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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?

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Part III: Travel Demand Modeling

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

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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

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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.

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Lecture 3 - Trip Generation

CIVE 461/861: Urban Transportation Planning

We can examine trip generation in several ways…

Academic Vs. Practitioner Considerations

• Practitioner struggle deciding between a theoretically sound but difficult to implement set of models & a more pragmatic modelling approach reflecting the limitations of the data, time, & resources available for a study
• Models are too complex
• Implies that heuristic approaches , rules of thumb, & ad hoc procedures are easier to understand & therefore preferable

Planning & Monitoring with the Help of Models

Classic 4-Step Travel Model

• Trip generation, 2) Trip distribution, 3) Modal split, & 4) Traffic assignment
• Generally recognized that travel decisions are not actually taken in this type of sequence
• Model sequencing depends on the form of the utility function assumed to govern all these travel choices
• Four-stage model is seen as concentrating attention on only a limited range of travellers’ responses
• Current thinking requires analysis of a wider range of responses to transport problems & schemes
• Route followed to avoid congestion or take advantage of new links - choice of parking place or combination of services in the case of public transport
• Mode used to get to the destination
• Departure time to avoid the most congested part of the peak
• Trip destination to a less congested area
• Trip frequency by undertaking the trip on another day, perhaps combining it with other activities

Trip Definitions

• Home-based work
• Home-based grade school
• Home-based university
• Home-based shopping
• Home-based other
• Non-Home-Based (NHB) Trip : A trip which neither starts nor ends at the home
• Trip production : The home end of a HB trip or origin of a NHB trip
• Trip attraction : The non-home end of a HB trip or the destination of a NHB trip

Trip Production & Attraction

Origin/Destination Vs. Production/Attraction

• Production & attraction approach works fine when dealing with 24-hour work trips
• Lose directionality when dealing with peak-period flows
• Typically use origin/destination approach

Trip Purpose

• Travel to work
• Travel to school or college (education trips)
• Shopping trips
• Social & recreational trips
• Escort trips (to accompany or collect someone else)
• Other trips (healthcare & personal business)
• First two are usually considered compulsory (mandatory) trips & we build skeleton schedules around them
• Other usually considered discretionary (optional) trips

Factors Affecting Trip Generation

• Since trip generation performed before distribution & mode split, usually do not use travel times, costs, etc.

Personal trip productions

• Car ownership
• Family size
• Household structure
• Value of land
• Residential density
• Accessibility
• First 4 considered in most household trip generation models
• Value of land & residential density often considered in zonal studies
• Accessibility rarely included in models but makes trip generation elastic (responsive) to changes in transport system

Personal trip attractions

• Floorspace available for industrial, commercial, & other services
• Zonal employment

Freight trip production & attraction

Number of employees

Number of sales

Roofed area of firm

Total area of firm

What about type of firm? Accessibility? Curiously, few applications in freight models despite it seeming logical that different products have different transport requirements

Trip Generation Aggregation

• Temporal aggregation : hourly, peak/off-peak, daily, weekly
• Persons: by income, driver’s license, vehicle ownership, occupation type, household size, etc.
• Households: by income, vehicle ownership, household size, number of workers, with/without children
• Zones : by location (e.g., downtown, suburb), major trip generator (e.g., hospital, stadium)
• Most operational models use either zones or households. Why?

Trip Generation Approaches

Three major operational approaches.

• Trip rate models
• Cross-classification models
• Regression models

Trip Rate Models

• Home-to-work trips for retail employees = Total observed trips by retail employees / Total retail employment
• Trip rates can be geographically stratified (i.e., different rates used for different areas of the city) & may combine multiple factors
• Total shopping trips=

A1 ×Population in Downtown Area + A2×Population in Inner Suburbs + A3×Population in Outer Suburbs

• Constant trip generation rate is estimated from observed data
• Trips attracted to a shopping mall = \(A_s \times {MallFloorArea}\)
• Trips generated by a zone = \(\sum_k A_k \times HH With Cars \text{ where k} = 0, 1, 2, 3, etc.\)
• Trips generated in a zone = \(t_i\)
• Trips generated in the future \(T_i = t_i \times PopulationGrowthRate\)

Cross-Classification Models

• Classify households (or persons) by one or more variables - often household size & number of vehicles
• Assume trip rates are relatively constant within each group
• Compute average trip rate for each group
• Zonal trips = sum of trips generated by all groups found in the zone
• Note: trip rate model is a cross-classification model by trip type not trip maker

Linear Regression Models

• Use regression to estimate “best fit” linear regression between # of trips & one or more explanatory variables \[T=1.229+1.379V\]
• Where \(T\) is daily trip productions per household for all purposes & \(V\) is the # of vehicles per household \[A=61.4+0.93E\]
• Where \(A\) is daily work trip attractions for a given zone & \(E\) is total zonal employment

Parameter Signs & Theoretical Expectations

• Model specification should “make sense” from behavioral & theoretical perspectives
• Variables included should have a causal influence on the dependent variable
• Parameters should have expected signs (+ or -)
• Parameters should have reasonable magnitudes

Parameter Signs & Theoretical Expecations

• Do these parameter signs make sense? \[Daily HH Trips= 3 Persons In HH − 0.5 HH Vehicles\]
• No : We expect trip making to increase with # of vehicles
• Does this parameter magnitude make sense? \[Daily HH Work Trips=0.2 HH Workers\]
• No : We expect each worker to make approx. 2 trips per day
• Does it make sense to include No. of HH workers in the model? \[Daily HH School Trips=1+0.2 HH Workers\]
• No : There’s no logical/causal relationship between no. of workers & school trips

Or Could The School Model Make Sense?…

International institute for applied systems analysis in austria:.

• Women with no schooling - 4.5 children
• Women with 2-3 years of primary school – 3 children
• Women who complete one or two years of secondary school - 1.9 children (replacement rate is 2.1 children)
• With one or two years of college, the average childbearing rate falls even further, to 1.7
• When women enter the workforce, start businesses, inherit assets, & otherwise interact with men on an equal footing, children per household fades even more dramatically

Zonal-Based Multiple Regression

Some important considerations.

• For this reason, only successful if inter-zonal variations reflect real reasons for trip variation
• Necessary that zones are homogeneous in socioeconomic composition & represent a wide range of conditions
• Major problem is that main variation in person trips is intra-zonal
• Role of intercept : One would expect estimated regression line to pass through zero but large intercepts are common. Why?
• Null zones : No information about certain dependent variables (e.g., no. HB trips in a non-residential zone). These zones must be excluded from analysis

Some important considerations cont…

• Zonal totals vs. zonal means : when formulating model, analyst has choice between aggregate or total variables , such as trips per zone or cars per zone vs. rates, such as trips per household or cars per household in the zone
• Important difference: \[Y_i=\beta_0+\beta_1 X_{1i}+\beta_2 X_{2i}+…+\beta_k X_{𝑘𝑖}+E_𝑖 \text{ (total trip model)}\] \[𝑦_i=\beta_0+\beta_1 x_1i+\beta_2 x_2i+…+\beta_k x_{𝑘𝑖}+e_i \text{ (trip rate model)}\]
• Where \(y_i=Y_i/H_i\) , \(x_i=X_i/H_i\) , & \(e_𝑖=E_i/H_i\)

Even more important considerations…

• Similar equations & parameters have a similar interpretation
• However , unless \(H_i\) is constant across zones, constant variance condition cannot hold for error terms in total trips model
• Aggregate variables reflect size of zone, thus magnitude of error depends on size of zone - heteroskedasticity (variability in variance)
• Using a \(1/H_i\) multiplier normalizes the model & reduces heteroskedasticity because model becomes independent of zone size
• Aggregate variable models often yield higher \(R^2\) but spurious effect from zone size helping to explain total trips
• DO NOT mix rate & aggregate variables in same model
• Even when rates used, zonal regression is conditioned by nature & size of zones ( spatial aggregation problem )
• Interzonal variance diminishes with larger zone size

Household-Based Regression

• More expensive models in terms of data collection, calibration, & operation
• Larger sampling errors (regression to mean with larger samples, so small zone means not capturing average person), which is assumed non-existent by multiple linear regression model
• Household used rather than person because we have a hard time incorporating intra-household dynamics (e.g., vehicle availability) - Some advances but still not great
• Can decide on variables in step-wise process but must be careful - May exclude a variable with slightly lower prediction that is easier to forecast
• Opposite approach of including all variables & removing variables can be a better approach

Validity of Linear Regression Assumption

• Regression models are easy to construct & use
• No correlations between explanatory variables
• Residual \(e_i = y_{i,observed} – y_{i,predicted}\)
• Residuals should be normally distributed with constant variance – can plot a histogram to check
• Residuals should be uncorrelated with any explanatory variable – can plot residuals vs. explanatory variables

Histogram of Residuals

Residuals Vs. Explanatory Variables

• Three variables are defined \(X\) , \(Y\) , & \(Z\) where \(X\) & \(Z\) are explanatory variables & \(Y\) is the dependent variable

First Model \(y = aX + b\)

Analysis of Residuals Vs. X

Analysis of Residuals Vs. Z

Second Model \(y = aX bZ + c\)

Analysis of Residuals Vs. X & Z$

Dealing With Non-Linear Relationships

Transform the Explanatory Variable (add \(x^2\) term)

Other Transformations

• Log transformation, \(x’ = ln(x)\) if effects starts strong at low x but reaches limiting return with further increases in \(x\) (often used for income variables)
• \(X_1\) = 0 if vehicle ownership = 0 & 1 if vehicle ownership > 0
• \(X_2\) = 1 if married & 0 otherwise
• \(X_1\) = age in years \(\times\) income less than $30k
• \(X_2\) = age in years \(\times\) income $30k-$60k
• \(X_3\) = age in years \(\times\) income over $60k
• \(X_4\) = gender (male) \(\times\) have fulltime job
• \(X_5\) = gender (male) \(\times\) have parttime job

Non-Linearity in Trip Generation Variables

• Linear regression assumes each independent variable exerts a linear influence on the dependent variable
• Not easy to detect non-linearity because relationship can appear linear until other variables enter into the model (i.e., it is representing the effect of multiple variables with a linear overall effect)
• Multivariate graphs can be helpful to identify non-linearity

Consider the variables trips per household ( \(Y\) ), number of workers ( \(𝑋_1\) ), and number of vehicles ( \(𝑋_2\) ). Successive steps were performed of a stepwise model estimation. Values (in parenthesis) are t-ratios. In the step 4 model, \(Z_1\) takes the value 1 for households with one car and 0 otherwise and \(Z_2\) takes the value 1 for households with two or more cars and 0 otherwise. We can see that zero car households will have the value 0 for both \(Z_1\) and \(Z_2\) . Even without the higher \(R^2\) , the step 4 model would be preferred because it clearly demonstrates there is a non-linear effect that’s ignored by \(𝑋_2\) .

Variable Selection & Model Building

• Problem : Choose the set of appropriate explanatory variables from a set of candidate variables - variable selection
• Exploring all possible variables - many combinations
• Forward selection : add variables to the model one at a time until there are no remaining candidate variables that improve fit
• Backward elimination : begin with all possible variables & remove the least significant variables
• Are there problems with these approaches?

Variable Specification & Forecasting

• Consider variables important for explaining differences in trip-making decisions
• Frequency & data plotting gives idea of variability with respect to different variables
• Variable choice depends on availability of observed information & capacity to forecast
• Statistical significance tests
• Model goodness of fit can be used to identify impact of specific variables

Model Validation

• A good way to validate a model is to compare observed vs. modeled values for some groupings of the data
• Better than comparing totals because biases may cancel in that case (high prediction cancels with low prediction)
• Errors are reasonably low (i.e., less than 30%)
• Large bias could be addressed by adjusting model parameters, but it’s not easy because there are no clear rules

Model Validation - Learning From Machine Learning

• Leave-one-out (LOO) & k-folds cross-validation to get mean & std. dev.
• Score (R2 for linear regression)
• Mean absolute error (MAE)

Obtaining Zonal Totals

• Simple process for zone-based models because already modeling total trips
• \(T_i=H_i(0.91+1.44\bar{X}_{1i}+1.07\bar{X}_{2i}\) where \(T_i\) is the total number of HB trips in zone \(i\)
• With dummy variables, we need to know the number of households in each group
• \(T_i=H_i(0.84+1.41X)+0.75H_{1i}+3.14H_{2i}\) where \(H_{1i}\) is the number of one vehicle households & \(H_{2i}\) is the number of two vehicle households in zone \(i\)

Matching Generations & Attractions

Models do not guarantee, by default, that total trips originating in a zone (the origins O_𝑖) at all zones will equal the total trips attracted (the destinations 𝐷_𝑗)

Following expression may not hold \[\sum_i O_i=\sum_j D_j\]

Generally assumed that trip generation models are better than trip attraction models

Total trips are then \(T=\sum_i O_i\) & a factor applied to trip attraction \[f=T/\sum_j D_j\]

This equality is necessary for the next model step: trip distribution

Growth Factor Modeling

• Growth factor method given by \[T_{i+t}=F_tT_i\]
• Where \(T_i\) is total trips at time \(i\) , \(T_{i+t}\) is total trips at time \(i+t\) , & \(F_i\) is a growth factor. Determining total current trips is a simple process but forecasting future trips (i.e., \(F_t\) ) is a big challenge
• \(F_t\) is related to variables such as population ( \(P\) ), income ( \(I\) ), & car ownership ( \(C\) ) \[F_t = \frac{f(P_t,I_t,c_t)}{f(P_i,I_i,c_i)}\]

Growth Factor Example

Consider a zone with 250 households with vehicles and 250 households without vehicles. Assuming we know the average trip generation rates of each group:

• Vehicle-owning households produce 6.0 trips/day
• Non-vehicle-owning households produce 2.5 trips/day

We can determine the current number of trips as \[T_i = 250 \times 2.5 + 250 \times 6.0 = 2125 \text{ trips/day}\]

Let’s assume that all households in the future have a vehicle. We can then estimate a simple multiplicative growth factor as \[F_t = 1/0.5 = 2.0\] \[T_{i+t} = 2 \times 2125 = 4250 \text{ trips/day}\]

Growth factor methods are crude & generally used in practice for external trips where additional information is not available

• Let us also assume that in the future all households will have a vehicle, but income & population remain unchanged

\[T_i = 6 \times 500 = 3000 \text{ trips/day}\]

• But do we expect non-vehicle households to make this many more trips if income is not changing?

Stability & Updating Trip Generation Model

• Temporal stability : Population/socio-economic structure changes over time & affects trip rates
• Geographic stability : Trip-making patterns vary from place to place
• If models were developed using old data , it is necessary to update the model for present situation
• Best way to re-estimate trip generation model using present data
• If sufficient data is not available , we can update previously estimated model using small present data sample

Forecasting Variables In Trip Generation Analysis

• Standard variables are household totals, household size (and structure), number of vehicles owned, & household income
• Social circumstances likely affect travel
• Personal living alone will have a different set of tradeoffs & coordination patterns than person living in with others
• Multi-person household dynamics will vary depending on structure – several students living together may not coordinate trips in same way as a family
• Elderly people living with younger people may be more engaged outside the home than those living alone or with similarly aged persons
• Appearance of a pre-school children
• Time when youngest child reaches school age
• Time when youth leaves home & lives alone, with other young adults, or marries
• Time when all children of a couple have left home but couple has not retired yet
• Time when all members of a household reach retirement age
• Important factors for identifying/classifying households as being in similar life stage & potentially having more homogeneous travel patterns
• What about overall aging of population? Age tends to be associated with decline in mobility in change in lifestyle

Trip Generation Inelastic In Most Models

• Independent of the level of service provided in the transport system
• Probably unrealistic but only recently techniques have been developed which can take systematic account of these (induced demand) effects

What is the fundamental event of interest?

• Home-based (HB) trip : trip with the home (or hotel for a nonresident) being either the trip origin or destination
• Non-home-based (NHB) trip : trip where neither end of the trip is the home
• Trip production : defined as the home end of an HB trip or as the origin of an NHB
• Trip attraction : defined as the non-home end of an HB trip or the destination of an NHB
• Trip generation : total number of trips generated by households in a zone, both HB & NHB
• Usually has a purpose associated to it: work, study, shopping, leisure, etc.
• Tour or trip chain : a sequence of linked trips
• Contemporary models are generally interested in tours rather than trips . Why?

Trip Generation & Accessibility

• Classical transport planning (4-step) model can incorporate an iterative process between distribution & assignment, leaving trip generation unaltered
• True even in most contemporary models, which attempt to appropriately solve supply-demand equilibrium problem
• E.g., Extension of a subway line would not generate more trips between that zone & other zones
• May hold for compulsory trips but unlikely to hold for discretionary trips
• Attempts made to incorporate an accessibility measure into trip generation by replacing \(O_i = f(H_i)\) by \(f(H_i,A_i\) where \(H_i\) are household characteristics & \(A_i\) is an accessibility measure
• Often give wrong sign or non-significant results due to unresolved model dynamics & problems from using cross-sectional rather than longitudinal data
• Leads to activity-based models & trip frequency/scheduling choice models (discussed in CIVE864 )

CIVE461 Home

Travel Demand Forecasting: Parameters and Techniques (2012)

Chapter: chapter 1 - introduction.

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

1 1.1 Background In 1978, the Transportation Research Board (TRB) published NCHRP Report 187: Quick-Response Urban Travel Estimation Techniques and Transferable Parameters (Sosslau et al., 1978). This report described default parameters, factors, and manual techniques for doing planning analysis. The report and its default data were used widely by the transportation planning profession for almost 20 years. In 1998, drawing on several newer data sources, including the 1990 Census and Nation- wide Personal Transportation Survey, an update to NCHRP Report 187 was published in the form of NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998). Since NCHRP Report 365 was published, significant changes have occurred affecting the complexity, scope, and context of transportation planning. Transportation planning tools have evolved and proliferated, enabling improved and more flexible analyses to support decisions. The demands on trans- portation planning have expanded into special populations and broader issues (e.g., safety, congestion, pricing, air quality, environment, climate change, and freight). In addition, the default data and parameters in NCHRP Report 365 need to be updated to reflect the planning requirements of today and the next 10 years. The objective of this report is to revise and update NCHRP Report 365 to reflect current travel characteristics and to pro- vide guidance on travel demand forecasting procedures and their application for solving common transportation problems. It is written for “modeling practitioners,” who are the public agency and private-sector planners with responsibility for devel- oping, overseeing the development of, evaluating, validating, and implementing travel demand models. This updated report includes the optional use of default parameters and appropriate references to other more sophisticated techniques. The report is intended to allow practitioners to use travel demand fore- casting methods to address the full range of transportation planning issues (e.g., environmental, air quality, freight, multimodal, and other critical concerns). One of the features of this report is the provision of trans- ferable parameters for use when locally specific data are not available for use in model estimation. The parameters pre- sented in this report are also useful to practitioners who are modeling urban areas that have local data but wish to check the reasonableness of model parameters estimated from such data. Additionally, key travel measures, such as average travel times by trip purpose, are provided for use in checking model results. Both the transferable parameters and the travel measures come from two main sources: the 2009 National Household Travel Survey (NHTS) and a database of model documentation for 69 metropolitan planning organizations (MPOs) assembled for the development of this report. There are two primary ways in which planners can make use of this information: 1. Using transferable parameters in the development of travel model components when local data suitable for model development are insufficient or unavailable; and 2. Checking the reasonableness of model outputs. This report is written at a time of exciting change in the field of travel demand forecasting. The four-step modeling process that has been the paradigm for decades is no longer the only approach used in urban area modeling. Tour- and activity-based models have been and are being developed in several urban areas, including a sizable percentage of the largest areas in the United States. This change has the potential to significantly improve the accuracy and analytical capability of travel demand models. At the same time, the four-step process will continue to be used for many years, especially in the smaller- and medium- sized urban areas for which this report will remain a valuable resource. With that in mind, this report provides information on parameters and modeling techniques consistent with the C h a p t e r 1 Introduction

2four-step process and Chapter 4, which contains the key information on parameters and techniques, is organized con- sistent with the four-step approach. Chapter 6 of this report presents information relevant to advanced modeling practices, including activity-based models and traffic simulation. This report is organized as follows: • Chapter 1—Introduction; • Chapter 2—Planning Applications Context; • Chapter 3—Data Needed for Modeling; • Chapter 4—Model Components: – Vehicle Availability, – Trip Generation, – Trip Distribution, – External Travel, – Mode Choice, – Automobile Occupancy, – Time-of-Day, – Freight/Truck Modeling, – Highway Assignment, and – Transit Assignment; • Chapter 5—Model Validation and Reasonableness Checking; • Chapter 6—Emerging Modeling Practices; and • Chapter 7—Case Studies. This report is not intended to be a comprehensive primer for persons developing a travel model. For more complete information on model development, readers may wish to consult the following sources: • “Introduction to Urban Travel Demand Forecasting” (Federal Highway Administration, 2008); • “Introduction to Travel Demand Forecasting Self- Instructional CD-ROM” (Federal Highway Administra- tion, 2002); • NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998); • An Introduction to Urban Travel Demand Forecasting— A Self-Instructional Text (Federal Highway Administration and Urban Mass Transit Administration, 1977); • FSUTMS Comprehensive Modeling Online Training Workshop (http://www.fsutmsonline.net/online_training/ index.html#w1l3e3); and • Modeling Transport (Ortuzar and Willumsen, 2001). 1.2 Travel Demand Forecasting: Trends and Issues While there are other methods used to estimate travel demand in urban areas, travel demand forecasting and mod- eling remain important tools in the analysis of transportation plans, projects, and policies. Modeling results are useful to those making transportation decisions (and analysts assisting in the decision-making process) in system and facility design and operations and to those developing transportation policy. NCHRP Report 365 (Martin and McGuckin, 1998) pro- vides a brief history of travel demand forecasting through its publication year of 1998; notably, the evolution of the use of models from the evaluation of long-range plans and major transportation investments to a variety of ongoing, every- day transportation planning analyses. Since the publication of NCHRP Report 365, several areas have experienced rapid advances in travel modeling: • The four-step modeling process has seen a number of enhancements. These include the more widespread incor- poration of time-of-day modeling into what had been a process for modeling entire average weekdays; common use of supplementary model steps, such as vehicle availability models; the inclusion of nonmotorized travel in models; and enhancements to procedures for the four main model components (e.g., the use of logit destination choice models for trip distribution). • Data collection techniques have advanced, particularly in the use of new technology such as global positioning systems (GPS) as well as improvements to procedures for performing household travel and transit rider surveys and traffic counts. • A new generation of travel demand modeling software has been developed, which not only takes advantage of modern computing environments but also includes, to various degrees, integration with geographic information systems (GIS). • There has been an increased use of integrated land use- transportation models, in contrast to the use of static land use allocation models. • Tour- and activity-based modeling has been introduced and implemented. • Increasingly, travel demand models have been more directly integrated with traffic simulation models. Most travel demand modeling software vendors have developed traffic simulation packages. At the same time, new transportation planning require- ments have contributed to a number of new uses for models, including: • The analysis of a variety of road pricing options, including toll roads, high-occupancy toll (HOT) lanes, cordon pricing, and congestion pricing that varies by time of day; • The Federal Transit Administration’s (FTA’s) user benefits measure for the Section 5309 New Starts program of transit projects, which has led to an increased awareness of model properties that can inadvertently affect ridership forecasts;

3 • The evaluation of alternative land use patterns and their effects on travel demand; and • The need to evaluate (1) the impacts of climate change on transportation supply and demand, (2) the effects of travel on climate and the environment, and (3) energy and air quality impacts. These types of analyses are in addition to several traditional types of analyses for which travel models are still regularly used: • Development of long-range transportation plans; • Highway and transit project evaluation; • Air quality conformity (recently including greenhouse gas emissions analysis); and • Site impact studies for developments. 1.3 Overview of the Four-Step Travel Modeling Process The methods presented in this report follow the conven- tional sequential process for estimating transportation demand that is often called the “four-step” process: • Step 1—Trip Generation (discussed in Section 4.4), • Step 2—Trip Distribution (discussed in Section 4.5), • Step 3—Mode Choice (discussed in Section 4.7), and • Step 4—Assignment (discussed in Sections 4.11 and 4.12). There are other components commonly included in the four-step process, as shown in Figure 1.1 and described in the following paragraphs. The serial nature of the process is not meant to imply that the decisions made by travelers are actually made sequentially rather than simultaneously, nor that the decisions are made in exactly the order implied by the four-step process. For example, the decision of the destination for the trip may follow or be made simultaneously with the choice of mode. Nor is the four-step process meant to imply that the decisions for each trip are made independently of the decisions for other trips. For example, the choice of a mode for a given trip may depend on the choice of mode in the preceding trip. In four-step travel models, the unit of travel is the “trip,” defined as a person or vehicle traveling from an origin to a destination with no intermediate stops. Since people traveling for different reasons behave differently, four-step models segment trips by trip purpose. The number and definition of trip purposes in a model depend on the types of information the model needs to provide for planning analyses, the char- acteristics of the region being modeled, and the availability of data with which to obtain model parameters and the inputs to the model. The minimum number of trip purposes in most models is three: home-based work, home-based nonwork, and nonhome based. In this report, these three trip purposes are referred to as the “classic three” purposes. The purpose of trip generation is to estimate the num- ber of trips of each type that begin or end in each location, based on the amount of activity in an analysis area. In most models, trips are aggregated to a specific unit of geography (e.g., a traffic analysis zone). The estimated number of daily trips will be in the flow unit that is used by the model, which is usually one of the following: vehicle trips; person trips in motorized modes (auto and transit); or person trips by all modes, including both motorized and nonmotorized (walking, bicycling) modes. Trip generation models require some explanatory variables that are related to trip-making behavior and some functions that estimate the number of trips based on these explanatory variables. Typical variables include the number of households classified by characteristics such as number of persons, number of workers, vehicle availability, income level, and employment by type. The output of trip generation is trip productions and attractions by traffic analysis zone and by purpose. Trip distribution addresses the question of how many trips travel between units of geography (e.g., traffic analysis zones). In effect, it links the trip productions and attractions from the trip generation step. Trip distribution requires explanatory variables that are related to the cost (including time) of travel between zones, as well as the amount of trip-making activity in both the origin zone and the destination zone. The outputs of trip distribution are production-attraction zonal trip tables by purpose. Models of external travel estimate the trips that originate or are destined outside the model’s geographic region (the model area). These models include elements of trip generation and distribution, and so the outputs are trip tables represent- ing external travel. Mode choice is the third step in the four-step process. In this step, the trips in the tables output by the trip distri- bution step are split into trips by travel mode. The mode definitions vary depending on the types of transportation options offered in the model’s geographic region and the types of planning analyses required, but they can be generally grouped into auto mobile, transit, and nonmotorized modes. Transit modes may be defined by access mode (walk, auto) and/or by service type (local bus, express bus, heavy rail, light rail, commuter rail, etc.). Nonmotorized modes, which are not yet included in some models, especially in smaller urban areas, include walking and bicycling. Auto modes are often defined by occupancy levels (drive alone, shared ride with two occupants, etc.). When auto modes are not modeled separately, automobile occupancy factors are used to convert the auto person trips to vehicle trips prior to assignment. The outputs of the mode choice process include person trip tables by mode and purpose and auto vehicle trip tables.

4Time-of-day modeling is used to divide the daily trips into trips for various time periods, such as morning and afternoon peak periods, mid-day, and evening. This division may occur at any point between trip generation and trip assignment. Most four-step models that include the time-of-day step use fixed factors applied to daily trips by purpose, although more sophisticated time-of-day choice models are sometimes used. While the four-step process focuses on personal travel, commercial vehicle/freight travel is a significant component of travel in most urban areas and must also be considered in the model. While simple factoring methods applied to per- sonal travel trip tables are sometimes used, a better approach is to model such travel separately, creating truck/commercial vehicle trip tables. The final step in the four-step process is trip assignment. This step consists of separate highway and transit assignment processes. The highway assignment process routes vehicle trips from the origin-destination trip tables onto paths along Forecast Year Highway Network Forecast Year Transit Network Forecast Year Socioeconomic DataTrip Generation Model Internal Productions and Attractions by Purpose Trip Distribution Model Mode Choice Model Person and Vehicle Trip Tables by Purpose/Time Period Time of Day Model Person and Vehicle Trip Tables by Mode/Purpose/Time Period Highway Assignment CHECK: Input and output times consistent? Transit Assignment Highway Volumes/ Times by Time Period Transit Volumes/ Times by Time Period Input Data Model Output Model Component Decision Feedback Loop Yes No Truck Trip Generation and Distribution Models Production/Attraction Person Trip Tables by Purpose Truck Vehicle Trip Tables by Purpose Truck Time of Day Model Truck Vehicle Trip Tables by Time Period External Trip Generation and Distribution Models External Vehicle Trip Tables by Time Period Figure 1.1. Four-step modeling process.

5 the highway network, resulting in traffic volumes on network links by time of day and, perhaps, vehicle type. Speed and travel time estimates, which reflect the levels of congestion indicated by link volumes, are also output. The transit assignment process routes trips from the transit trip tables onto individual transit routes and links, resulting in transit line volumes and station/ stop boardings and alightings. Because of the simplification associated with and the resul- tant error introduced by the sequential process, there is some- times “feedback” introduced into the process, as indicated by the upward arrows in Figure 1.1 (Travel Model Improvement Program, 2009). Feedback of travel times is often required, particularly in congested areas (usually these are larger urban areas), where the levels of congestion, especially for forecast scenarios, may be unknown at the beginning of the process. An iterative process using output travel times is used to rerun the input steps until a convergence is reached between input and output times. Because simple iteration (using travel time outputs from one iteration directly as inputs into the next iteration) may not converge quickly (or at all), averaging of results among iterations is often employed. Alternative approaches include the method of successive averages, constant weights applied to each iteration, and the Evans algorithm (Evans, 1976). Although there are a few different methods for implement- ing the iterative feedback process, they do not employ param- eters that are transferable, and so feedback methods are not discussed in this report. However, analysts should be aware that many of the analysis procedures discussed in the report that use travel times as inputs (for example, trip distribution and mode choice) are affected by changes in travel times that may result from the use of feedback methods. 1.4 Summary of Techniques and Parameters Chapter 4 presents information on (1) the analytical tech- niques used in the various components of conventional travel demand models and (2) parameters for these mod- els obtained from typical models around the United States and from the 2009 NHTS. These parameters can be used by analysts for urban areas without sufficient local data to use in estimating model parameters and for areas that have already developed model parameters for reasonableness checking. While it is preferable to use model parameters that are based on local data, this may be impossible due to data or other resource limitations. In such cases, it is common practice to transfer parameters from other applicable models or data sets. Chapter 4 presents parameters that may be used in these cases, along with information about how these parameters can be used, and their limitations. 1.5 Model Validation and Reasonableness Checking Another important use of the information in this report will be for model validation and reasonableness checking. There are other recent sources for information on how the general process of model validation can be done. Chapter 5 provides basic guidance on model validation and reasonable- ness checking, with a specific focus on how to use the informa- tion in the report, particularly the information in Chapter 4. It is not intended to duplicate other reference material on validation but, rather, provide an overview on validation consistent with the other sources. 1.6 Advanced Travel Analysis Procedures The techniques and parameters discussed in this report focus on conventional modeling procedures (the four-step process). However, there have been many recent advances in travel modeling methods, and some urban areas, especially larger areas, have started to use more advanced approaches to modeling. Chapter 6 introduces concepts of advanced model- ing procedures, such as activity-based models, dynamic traffic assignment models, and traffic simulation models. It is not intended to provide comprehensive documentation of these advanced models but rather to describe how they work and how they differ from the conventional models discussed in the rest of the report. 1.7 Case Study Applications One of the valuable features in NCHRP Report 365 was the inclusion of a case study to illustrate the application of the parameters and techniques contained in it. In this report, two case studies are presented to illustrate the use of the information in two contexts: one for a smaller urban area and one for a larger urban area with a multimodal travel model. These case studies are presented in Chapter 7. 1.8 Glossary of Terms Used in This Report MPO—Metropolitan Planning Organization, the federally designated entity for transportation planning in an urban area. In most areas, the MPO is responsible for maintaining and running the travel model, although in some places, other agencies, such as the state department of transportation, may have that responsibility. In this report, the term “MPO” is sometimes used to refer to the agency responsible for the model, although it is recognized that, in some areas, this agency is not officially the MPO.

6Model area—The area covered by the travel demand model being referred to. Often, but not always, this is the area under the jurisdiction of the MPO. The boundary of the model area is referred to as the cordon. Trips that cross the cordon are called external trips; modeling of external trips is discussed in Section 4.6. Person trip—A one-way trip made by a person by any mode from an origin to a destination, usually assumed to be without stops. In many models, person trips are the units used in all model steps through mode choice. Person trips are the usual units in transit assignment, but person trips are converted to vehicle trips for highway assignment. Trip attraction—In four-step models, the trip end of a home-based trip that occurs at the nonhome location, or the destination end of a nonhome-based trip. Trip production—In four-step models, the trip end of a home-based trip that occurs at the home, or the origin end of a nonhome-based trip. Vehicle trip—A trip made by a motorized vehicle from an origin to a destination, usually assumed to be without stops. It may be associated with a more-than-one-person trip (for example, in a carpool). Vehicle trips are the usual units in highway assignment, sometimes categorized by the number of passengers per vehicle. In some models, vehicle trips are used as the units of travel throughout the modeling process. Motorized and nonmotorized trips—Motorized trips are the subset of person trips that are made by auto or transit, as opposed to walking or bicycling trips, which are referred to as nonmotorized trips. In-vehicle time—The total time on a person trip that is spent in a vehicle. For auto trips, this is the time spent in the auto and does not include walk access/egress time. For transit trips, this is the time spent in the transit vehicle and does not include walk access/egress time, wait time, or time spent transferring between vehicles. Usually, transit auto access/ egress time is considered in-vehicle time. Out-of-vehicle time—The total time on a person trip that is not spent in a vehicle. For auto trips, this is usually the walk access/egress time. For transit trips, this is the walk access/ egress time, wait time, and time spent transferring between vehicles. In some models, components of out-of-vehicle time are considered separately, while in others, a single out-of- vehicle time variable is used.

TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.

The report presents a range of approaches that are designed to allow users to determine the level of detail and sophistication in selecting modeling and analysis techniques based on their situations. The report addresses techniques, optional use of default parameters, and includes references to other more sophisticated techniques.

Errata: Table C.4, Coefficients for Four U.S. Logit Vehicle Availability Models in the print and electronic versions of the publications of NCHRP Report 716 should be replaced with the revised Table C.4 .

NCHRP Report 716 is an update to NCHRP Report 365 : Travel Estimation Techniques for Urban Planning .

In January 2014 TRB released NCHRP Report 735 : Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models , which supplements NCHRP Report 716.

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Trip Generation Based on Land Use Characteristics: A Review of the Techniques Used in Recent Years

• Conference paper
• First Online: 19 December 2023
• Cite this conference paper

• Saumya Anand   ORCID: orcid.org/0000-0003-4976-6392 15 ,
• Pritikana Das   ORCID: orcid.org/0000-0002-8274-6513 15 &
• G. R. Bivina   ORCID: orcid.org/0000-0002-9923-3386 15  

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 434))

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• International Conference on Transportation Planning and Implementation Methodologies for Developing Countries

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The inadequate transportation facilities and growing population reflect the necessity of travel demand forecasting in developing nations. Forecasting travel demand serves as the foundation for planning and policy development that helps a country's economy grow. The trip generation process is the first step in the travel demand modelling process.. The accuracy of modelling trip generation is heavily reliant on the accuracy of two stages- data collection stage and generation of the model which depends on different modelling techniques used. The first and most important step in Trip Generation is data collection. Data from household trips is critical for both managing the existing transportation network and planning and designing future facilities. For many decades, household travel surveys (HTS) have been used as a time-consuming and costly method of data collection. New technologies emerge as alternatives to HTS as time passes, but HTS remains the most commonly used technique in developing countries. Data analysis techniques are the second important step in Trip generation modelling. Unlike existing modelling techniques, i.e., regression and category analysis, new modelling trip generation techniques involving machine learning have been developed in developing countries in recent years. A brief overview of data collection and modelling techniques used in developing and developed countries is provided as a comparative study.

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Saumya Anand, Pritikana Das & G. R. Bivina

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Avijit Maji

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Anand, S., Das, P., Bivina, G.R. (2024). Trip Generation Based on Land Use Characteristics: A Review of the Techniques Used in Recent Years. In: Singh, D., Maji, A., Karmarkar, O., Gupta, M., Velaga, N.R., Debbarma, S. (eds) Transportation Research. TPMDC 2022. Lecture Notes in Civil Engineering, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-99-6090-3_29

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DOI : https://doi.org/10.1007/978-981-99-6090-3_29

Published : 19 December 2023

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