purpose is generated.

Additionally, a new retail center in a part of the city accommodates 370 retail workers and 550 non-retail workers. According to this information, the number of trips attracted to this area can be calculated as:

First, using the information in table 10.14:

HBW: (370 * 1.7) + (550 * 1.8) = 1619

HBO: (370 * 5.4) + (550 * 2.2) = 3208

NHB: (370 * 3.0) + (550 * 1.1) = 1715

Total = 6542trips/day (example adopted from: Alkaissi, 2021)

Balancing Attractions and Productions

After generating trips, the final step is to balance trip production and attraction. Since trip generation is more accurate, and its validity is more reliable compared to trip attraction models, attraction results are usually brought to the scale of trip generation. Balance factors are used to balance Home-Based Work (HBW) trip attraction and production, which is illustrated in the example below.

According to Table 10.15, the total number of trips generated by all three zones is 600. However, the total number of trips attracted to all the zones is 800, which is an unreasonable value. To fix this issue, we use a balancing factor to multiply each cell in the attraction column by (600/800).

When planning NHB (non-home-based) trips, it is important to take an extra step to ensure that the production and attraction outputs are balanced. This means that for all zones and each zone, the total number of trips attracted and generated should be the same. The reason for this is that NHB trips have unknown origins, meaning that the origin information is not available through surveys or census data. Therefore, the most accurate estimate possible is to set the total NHB productions and attractions to be equal.

In this chapter, we introduced and reviewed the first step of travel demand modeling that is developed for estimating trip generation from each neighborhood or zone. We specifically focused on different methods (growth factor, regression, and cross-classification) and provided examples for each method along with an overview of key concepts and factors contributing to trip generation. Today, the ongoing advancements in computational capacity as well as capabilities for real-time data collection appear to be promising in equipping us with more accurate predictions of trip generation. For instance, GPS mobile data can be used to empirically estimate the rate of trip generation, build advanced models (such as machine learning models) to develop highly calibrated and optimized models.

In the next chapter, we learn about trip distribution. It is worth noting here that the trip distribution is completely based on a foundation of attractiveness of various location determined in trip generation step. As we will see, we used gravity-based models to allocate demand to pair of zones in space. In other words, four-step model is a sequential model, in which the accuracy and reliability of the each step depends on model performance in previous steps.

• activity-based model is travel forecasting framework which is based on the principle that travel is derived from demand reflected in activity patterns of individuals.
• Travel diaries (tours) refers to a chain of trips between multiple locations and for different purposes such as home to work to shopping to home.

Land-use Intensity is a measure of the amount of development on a piece of land usually quantified as dwelling per acre.

• Pass-by trips refers to the trips for which the destination is not a final destination but rather an stop along the way by using the connecting roads.
• Diverted link trips are produced from the traffic flow in the adjacent area of the trip generator that needs diversion. This new traffic will be accumulated in the roadways close to the site.

Key Takeaways

In this chapter, we covered:

• What trip generation is and what factors influence trip generation.
• Different approaches for estimating trip generation rates and the data components needed for each.
• The advantages and disadvantages of different methods and assumptions in trip generation.
• How to perform a trip generation estimation manually using input data.

Prep/quiz/assessments

• List all the factors that affect trip generation. What approaches can help incorporate these factors?
• What are the different categories of trip purposes? How do newer (activity-based models) models differ from traditional models (FSM) based on trip purposes?
• What are the data requirements for the growth factor model, and what shortcomings does this method have?
• Why should trip productions’ and attractions’ total be equal, and how do we address a mismatch?

Alkaissi, Z. (2021). Trip generation model. In Advanced Transportation Planning, Lecture, 4. Mustansiriya University   https://uomustansiriyah.edu.iq/media/lectures/5/5_2021_05_17!10_34_51_PM.pdf

Aloc, D. S., & Amar, J. A. C. (2013). Trip generation modelling of Lipa City . Seminar and research methods in civil engineering research program, University of Philippines Diliman. doi: 10.13140/2.1.2171.7126.

Ben-Akiva, M.E., Bowman, J.L. (1998). Activity based travel demand model systems. In: P. Marcotte, S. Nguyen, S. (eds) Equilibrium and advanced transportation modelling. Centre for Research on Transportation . Springer, Boston, MA. Kluwer Academic Publishers, pp. 27–46.  https://doi.org/10.1007/978-1-4615-5757-9_2

Ettema, D., Borgers, A., & Timmermans, H. (1996). SMASH (Simulation model of activity scheduling heuristics): Some simulations. Transportation Research Record , 1551 (1), 88–94. https://doi.org/10.1177/0361198196155100112

Ewing, R., DeAnna, M., & Li, S.-C. (1996). Land use impacts on trip generation rates. Transportation Research Record , 1518 (1), 1–6. https://doi.org/10.1177/0361198196151800101

Giuliano, G. (2003). Travel, location and race/ethnicity. Transportation Research Part A: Policy and Practice , 37 (4), 351–372. https://doi.org/10.1016/S0965-8564(02)00020-4

Glickman, I., Ishaq, R., Katoshevski-Cavari, R., & Shiftan, Y. (2015). Integrating activity-based travel-demand models with land-use and other long-term lifestyle decisions. Journal of Transport and Land Use , 8 (3), 71–93. https://doi.org/10.5198/jtlu.2015.658

ITE, I. of T. E. (2017). Trip generation manual . ITE Journal. ISSN 0162-8178. 91(10)

Jahanshahi, K., Williams, I., & Hao, X. (2009). Understanding travel behaviour and factors affecting trip rates. In  European Transport Conference, Netherlands (Vol. 2009). https://www.researchgate.net/profile/Kaveh Jahanshahi/publication/281464452_Understanding_Travel_Behaviour_and_Factors_Affecting_Trip_Rates/links/57286bc808ae262228b5e362/Understanding-Travel-Behaviour-and-Factors-Affecting-Trip-Rates.pdf

Malayath, M., & Verma, A. (2013). Activity based travel demand models as a tool for evaluating sustainable transportation policies. Research in Transportation Economics , 38 (1), 45–66. https://doi.org/10.1016/j.retrec.2012.05.010

Mathew, T. V., & Rao, K. K. (2006). Introduction to transportation engineering. Civil Engineering–Transportation Engineering. IIT Bombay, NPTEL ONLINE, Http://Www. Cdeep. Iitb. Ac. in/Nptel/Civil% 20Engineering .

Mauch, M., & Taylor, B. D. (1997). Gender, race, and travel behavior: Analysis of household-serving travel and commuting in San Francisco bay area. Transportation Research Record , 1607 (1), 147–153.

McNally, M. G. (2007). The four step model. In D. A. Hensher, & K. J. Button (Eds.), Handbook of transport modelling , Volume1 (pp.35–53). Bingley, UK: Emerald Publishing. http://worldcat.org/isbn/0080435947

Meyer, M. D., (2016). Transportation planning handbook . John Wiley & Sons: Hoboken, NJ, USA, 2016.

New Jersey Transit, N. (1994). Planning for transit-friendly land use: A handbook for New Jersey communities . NJ Transit, Trenton, NJ.

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)

Park, K., Sabouri, S., Lyons, T., Tian, G., & Ewing, R. (2020). Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model. Transportation , 47 (5), 2087–2108. https://doi.org/10.3141/1607-20

Sharpe, G. B., Hansen, W. G., & Hamner, L. B. (1958). Factors affecting trip generation of residential land-use areas . Highway Research Board Bulletin, 203 . http://onlinepubs.trb.org/Onlinepubs/hrbbulletin/203/203-002.pdf

Wang, X., & Vom Hofe, R. (2020). Selected methods of planning analysis (2nd ed. 2020 edition). Springer. Springer Nature. https://doi.org/10.1007/978-981-15-2826-2

Whitney, V. (2019, September, 29). Activity & Trip Based Travel Models. Medium . https://medium.com/data-mining-the-city/activity-trip-based-travel-models-e4833571570

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

Transportation Land-Use Modeling & Policy Copyright © by Qisheng Pan and Soheil Sharifi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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

About The Author

Trip Generation

Other resources sponsored by the ite transportation planning council (tpc).

This page is intended to serve as a resource for users of trip generation tools and research. It should be noted the resources posted here have not been reviewed or approved by ITE. They are presented here to increase user's awareness of related research and tools that are currently available. If you have ideas or thoughts for additional content on this page, please forward this information to Lisa Fontana Tierney for consideration.

Internal Capture

This spreadsheet is referenced in the Trip Generation Handbook , 3rd Edition, page 46. The section of the handbook is shown below that explains the usage of the spreadsheet. NCHRP Report 684 can be referenced for more details as well.

6.5 Process for Estimating Mixed-Use Trip Generation

The recommended procedure for estimating internal trip capture and trip generation for a mixed-use development is a series of nine steps: Step 1: Determine whether methodology is appropriate for study site. Step 2: Estimate person trip generation for individual on-site land uses. Step 3: Estimate proximity between on-site land use pairs. Step 4: Estimate unconstrained internal person trip capture rates with proximity adjustment. Step 5: Estimate unconstrained demand between on-site land use pairs. Step 6: Estimate balanced demand between on-site land use pairs. Step 7: Estimate total internal person trips between on-site land use pairs. Step 8: Estimate total external person trips for each land use. Step 9: Calculate overall internal capture and total external vehicle trip generation.

The spreadsheet tool automatically performs many of the required calculations based on input data.

If using the spreadsheet tool, the analyst needs to complete Steps 1 through 3. The estimation tool automatically calculates overall internal capture and total external vehicle trips in Steps 4 through 8 and summarizes the results. The complete step-by-step procedure is provided in Chapter 6 of the Handbook  if the analyst chooses to do the calculations manually (if the analyst, for example, is using local data to supplement the national database). Appendix G contains an example application of the recommended process.

Source: An adaptation of a figure in NCHRP 684 (called Tables 103 and 104). Bochner, B., K. Hooper, B. Sperry, and R. Dunphy. NCHRP Report 684: Enhancing Internal Trip Capture Estimation for Mixed-Use Developments . Washington, DC: Transportation Research Board, 2011.

• NCHRP Project 08-51 (Active) Enhancing Internal Trip Capture Estimation for Mixed-Use Developments The objective of this two-phase research project is to produce a methodology for enhancing internal trip capture estimates including (1) a classification system of mixed-use developments that identifies the site characteristics, features, and context that are likely to influence internally captured trips and (2) a data-collection framework for quantifying the magnitude of internal travel to and around mixed-use developments to determine the appropriate reduction rates.

Trip Generation Rates for Infill Developments

• NCHRP Project 08-66 (Active) Trip Generation Rates for Transportation Impact Analyses of Infill Developments The objective of this research is to develop an easily applied methodology to prepare and review site-specific transportation impact analyses of infill development projects located within existing higher-density urban and suburban areas. For the purposes of this study, “methodology” refers to trip-generation, modal split, and parking generation. The methodology will address both daily and peak-hour demand for all travel modes.

Trip Generation Rates at Transit-Oriented Developments

• TCRP H-27A (Completed) Ensuring Full Potential Ridership from Transit-Oriented Development This study of transit-oriented development (TOD) is a national assessment of TOD issues, barriers, and successes. This project included evaluation of 10 case studies from a variety of geographic and development settings. This study indicates that increased ridership is the principal goal of transit agencies in supporting TODs. However, increased ridership as a result of TOD is a complex outcome involving behavioral, locational, and situational factors.

Freight Trip Generation

• NCFRP 25 (Active) Freight Trip Generation and Land Use (Jointly Funded as NCHRP 08-80) - Published as NCHRP Report 739 and NCFRP Report 19 (Joint Report) NCHRP Report 739/NCFRP Report 19: Freight Trip Generation and Land Use provides a comprehensive discussion of how the freight system, and specifically freight trip generation and land use, relate. The report consolidates available freight trip generation models in an electronic database to assist practitioners interested in using these models; identifies the most appropriate approaches to develop and apply freight trip generation models; and estimates establishment-level freight trip generation models in a number of case studies. The case studies confirm the superiority of economic classification systems over standard land use classification systems as the foundation for estimating freight trip generation.

Traditional Neighborhood Developments

• Carolina Transportatin Program - Traditional Neighborhood Development Trip Generation Study

Multivariate Site Trip Generation

Investigations of the components of vehicle trip generation using tool such as multiple regression analysis have resulted in proposed relationships among independent variables and vehicle trips. Two existing models include:

• Index Planning Support Software which contains suggested elasticity equations relating trip generation rates to density, diversity, design, destinations, and distance to heavy rail (labeled "Index 5D"). The Index Plan Builder User Guide was prepared by Criterion Planners in 2007 and the relationships and source bibliography are contained in Appendix A to the guide.
• The Urbemis urban emissions model includes a module containing suggested trip reduction equations associated with site-level and areawide-level mitigation strategies. The users guide "Adjusting Site Level Trip Generation Using Urbemis" was prepared by Nelson/Nygaard Consulting Associates in 2005 and contains study references in its appendix.

Pedestrian and Bicycle Trip Generation

One of the greatest challenges facing the bicycle and pedestrian field is the lack of documentation on usage and demand. Without accurate and consistent demand and usage figures, it is difficult to measure the positive benefits of investments in these modes, especially when compared to the other transportation modes such as the private automobile.

• The National Bicycle & Pedestrian Documentation Program , co-sponsored by and Alta Planning and Design and the Institute of Transportation Engineers (ITE) Pedestrian and Bicycle Council is a nationwide effort to provide a consistent model of data collection and ongoing data for use by planners, governments, and bicycle and pedestrian professionals. Instructions for data collection, project description and forms are included on the web site.

Local Trip Generation Rates

Some jurisdictions have established localized trip generation rates for development approval based on observed characteristics in their community.

• Montgomery County, Maryland has provided trip generation equations and rates are provided for nine general land uses: general office, retail, residential, fast food restaurants, child day-care centers, private schools/educational institutions, senior/elderly housing, mini-warehouse, and automobile filling stations with or without ancillary uses for car washes, convenience stores, and garages. Appendices A-C of Local Area Transportation Review Guidelines: Guidelines of the Montgomery County Planning Board for the Administration of the Adequate Public Facilities Ordinance contain local trip generation rates.

Regional Travel Demand Models

Travel demand models use roadway and transit networks, population and employment data to calculate the expected demand for transportation facilities.

• Travel Estimation Techniques for Urban Planning updates NCHRP Report 187, "Quick-Response Urban Travel Estimation Techniques and Transferable Parameters" (1978) and provides a thorough review of the four-step travel demand process and transferable parameters that can be used in simple planning analyses.
• NCHRP 08-61 [Active] Travel Demand Forecasting: Parameters and Techniques The objective of this research is to update existing research to reflect current travel characteristics and to provide guidance on travel demand forecasting procedures for solving common transportation problems.

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GUIDELINES FOR USING TRIP GENERATION RATES OR EQUATIONS

Guidance is provided to users of ITE's 'Trip Generation' on the selection of the most appropriate method for estimating trip generation. Aids to understanding methodologies are discussed and include the graphic plot, the equation, and weighted average rate. Methodology selection is discussed, and guidelines are given for using trip generation rates or equations. It is noted that the ITE Permanent Trip Generation Committee has examined each land use, independent variable, and day/time period and has developed a set of suggested use rates, equations, and engineering judgment.

• Find a library where document is available. Order URL: http://worldcat.org/oclc/614107147
• By the ITE Technical Council Committee 6A-32.

Institute of Transportation Engineers (ITE)

• Publication Date: 1990-8
• Features: References;
• Pagination: p. 14-16
• ITE Journal
• Issue Number: 8
• Publisher: Institute of Transportation Engineers (ITE)
• ISSN: 0162-8178
• Serial URL: https://www.ite.org/publications/ite-journal/

Subject/Index Terms

• TRT Terms: Equations ; Estimating ; Guidelines ; Land use ; Traffic engineering ; Trip generation
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 00497439
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Sep 30 1990 12:00AM

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Mixed-Use Trip Generation Model

Neighborhoods that mix land uses and make walking safe and convenient allow residents and workers to drive significantly less, thereby reducing traffic and realizing other benefits.

To help give communities better tools to analyze new development, EPA, in cooperation with the Institute of Transportation Engineers (ITE), worked with leading researchers and practitioners to develop new data and methods to estimate the trip-generation impacts of mixed-use developments. 

Download the  Mixed-Use Trip Generation Tool .

On this page:

Purpose of the Tool

• How the Tool was Created

Additional Resources

Trip generation analysis, the technical methodology to estimate how much traffic a new development will create, have been standardized by ITE and are used by traffic engineers across the country.

However, these methods are generally based on data collected from single-use, automobile-dependent, suburban sites, and therefore they do not adequately account for the effects of compact development, mix of uses, site design, walkability, transit, and regional accessibility – key elements of smart growth strategies and of a sustainable community.

To address this challenge, EPA created a spreadsheet tool that makes it easy for local stakeholders to estimate the internal capture of trips generated by a new mixed-use development. 

The spreadsheet estimates vehicle trips generated by a new mixed-use development in peak periods and for an entire day. The method also predicts trips by walking and transit and estimates the daily vehicle miles of travel associated with the development. 

How the Tool Was Created

In order to create the tool, EPA analyzed six metropolitan regions, merging data from household travel surveys, GIS databases, and other sources to create consistent land use and travel measures. The tool requires information about the development site and its surrounding area, including geographic, demographic, and land use characteristics.

It includes default national parameters for trip generation, but allows the use of local values if available. An associated report, available upon request, describes the research basis for the method and the data used to calibrate and validate it. 

This method was used in several regions in California, Washington, and New Mexico, and the Virginia Department of Transportation adopted it as a statewide standard for determining the traffic impacts of urban mixed-use developments.

The following resources give more information on development, testing, and use of the method and tool:

• Traffic Generated by Mixed-Use Developments – A Six-Region Study Using Consistent Built Environmental Measures , Ewing et al., ASCE Journal of Urban Planning and Development , 2011. This peer-reviewed article describes the analytic basis for the models, database development, and reports on validation tests. (Fee or subscription required.)
• Mixed-Use Development Trip Generation , Fehr & Peers. This website, by the firm that led development of the tool for EPA, describes the tool and reports on the statistical validation of the models.
• Smart Growth Trip Generation and Parking Study , San Diego Association of Governments (SANDAG), 2010. SANDAG approved the method for use regionwide following comparison to local sites and review by local staff. This web page provides details on its review and implementation.

The following resources describe standard trip generation methods and other efforts to better understand the impacts of mixed-use developments and related smart growth strategies:

• Trip and Parking Generation Technical Resources , Institute of Transportation Engineers. Links to trip generation publications and other resources.
• Enhancing Internal Trip Capture Estimation for Mixed-Use Developments ,  National Cooperative Highway Research Program Report 684, 2011. This method, similar in scope to the EPA method described above, estimates peak-period internal capture rates for mixed-use developments for use in standard ITE trip generation applications.
• Effects of TOD on Housing, Parking, and Travel , Transit Cooperative Research Program Report 128, 2008. This report gives insight into the characteristics of residents of transit-oriented development, including their trip generation rates. 
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ITE Releases Trip Generation Handbook, 3rd Edition - Special member pricing of $50.00 through the end of January

By marianne saglam posted 10-06-2014 03:59 pm, ite announces the release of  trip generation handbook , 3rd edition..

The principal objectives of Trip Generation Handbook , 3rd Edition are to provide recommendations for:

• Proper techniques for estimating trip generation, both person and vehicle , for potential development sites in urban, suburban, and rural settings;
• Standardization of trip generation data collection efforts; and
• Ethics and objectivity in the use of Trip Generation Manual data .

The recommended practice material from the second edition of Trip Generation Handbook has undergone a comprehensive review, resulting in an update and refinement to each component of the recommended practice.

• evaluation of mixed-use developments;
• establishment of local trip generation rates;
• interpretation of the Trip Generation Manual data plots;
• estimation of truck trips generated by a development site; and
• collection of data to support trip generation analyses.
• New guidance is provided for the estimation of trips generated by sites in urban settings or served by significant levels of transit service.
• Additional data are provided in the pass-by trip data tables.
• Standard deviation values for Trip Generation Manual data plots are updated to represent weighted values.

To order your copy of Trip Generation Handbook , 3rd Edition , visit www.ite.org/bookstore . The cost for ITE members is $50.00 through the end of January  and for nonmembers the cost is $93.75.

Trip Generation Handbook , 3rd Edition . Pub No. RP-028C. ISBN 10: 1-933452-80-3; ISBN 13: 978-1-933452-80-7.

This report is published as a proposed recommended practice of ITE. As such, it is to be considered in its proposed form, but is subject to change after receipt and consideration of suggestions from those who have reviewed the report.

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4 Methods for Calculating Reductions to Trip Generation for Internal Trips

By   Mike Spack

December 9, 2013

Here are the four methods I’ve encountered for accounting for this deduction:

• ITE’s Trip Generation Handbook, 2nd Edition :   There’s a specific methodology laid out and the underlying data is based on a couple of mixed use sites in Florida.  If your site is all commercial/retail, the tables suggest using a 20% reduction in trips during the p.m. peak hour.
• NCHRP Report 684 – Enhancing Internal Trip Capture Estimation for Mixed Use Developments :  Similar methodology to the ITE version above, but expanded the dataset from three sites in Florida by adding in three sites in Texas.  Looks like the average trip reduction is about 13%.
• US EPA Mixed-Use Development Trip Generation :  Based on a much larger sample size and requires much more detailed inputs.  I used it once and my internal capture results were 8 % to 11% reduction factors depending on the time-frame.
• Use a 10% across the board reduction:  I typically use this blanket reduction as do most of the traffic engineers in Minnesota.  Given the above datasets, I think it’s reasonable if not slightly conservative.  It’s also quick to use.

Gathering data on internal trips is very tedious.  Call out to AirSage – I wonder if we can look at cell phone usage within mixed use developments to tease out how many stops people make within the site.  Our industry could use a lot more robust dataset/methodology.

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

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