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

Chapter overview.

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

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

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

Learning Objectives

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

Introduction

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

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

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

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

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

9.2 Four-step Model

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Trip Generation

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

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

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

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

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

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

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

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

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

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

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

Trip Distribution

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

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

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

T ij = trips produced at I and attracted at j

P i = total trip production at I

A j = total trip attraction at j

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

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

C= calibration factor for the friction factor

K ij = a socioeconomic adjustment factor for interchange ij

i = origin zone

n = number of zones

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

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

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

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

Mode choice

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

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

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

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

A generalized modal split chart is depicted in Figure 9.5.

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

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

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

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

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

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

b= Dispersion Parameter measuring sensitivity to cost.

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

Trip assignment

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

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

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

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

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

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

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

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

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

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

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

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

t= link travel time per length unit

t 0 =free-flow travel time

v=link flow

c=link capacity

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

Model improvement

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

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

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

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

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

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

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

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

A Simple Example of the FSM model

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

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

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

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

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

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

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

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

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

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

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

Table 9.9-Travel cost table (skim tree)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways

In this chapter, we covered:

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

Prep/quiz/assessments

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

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

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

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

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

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

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

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

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

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

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

Kneebone, E., & Berube, A. (2013). Confronting suburban poverty in America . Brookings Institution Press.

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

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

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.

Meyer, M. D., & Institute Of Transportation Engineers. (2016).  Transportation planning handbook . Wiley.

Mladenovic, M., & Trifunovic, A. (2014). The shortcomings of the conventional four step travel demand forecasting process. Journal of Road and Traffic Engineering , 60 (1), 5–12.

Mitchell, R. B., and C. Rapkin. (1954). Urban traffic: A function of land use . Columbia University Press. https://doi.org/10.7312/mitc94522

Rahman, M. S. (2008). “ Understanding the linkages of travel behavior with socioeconomic characteristics and spatial Environments in Dhaka City and urban transport policy applications .” Hiroshima: (Master’s thesis, Hiroshima University.) Graduate School for International Development and Cooperation. http://sr-milan.tripod.com/Master_Thesis.pdf

Rodrigue, J., Comtois, C., & Slack, B. (2020). The geography of transport systems . London ; New York Routledge.

Shen, Q. (1998). Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environment and Planning B: Planning and Design , 25 (3), 345–365.

Sharifiasl, S., Kharel, S., & Pan, Q. (2023). Incorporating job competition and matching to an indicator-based transportation equity analysis for auto and transit in Dallas-Fort Worth Area. Transportation Research Record , 03611981231167424. https://doi.org/10.1177/03611981231167424

Weiner, Edward. 1997. Urban transportation planning in the United States: An historical overview . US Department of Transportation. https://rosap.ntl.bts.gov/view/dot/13691

Xiongbing, J,  Grammenos, F. (2013, May, 21) . Taking the Guesswork out of Designing for Walkability. Planetizen .  https://www.planetizen.com/node/63248

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

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

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

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

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TF Resource

Travel Forecasting, Explained

A collection of best practices for travel demand forecasting and travel survey methods.

Table of Contents

State of the practice

This site collects the latest and most practical methods and tools available for travel modelers.

More than a wiki: many pages are written and reviewed by leaders in our industry, including members of the TRB ADB45 committee.

We welcome content from the entire travel forecasting community. Please help us keep the content fresh!

# What is the Travel Forecasting Resource ?

This project came about due to a TRB research project designed to examine the state-of-the-practice in metropolitan travel forecasting.

In the final report, Metropolitan Travel Forecasting: Current Practice and Future Direction , the committee identified many recommendations to improve travel demand forecasting, including a national travel forecasting handbook to be developed and kept current.

This suggestion was taken up by the forecasting community, and this TFResource is that handbook! We hope you find it useful.

Acknowledgements

Thank you to the Transportation Research Board and to the volunteers of the ADB45 Travel Forecasting Resources committee, without whom none of this content would exist.

This website graciously funded by the USDOT-sponsored Tier-1 TOMNET University Transportation Center at Arizona State University.

Website design by Billy Charlton from Because LLC using VuePress

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TRAVEL DEMAND MODELLING: Evolution of travel demand forecasting, new paradigms and technologies

transform transport

July 20th | Theme: Travel Demand Modeling

Evolution of travel demand forecasting, new paradigms and technologies.

Curator: Alessandro Vacca, Systematica

Moderator: Jonelle Hanson, Systematica

Register in advance for this meeting . After registering, you will receive a confirmation email containing information about joining the meeting.

Transport modelling has always played a decisive role in supporting the decision-making process, providing evidence-based analysis and a robust technical response to planning transportation infrastructure. This recently gained importance as cities and mobility requirements evolved rapidly and the increase of new technologies trigger a shift in transportation modes and cater for people’s changing behaviors and needs. In this session, we question how can transport modelling respond to the rapid changes our cities are going through and how can we take full advantage of the evolving data collection technologies and constantly update and revisit the modelling approaches and techniques? This talk is dedicated to provide some insights on how travel demand modelling is evolving and what modellers, practitioners and researchers will be asked to answer to in the coming years for making the process faster, more effective and more dynamic.

significance of travel demand forecasting in everyday traffic

Vince Bernardin, PhD, Vice President, Travel Demand Analytics Caliper

significance of travel demand forecasting in everyday traffic

Dr. Vince Bernardin is a Vice President of Caliper Corporation based in Indiana.  Vince has project experience in twenty-seven states and three continents developing and applying statewide, urban, and corridor-level travel forecasting models for both plan development and major project studies. He has managed and contributed to the development of more statewide models than anyone else.  He is best known for his pioneering work with big data and for his development of innovative modeling approaches.  Dr. Bernardin has been at the forefront of data-driven travel modeling and forecasting using big data for a decade. He was the first to use big data for statewide modeling (2010) and activity-based modeling (2016). He has worked with nearly every major source of big data in transportation, including both aggregate data products (StreetLight, AirSage, HERE) and raw disaggregate (ATRI, Safegraph, Veraset, Cuebiq, INRIX) big data.  He is an active member of TRB, currently serving as Chair of the Urban Big Data Subcommittee and previously serving as Chair of the TRB Planning Applications Conference as well as a member of several standing committees.  Vince holds a BA in Philosophy from the University of Notre Dame, and an MS and Ph.D.in Transportation Engineering from Northwestern University.

Leta Huntsinger, PhD, PE, Director of Research, Systems Planning and Analysis Institute for Transportation Research and Education (ITRE)

significance of travel demand forecasting in everyday traffic

Dr. Leta Huntsinger is a 30-year transportation professional with extensive experience in travel modeling, transportation planning, and project management including experience in the public sector, private sector, and academia. She is a core leader within ITRE with responsibilities spanning research, technical services, and training. She is an Adjunct Associate Professor in the Department for Civil, Construction, and Environmental Engineering at NC State, and a Professor of Practice in the Department of City and Regional Planning at UNC-Chapel Hill where she engages in her passion for teaching and mentoring. In her time away from work, Leta enjoys running, hiking, biking, and kayaking.

Luis Willumsen, PhD, Managing Partner Nommon Solutions and Technologies

significance of travel demand forecasting in everyday traffic

Dr. Luis Willumsen has over 35 years of experience as a consultant, transport modeller and planner. He is co-author of “Modelling Transport” a book published by Wiley and now in its fourth edition. He has also published “Better Traffic and Revenue Forecasting”, dealing with the critical task of delivering demand and revenue projections for transport concessions. He was a researcher and lecturer at Leeds University and University College London. He was a Board Director of Steer Davies Gleave for 20 years leaving in 2009 to develop his own consultancy: Willumsen Advisory Services. He is also a Managing Partner of Nommon Solutions and Technologies, a company specialised in the use of smartcard and mobile phone data to deliver trip matrices and useful transport insights. Through both companies he has focussed his work on the future impact of new mobility, Agent Based models and how best to deal with deep uncertainty and risk. He is also Visiting Professor in the Department of Civil, Environmental & Geomatic Engineering at University College London.

Eduardo Espitia, Transportation Consultant Systematica

significance of travel demand forecasting in everyday traffic

Eduardo Espitia is a transport engineer working with Systematica since 2018. He has been involved in multimodal transport modelling, policy assessment, and demand forecasting at different scales, from masterplans to Sustainable Urban Mobility Plans and National Transport Models.

National Academies Press: OpenBook

Travel Demand Forecasting: Parameters and Techniques (2012)

Chapter: chapter 7 - case studies.

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.

100 7.1 Introduction As discussed in Chapter 5, there are two primary uses for the data provided in this report: • Developing travel model components when no local data suitable for model estimation are available and • Checking the reasonableness of model components devel- oped using local data. In the first case, local data should be collected to validate the models or model components developed based on this report. In the second case, the data in this report can be used to supplement and support the validation and reasonableness checking process. This chapter presents two case studies to illustrate the use of the report for these purposes. In the first case study, the MPO for a large metropolitan area, Gtown, has recently conducted a household activity/travel survey, and has recalibrated its model using the new data. The information from this report is used to verify that the model parameters and results from this recalibration are reasonable. Note that this case study does not represent the entire validation effort for such a model, which must include other checks (for example, sensitivity tests and checks of forecasts). The second case study is for a small urban area, Schultzville, that has never had a travel forecasting model and does not have any area-specific travel data. The MPO for this area has borrowed the model structure from another small area and is using that structure to develop a model for its area. 7.2 Model Reasonableness Check Gtown is a large metropolitan area with more than 5 million residents and a diverse public transportation system that includes various rail and bus services. A household activity/ travel survey was completed 3 years ago; and data from that survey, transit surveys, and traffic counts have been used by MPO staff to recalibrate the trip-based travel forecasting model for the area. The MPO staff wants to make sure that the newly calibrated model is reasonable and has decided to compare model parameters and selected model results with information contained in this report. In this section, parameters from the recalibrated Gtown model are compared to those provided in Chapter 4 of this report. The information provided in Chapter 4 often does not use the same variables or uses them at different levels of aggregation. Therefore, throughout this section, either parameters from Chapter 4 or the Gtown data are aggregated to make them comparable. One prime example of this dif- ference relates to trip purpose. The Gtown model has five trip purposes: home-based work (HBW), home-based shop (HBS), home-based other (HBO), nonhome-based work (NHBW), and nonhome-based other (NHBO). Parameters and data in Chapter 4 are provided for three purposes: HBW, home-based nonwork (HBNW), and nonhome based (NHB) (alternatively, for four purposes, including home-based school, but this purpose is not used in the Gtown model). Therefore, for Gtown parameters to be compared to those in this report, the Gtown data for the five trip purposes must be collapsed to the classic three trip purposes. 7.2.1 Trip Generation Trip Production Rates Trip production rates for Gtown for all trip purposes are applied using a three-dimensional, cross-classification model with household size, number of vehicles, and income level as variables. All person trips are modeled, including non- motorized trips. Table C.5 in Appendix C provides HBW trip rates derived from NHTS data, based on three different cross-classifications; two of which are household size by number of vehicles and C h a p t e r 7 Case Studies

101 household size by income level. However, the income defini- tions in the Gtown model are significantly different than those in the NHTS data summaries. It was therefore decided to com- pare the rates using the household size by number of vehicles classification, as shown in the middle section of Table C.5. Table 7.1 shows this comparison. Note that the Gtown model uses only four household size categories (the largest is 4 or more persons), while the NHTS data summary in Table C.5 uses five categories (the largest is 5 or more persons). As shown in Table 7.1, the Gtown trip production rate is 1.7 HBW trips per household, compared to 1.4 trips per household from Chapter 4; a difference of about 20 percent. This difference seems to be concentrated in smaller households, which predominantly are childless households. The Gtown MPO theorized that the difference may be due to a lower than average rate of retired people living in the region. In addition, Gtown has higher than average transit usage, and there may be more direct trips between home and work than in other areas since auto trips are more likely to include stops on the way to or from work (leading to more HBNW and NHB trips in place of HBW trips). The basic question for the MPO is whether the trip rates derived from their local survey are more reliable than those from the NHTS, which has a higher sample size but is a national sample collected mostly outside Gtown. Certainly, the difference indicates that checks of the Gtown survey data are warranted. Table C.6 provides HBNW trip rates derived from NHTS data, based on three different cross-classifications, two of which are household size by number of vehicles and household size by income level. Separate rates are presented for areas with populations more than 500,000 and less than 500,000. The appropriate rates to use for this comparison are those for the areas of less than 500,000. It was decided to compare the rates using the household size by number of vehicles classification, as shown in the third section of Table C.6. Table 7.2 shows this comparison. As shown in Table 7.2, the Gtown trip production rate is 4.6 HBNW trips per household, compared to 5.6 trips per household from Table C.6; a difference of nearly 20 percent. For HBNW trips, the differences seem to be across all house- hold size and vehicle availability categories. Again, the differ- ences indicate that further checks of the Gtown survey data are warranted. Table C.7 provides NHB trip rates derived from NHTS data, based on three different cross-classifications, two of which are household size by number of vehicles and household size by income level. It was decided to compare the rates using the household size by number of vehicles classification, as shown in the middle section of Table C.7. Table 7.3 shows this comparison. As shown in Table 7.3, the Gtown trip production rate is 2.3 NHB trips per household, compared to 3.0 trips per household from Table C.7; a difference of nearly 25 percent. For NHB trips, the differences seem to be across most house- hold size and vehicle availability categories, although the differences are higher in larger households. Again, the differ- ences indicate that further checks of the Gtown survey data are warranted. NHTS Data (from Table C.5) Autos Persons 1 2 3 4 5+ Average 0 0.2 0.7 1.1 1.0 0.9 0.5 1 0.6 0.8 1.2 1.7 1.5 0.8 2 0.7 1.3 2.0 2.0 2.3 1.6 3+ 0.9 1.4 2.6 2.9 3.3 2.3 Average 0.5 1.2 2.0 2.3 2.4 1.4 Gtown Trip Rates Autos Persons 1 2 3 4 Average 0 0.9 1.3 1.4 1.5 1.1 1 0.9 1.4 1.8 1.8 1.3 2 1.0 1.6 2.0 2.1 1.8 3+ 1.0 1.7 2.4 2.7 2.2 Average 0.9 1.5 2.1 2.2 1.7 Table 7.1. Comparison of Gtown HBW trip production rates to NHTS data from Table C.5.

102 NHTS Data (from Table C.6) Vehicles Household Size 1 2 3 4 5+ Average 0 1.4 3.8 5.6 7.5 10.0 3.2 1 1.9 3.9 6.5 9.0 11.8 3.7 2 2.4 4.0 6.5 11.0 14.0 6.8 3+ 2.5 4.0 7.3 11.0 14.5 8.6 Average 1.8 4.0 6.7 10.6 13.4 5.6 Gtown Trip Rates Autos Persons 1 2 3 4 Average 0 1.6 2.3 2.9 3.4 1.9 1 1.6 3.2 4.4 7.4 2.8 2 1.7 3.3 5.4 8.3 5.1 3+ 1.9 3.4 5.5 9.2 6.2 Average 1.6 3.2 5.1 8.4 4.6 Table 7.2. Comparison of Gtown HBNW trip production rates to NHTS data from Table C.6. NHTS Data (from Table C.7) Vehicles Household Size 1 2 3 4 5+ Average 0 0.7 1.7 2.0 3.7 3.9 1.3 1 1.4 2.3 3.5 3.9 3.9 2.0 2 1.6 2.6 3.9 5.5 5.6 3.5 3+ 1.6 2.7 4.5 5.8 7.1 4.4 Average 1.3 2.5 3.8 5.3 5.7 3.0 Gtown Trip Rates Autos Persons 1 2 3 4 Average 0 1.0 1.3 1.7 2.1 1.2 1 1.4 2.0 2.3 3.1 1.8 2 1.7 2.1 2.3 3.2 2.5 3+ 2.0 2.4 2.5 3.6 2.9 Average 1.5 2.1 2.3 3.2 2.3 Table 7.3. Comparison of Gtown NHB trip production rates to NHTS data from Table C.7.

103 When total trips per household by all purposes from the Gtown model are compared to the information presented in Tables C.5 through C.7, the overall rate for Gtown is 8.6 trips per household, 14 percent lower than the total of 10.0 trips per household derived from the NHTS in Chapter 4. Based on this analysis, Gtown rates are lower than the national average. NHTS rates are averages based on urban areas with different characteristics, and the rates for individual areas can be dif- ferent. Furthermore, the higher Gtown rate for HBW trips, which are generally longer, may compensate for the lower overall rate. Trip Attraction Rates Table 4.4 summarizes average trip attraction rates from the MPO Documentation Database for the classic three trip purposes. The Gtown trip attraction model differs from the models shown in Table 4.4 in several ways. First, the employ- ment categories used for the Gtown HBNW and NHB attrac- tion models are defined differently than those in Table 4.4. For comparison purposes, the categories in the Gtown model were redefined to approximate those shown in Table 4.4. Second, the Gtown model stratifies trip attraction rates by area type. Weighted averages of Gtown’s area type-specific models were used to compare to the models in Table 4.4. The resulting comparison of trip attraction models is shown in Table 7.4. The models chosen for comparison from Table 4.4 were Model 1 for HBW, Model 3 for HBNW, and Model 2 for NHB. As can be seen in Table 7.4, the Gtown trip attraction rates are lower than the rates shown in Table 4.4, especially those for HBNW trips. The Gtown trip attraction models will generate fewer attractions than the models shown in Table 4.4. Since trip attractions are typically balanced to match produc- tions, the effects of the lower trip attraction rates might be small, but it makes sense to further check the trip attraction model estimation results, as well as the balancing of produc- tions and attractions. If the balancing process requires factoring up attractions to match productions, perhaps the rates could be adjusted upward. 7.2.2 Trip Distribution The reasonableness of the Gtown trip distribution model can be assessed by comparing the friction factors used in the Gtown gravity model and the resulting average trip lengths with comparable values provided in Section 4.5. Average Trip Length Table C.10 provides average trip length by mode (travel times in minutes) for urban areas of different sizes. The Gtown model results should be compared to the figures from Table C.10 cor- responding to areas of “1 million or more with subway or rail.” The Gtown trip distribution model produces a compos- ite travel time that reflects highway and transit travel times. Table 7.5 compares the average trip times for all modes by trip purpose from Table C.10 and compares those trip lengths to the times resulting from the Gtown model. The average trip duration for HBW trips from the Gtown model is 48 minutes, compared to an average HBW trip duration from the NHTS of 32 minutes. While most large metropolitan areas experience high levels of congestion during peak hours, the Gtown highway network is very congested during the peak periods, which can last 4 or more hours. Since most HBW trips are made during the peak periods, it can be expected that the travel time for those trips will be longer in Gtown than in other areas with a popula- tion over 3 million. Furthermore, Gtown encompasses a very large geographic area, also contributing to longer work trips. Another consideration is that Gtown has a relatively high transit share, and transit trips are longer than auto trips, as shown in Table C.10. Households Employment Basic Retail Service Total Home-Based Work Gtown Model 0.9 Model 1 from Table 4.4 1.2 Home-Based Nonwork Gtown Model 0.4 0.9 3.4 Model 3 from Table 4.4 0.7 0.7 8.4 3.5 Nonhome Based Gtown Model 0.1 3.3 0.7 Model 2 from Table 4.4 1.4 6.9 0.9 Table 7.4. Comparison of Gtown trip attraction rates to those shown in Table 4.4.

104 Nonetheless, the large discrepancy between the Gtown average trip length for HBW trips and that of other large areas does warrant some further review. The 48-minute average travel time resulting from the model was compared to the time reported in the household travel survey and the 2000 CTPP. The average travel time reported for HBW trips in the house- hold survey was also 48 minutes; and in the 2000 CTPP, it was 45 minutes, thus, confirming the modeled time. The average travel time for HBNW and NHB trips result- ing from the Gtown model compared more favorably to those shown in Table C.10. The mean HBNW travel time for Gtown is 17 minutes, compared to 18 minutes from the NHTS data. NHB travel times also compared favorably with both the Gtown and NHTS averages at approximately 20 minutes. The total travel time for all trips is 24 minutes from the Gtown model, which is 2 minutes longer than the time reported in Table C.10. If the Gtown trip generation rates and travel times are viewed together, they seem more reasonable. Studies have shown that people will only travel a certain amount of time for all pur- poses during a given day. Thus, the longer-than-usual amount of time spent making work trips can result in fewer and shorter trips for other purposes. Thus, the lower HBNW and NHB trip generation rates in the Gtown model may result from higher HBW trip rates and longer travel times. Gamma Function and Friction Factors The Gtown model distributes trips separately for each of four income groups and five purposes. A useful reasonableness check is to compare the Gtown estimated model parameters to those developed in other regions. The estimated friction factors calibrated for Gtown are represented by gamma functions that can be compared to those reported by areas of similar size. Table 4.5 provides trip distribution gamma func- tion parameters for eight MPOs, three of which are large. One way to compare friction factors used in the Gtown model to those resulting from the gamma functions for large MPOs in Table 4.5 is to compare the resulting graphs of friction factors to see if they are comparable. Figure 7.1 is a graph of the HBW friction factors for Gtown compared to those for the three large MPOs reported in Table 4.5. Friction factors for the three large MPOs and for the four HBW income groups in the Gtown model are shown in Figure 7.1. The Gtown friction factors for the two higher incomes are almost exactly the same as those for MPO 3. The friction factors for the two lower incomes are not as steep but are comparable to those for the three sample MPOs. Figure 7.2 is a graph of the HBS and HBO friction factors for Gtown compared to the HBNW friction factors for the three large MPOs. All of the Gtown friction factors lie between the values for MPO 1 and MPO 3, and the slopes for almost all purposes and income groups are very similar to that for MPO 1. Figure 7.3 is a graph of the NHB friction factors for Gtown compared to those for the three large MPOs reported in Table 4.5. The Gtown friction factors for NHBO trips are similar to the NHB values for MPO 2. The Gtown friction factors for NHBW trips are not as steep as those for any of the MPOs. Since neither the NHBO or the NHBW friction factors are as steep as those from any of the large MPOs, it is unlikely that friction factors for a combination of NHBO and NHBW trips would match the values for any of the MPOs. However, since the average travel times for NHB trips from the Gtown model are the same as those from the NHTS, the difference in friction factors may not be significant. 7.2.3 Mode Choice The Gtown model uses a nested logit mode choice model with coefficients for the classic three trip purposes. Auto submodes include drive alone and shared ride; and transit submodes include local, premium, and rail submodes (as well as separate models for auto and walk access). Variables used in the Gtown model include in-vehicle time, out-of-vehicle time, and a single cost variable. The coefficients of these vari- ables are summarized in Table 7.6. Tables 4.8, 4.11, and 4.14 present mode choice model parameters, by purpose, that are used by MPOs included in the MPO Documentation Database. For HBW trips, Models B, C, D, F, G, and I from Table 4.8, all of which are for urban areas All Modes (Minutes) Average All Trips HBW HBNW NHB Gtown 48 17 20 24 NHTS Averages from Table C.10 32 18 20 22 Difference 16 −1 0 2 Percentage Difference 50% −6% 0% 9% Table 7.5. Comparison of Gtown average trip length to NHTS data from Table C.10.

105 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 3 5 7 9 1 1 1 3 1 5 1 7 1 9 2 1 2 3 2 5 2 7 2 9 3 1 3 3 3 5 3 7 3 9 4 1 4 3 45 Travel time (min) MPO 2 MPO 1 MPO 3 MPO 3 Gtown Income 1 Gtown Income 2 Gtown Income 3 Gtown Income 3 Gtown Income 4 Gtown Income 4 Figure 7.1. Home-based work trip distribution friction factors. 0.1 1.0 10.0 100.0 1,000.0 10,000.0 100,000.0 1,000,000.0 10,000,000.0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 HBNW MPO 1 HBNW MPO 2 HBNW MPO 3 HBS Gtown Income 1 HBS Gtown Income 2 HBS Gtown Income 3 HBS Gtown Income 4 HBO Gtown Income 1 HBO Gtown Income 2 HBO Gtown Income 3 HBO Gtown Income 4 Travel time (min) Figure 7.2. Home-based nonwork trip distribution friction factors.

106 with populations of more than 1 million, have comparable variables to those in the Gtown model. Models F, G, and I are nested logit models. The coefficients of the Gtown HBW mode choice model are not too different from those of Models F, G, and I, although the Gtown cost coefficients are lower in absolute value. Looking at the relationships between coefficients, Table 7.7 shows that the ratio of the out-of-vehicle time and in- vehicle time coefficients in the Gtown model is comparable to those for Models F, G, and I, as shown in Table 4.9. The value of time in the Gtown model, however, is significantly higher than in the models from other areas. This compari- son holds for most of the other models shown in Tables 4.8 and 4.9. For HBNW trips, Models E, G, I, and K from Table 4.11 are for urban areas with populations of more than 1 million and have comparable variables. The in-vehicle time coefficient of the Gtown HBNW mode choice model is higher than those in the models from Table 4.11, while the Gtown cost coeffi- cients are lower in absolute value. Looking at the relationships between coefficients, Table 7.8 shows that the ratio of the out- of-vehicle time and in-vehicle time coefficients in the Gtown model is a bit lower than those of the other models, as shown in Table 4.12. The value of time in the Gtown model, however, is significantly higher than in the models from other areas. This comparison holds for most of the other models shown in Tables 4.11 and 4.12. For NHB travel, models F, G, and I from Table 4.14 are most comparable to Gtown. The coefficients in the Gtown HBNW mode choice model are fairly comparable. Looking at the relationships between coefficients, Table 7.9 shows that the ratio of the out-of-vehicle time and in-vehicle 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 3 5 7 9 11 13 1 Travel Time (min) 5 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 NHB MPO 1 NHB MPO 2 NHB MPO 3 NHW Gtown NHO Gtown Figure 7.3. Nonhome-based trip distribution friction factors. HBW HBNW NHB Parameter In-Vehicle Time Out-of-Vehicle Time Cost (low income) Cost (high income) Derived Relationships Out-of-Vehicle Time/ In-Vehicle Time Ratio 2.0 2.0 2.0 Value of In-Vehicle Time $9.08/hour (low income) $25.44/hour (high income) $8.80/hour (low income) $22.00/hour (high income) $1.76/hour HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome based. −0.0212 minute −0.022 minute −0.029 minute −0.043 minute −0.0449 minute −0.0572 minute −0.0014 cent −0.0015 cent −0.0099 cent −0.0005 cent −0.0006 cent −0.0099 cent Table 7.6. Gtown mode choice model parameters.

107 Model Out-of-Vehicle Time/ In-Vehicle Time Value of In-Vehicle Time Gtown 2.0 $9.08 to $25.44/hour Model F (Table 4.9) 2.0 $3.94/hour Model G (Table 4.9) 2.3 $3.05/hour Model I (Table 4.9) 2.0 $3.00/hour Table 7.7. Relationships between coefficients from home-based work mode choice models for Gtown and from Table 4.9. Model Out-of-Vehicle Time/ In-Vehicle Time Value of In-Vehicle Time Gtown 2.0 $8.80 to $22.00/hour Model E (Table 4.12) 3.0 $3.69/hour Model G (Table 4.12) 4.6 $0.21/hour Model I (Table 4.12) 3.1 $0.48/hour Model K (Table 4.12) 3.0 $1.40/hour Table 7.8. Relationships between coefficients from home-based nonwork mode choice models for Gtown and from Table 4.12. Model Out-of-Vehicle Time/ In-Vehicle Time Value of In-Vehicle Time Gtown 2.0 $1.75/hour Model F (Table 4.15) 2.0 $4.04/hour Model G (Table 4.15) 11.3 $0.46/hour Model I (Table 4.15) 2.1 $2.00/hour Table 7.9. Relationships between coefficients from nonhome-based mode choice models for Gtown and from Table 4.15. time coefficients and value of time in the Gtown model are (as shown in Table 4.15) fairly comparable to those in Models F and I, but Model G appears to be an outlier. The other models shown in Tables 4.14 and 4.15 have coefficient values that vary widely, but the coefficients from Gtown fit well within this range. In summary, the value of time, indicating the willingness to pay for travel timesavings by switching modes, seems high for home-based trips in the Gtown model. The related model coefficients, mainly the cost coefficients for these trip purposes, should be reviewed. 7.2.4 Automobile Occupancy The Gtown mode choice model forecasts auto driver and auto passenger trips by purpose separately. Table 7.10 provides a comparison of the resulting Gtown auto occupancy rates compared to the values reported from the NHTS in Table 4.16. As Table 7.10 shows, the Gtown home-based auto occupan- cies are within 5 percent of those from the NHTS. Gtown NHB auto occupancies are noticeably lower than those from the NHTS. The NHB mode choice model should be checked regarding how auto driver and passenger choices are made. HBW HBNW Nonhome Based All Trips NHBW NHBO Gtown 1.05 1.64 1.10 1.48 1.39 Table 4.16 1.10 1.72 1.66 1.55 HBW = home-based work; HBNW = home-based nonwork; NHBW = nonhome-based work; NHBO = nonhome-based other. Table 7.10. Comparison of average daily vehicle occupancy by trip purpose.

108 The household survey is another source against which auto occupancy rates by purpose can be checked. 7.2.5 Time of Day Table 7.11 provides a comparison between the modeled times of day for auto trips in the Gtown model with those derived from NHTS data that are shown in Table C.11. As Table 7.11 shows, the percentage of travel occurring in peak periods is lower in Gtown than in the national sur- vey, and the nighttime percentage of travel is substantially higher in Gtown. As mentioned earlier, the Gtown highway system is very congested, and the peaks are much longer than in other comparable cities. It would seem reasonable, therefore, that peak spreading would be more prevalent in Gtown. This finding could be confirmed using other data sources such as traffic counts. 7.2.6 Summary This section provides a comparison of model parameters and results produced by the model for a hypothetical large MPO and the values in this report. Overall, the Gtown model parameters and results appear to be reasonable when compared to the values in Chapter 4 of the report, although some Gtown model parameters, such as cost coefficients in the mode choice models for home-based trip purposes, should be checked further. The congested nature of Gtown does appear to result in fewer nonwork trips, very long work trips, and extended peak periods. 7.3 Model Development Case Study for a Smaller Area without Data for Model Estimation This case study is for a small urban area that never had a travel forecasting model and does not have any local data from which to estimate model parameters. The MPO for this hypothetical city, Schultzville, borrowed the model structure from another small area and used that structure to develop its own model. Schultzville is an urban area of about 100,000 people. It has very little in the way of pub- lic transportation, so the MPO decided to develop a daily (i.e., no time of day), three-step model with auto trips only, using the classic three trip purposes. 7.3.1 Zone and Highway Network Definition Highway Network Definition A highway network for the Schultzville area was developed to obtain acceptable volumes on minor arterials; therefore, collectors and local roads were included in the network. Digital street files available from the U.S. Census Bureau (TIGER/ Line files) were used to create the highway network shown in Figure 7.4. Freeways, major arterials, minor arterials, collector links, and some local roads were coded into the network. The following are examples of some of the fields coded for nodes and links in the network: Time Period Gtown Table C.11 Difference Percent Difference 14.4% 17.1% 34.4% 35.6% 27.4% 32.1% 23.8% 15.2% 8.6% 57% Total 100.0% 100.0% −2.7% −16% −1.2% −3% −4.7% −15% 6:00 a.m.−9:00 a.m. 9:00 a.m.−3:00 p.m. 3:00 p.m.−7:00 p.m. 7:00 p.m.−6:00 a.m. Table 7.11. Comparison of time of day for auto trips. Figure 7.4. Schultzville highway network.

109 • XY coordinates—Geographic coordinates for nodes; • Node identifiers (anode/bnode)—Unique numbers assigned to each end of a link; • Distance—Distance in miles between anode and bnode; • Functional (link) classification—Type of facility (e.g., major arterial, minor arterial, etc.); • Traffic count volume—Average daily volume of traffic on link (where available); • Number of lanes; • Facility type; • Area type—Location and development characteristics of area that link serves (e.g., urban, suburban, rural, etc.); and • Link capacity and free-flow speed—Link capacities are a function of the number of lanes on a link. Area type and facility type were used to define per-lane default capacities and speed. The number of lanes was also checked using field verification or aerial imagery to ensure accuracy. Transportation Analysis Zone Definition A map of Schultzville transportation analysis zones is shown in Figure 7.5. Each TAZ has a centroid, which is a point that represents all travel origins and destinations in a zone. 7.3.2 Socioeconomic Data Socioeconomic data—household and employment data for the modeled area—were organized into the TAZs. Esti- mates of base-year socioeconomic data by TAZ were devel- oped for use in model development. The population and household data for Schultzville came from the decennial census. Data such as income and vehicle availability were derived from the ACS. Basic socioeconomic data by TAZ were derived for Schultzville, including households, population, total employ- ment, retail employment, service employment, manufacturing employment, nonmanufacturing employment, and school enrollment. More detailed data, such as number of persons per household, household income, workers per household, and vehicles owned per household, as well as cross-classifications of households by zone, were also derived from the U.S. Census and ACS. Employment data by TAZ were derived from data pro- vided by the state employment commission. Each employer was identified by a federal identification number, number of employees, and a geocodable address, which were allocated to TAZs. Since these data were keyed to where the payroll is prepared for employees, the MPO made adjustments to allocate employment to the proper TAZ, where necessary. School enrollment data by school were provided by the Schultzville School District and allocated to the appropriate TAZs; this information was supplemented by information the MPO collected directly from the larger private schools in the region. 7.3.3 Trip Generation Trip Productions The MPO was able to develop estimates of households cross- classified by household size and number of vehicles, and by workers by number of vehicles for each zone. The information in Tables C.5 through C.7, which shows trip rates derived from 2009 NHTS data, was used to estimate productions by trip purpose. The HBNW trip rates for areas with less than 500,000 residents in Table C.6 were used. These trip generation rates were applied to the socioeconomic data for each zone to create total productions by purpose by zone. An example calculation is provided for home-based work trips in Table 7.12. Trip production rates from Table C.5 were multiplied by the households cross-classified by workers and vehicles to obtain a total of 1,092 HBW trip productions occurring in the sample zone. (Note that Table C.5 provides rates for households with three or more vehicles, while data for Schultzville were only available for households with two or more vehicles; therefore, the rates for two vehicle and three vehicle households were averaged for use in Schultzville.) Trip Attractions The values for trip attraction rates for motorized trips, shown in Table 4.4, were used as a trip attraction model for Schultzville. Model 1 from this table was used for each trip purpose. An example calculation is provided for home-based Figure 7.5. Schultzville TAZs.

110 work trips in Table 7.13. Data for households, employment, and school enrollment for each Schultzville TAZ were multiplied by the trip attraction rates from Table C.7 to achieve a total of 130 HBW, 583 HBNW, and 306 NHB trip attractions occur- ring in the sample zone. 7.3.4 Trip Distribution The doubly constrained gravity model, described in Equation 4-5, was used as the trip distribution model for Schultzville. The inputs to the trip distribution model include: • The trip generation outputs—productions and attractions by trip purpose for each zone; • Highway travel time, as the measure of travel cost between each pair of zones; and • Friction factors, as discussed in the following section. The outputs are trip tables, production zone to attraction zone, for each trip purpose. Because trips of different purposes have different levels of sensitivity to travel time and cost, trip distribution is applied separately for each trip purpose, with different model parameters. Development of Travel Time Inputs Zone-to-zone (interzonal) travel costs. This case study used the simplest cost variable, highway travel time, which is an Number of Autos Workers Total 0 1 2 3+ Home-Based Work Trip Production Rates 0 0.0 1.1 2.0 4.0 1 0.0 1.1 2.5 4.3 2+ 0.0 1.3 2.6 4.5 Example TAZ Data 0 20 30 10 0 1 65 155 75 4 2+ 4 90 170 24 Example Zone Trip Productions 0 0 33 20 0 1 0 171 188 17 2+ 0 116 442 106 Total Productions 0 319 650 123 1,092 Table 7.12. Example trip production calculation. Trip Pu rp os e Households School Enroll me nt Em ploy me nt Trip Attractions Basic Retail Service Total Ho me -Based Work Model 1 1.2 Sa mp le TA Z Va lu e 108 Tr ip A ttractions 130 130 Ho me -Based Nonwork Model 1 0.4 1.1 0.6 4.4 2.5 Sa mp le TA Z Va lu e 320 210 34 10 64 Tr ip A ttractions 128 231 20 44 160 583 Nonh om e Base d Model 1 0.6 0.7 2.6 1.0 Sa mp le TA Z Va lu e 320 34 10 64 Tr ip A ttractions 192 24 26 64 306 Total Trips Attr acted to Sa mp le TA Z 1,019 Table 7.13. Trip attractions calculation for sample TAZ.

111 adequate measure for a small area such as Schultzville. This area does not have a significant level of auto operating cost beyond typical per-mile costs—for example, relatively high parking costs or toll roads—or extensive transit service. The zone-to- zone highway travel time matrix was developed through “skim- ming” the highway network using travel modeling software. The highway assignment process does not require that times be coded on the centroid connectors since those links are hypo- thetical constructs representing the travel time within zones. Initial skim times from the network assignment did not include time representing travel within zones, or terminal time. Intrazonal time. Intrazonal times were defined as one- half of the average of the skim times to the three nearest neighboring zones. Terminal time. Terminal times, which represent the time required to park a vehicle and walk to the final destination, or vice versa, were added to the intrazonal time. Terminal times of 4 minutes were added to the time for any trip where a trip end was in the business district, and 2 minutes were added for trip ends elsewhere. Friction factors. Friction factors were derived for each purpose (HBW, HBNW, and NHB trips) using a gamma function (described in Equation 4-6) using the b and c values shown in Table 4.5 for Small MPO 1. The gamma func- tion parameters, including the scaling factor a, are shown in Table 7.14. The resulting friction factors are plotted in Figure 7.6. The resulting average travel times by trip purpose from this first application of the gravity model were evaluated to determine if the distribution was acceptable. Friction factors were calibrated to match average travel times using an iterative process. No local data existed regarding average travel times, so the best option in this situation was to start with parameters from another modeling context. Average trip lengths by trip purpose are presented in Table C.10, and were used as a basis of comparison with trip lengths resulting from the initial trip distribution in Table 7.15. As can be seen in Table 7.15, the average trip lengths resulting from this initial set of friction factors are lower than the average travel times reported in Table C.10. Since Schultzville is a small geographic area with little congestion, one might expect that the average trip length would be lower than the NHTS aver- age reported for all areas with a population less than 500,000. However, the initial mean travel times were judged too low. The initial friction factors were adjusted iteratively to test variations Parameter HBW HBNW NHB a 26,000 130,000 260,000 b c HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome based. −0.265 −1.017 −0.791 −0.04 −0.079 −0.195 Table 7.14. Gamma function parameters for Schultzville. 0.10 1.00 10.00 100.00 1,000.00 10,000.00 100,000.00 1,000,000.00 61 11 16 21 26 31 36 41 46 51 56 NHB HBNW HBW HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome based. Travel time (min) Figure 7.6. Schultzville case study initial friction factors.

112 that achieved a higher average trip length for all purposes. The friction factors resulting from this fitting process are shown in Figure 7.7. The comparison of the mean travel times resulting from the use of these revised friction factors with those from Table C.10 is shown in Table 7.16. The final friction factors are not as steep as those that were initially used and result in mean travel times closer to those shown in Table C.10. 7.3.5 External Trips The best source of data for estimating external trips (EI and EE) is a roadside survey conducted at external stations; however, no such survey was available for Schultzville. The state in which Schultzville is located has a statewide travel model that provided information on EE trips and EI trips for the study area. The statewide model provided the origin and destination station, as well as the volume for EE trips. For EI trips, a select link assignment from the statewide model provided the number of trips entering and leav- ing each external station allocated to the statewide model zones. These needed to be suballocated to the Schultzville model zones based on the relative internal attractions and productions in each TAZ compared to the total in the larger statewide model zones. 0.10 1.00 10.00 100.00 1,000.00 10,000.00 100,000.00 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 Initial HBW Initial HBNW Initial NHB Final HBW Final HBNW Final NHB HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome based. Travel time (min) Figure 7.7. Schultzville case study final friction factors. HBW HBNW NHB Urban Area Population from Table C.10 Less than 500,000 All population ranges Other urban area Value from Table C.10 20 minutes 18 minutes 18 minutes Schultzville 15 minutes 12 minutes 9 minutes Difference 5 minutes 6 minutes 9 minutes HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome based. Table 7.15. Initial evaluation of Schultzville mean travel times. Table 7.16. Evaluation of Schultzville mean travel times using adjusted friction factors. HBW HBNW NHB Urban Area Population from Table C.10 Less than 500,000 All population ranges Other urban area Value from Table C.10 20 17 3 minutes 18 15 3 minutes 18 15 3 minutes Schultzville minutes minutes minutes Difference minutes minutes minutes HBW = home-based work; HBNW = home-based nonwork; NHB = nonhome based.

113 7.3.6 Vehicle Occupancy The highway assignment step, discussed in Section 7.3.7, requires tables of vehicle trips, while the output of early model steps was in person trips. Person trips made by auto from the earlier steps were converted to vehicle trips using the factors provided in the first row of Table 4.16, which represent all auto modes for daily travel. These factors—1.10 for HBW, 1.72 for HBNW, and 1.66 for NHB—were applied to the auto passenger trip tables produced by the trip distribution step, as described in Section 7.3.4. 7.3.7 Highway Assignment Trip tables from origins to destinations (O-D format) are required for the daily highway assignment; however, the HBW and HBNW trip tables resulting from the previous steps pro- vide trip tables from productions to attractions (P-A format). The P-A trip tables were converted to O-D trip tables by splitting the value in each cell in half to create two duplicate matrices, transposing the values in one of the matrices, and adding the two matrices together. The resulting O-D trip tables were then ready to be assigned to the highway network. A user equilibrium assignment using the BPR formula for capacity restraint was used for assigning vehicle trips to the highway network. Values for the a and b parameters were needed for application of the BPR formula (described in Section 4.11.1). Table 4.26 presents BPR function parameters used by 18 MPOs. The most appropriate values for Schultzville are those shown for areas with a population less than 200,000: a = 0.15 for freeways, 0.45 for arterials; and b = 8.8 for freeways, 5.6 for arterials. The results of the traffic assignment are shown as a band- width plot in Figure 7.8. In this diagram, the width of each link in the network is proportional to the volume on that link. An assessment was made of the quality of the traffic assign- ment on links where traffic counts were available by comparing the root mean square error (RMSE) of assigned values to traffic counts by facility type. As can be seen in Table 7.17, the RMSE is within an acceptable range for all facility types, except local roads. Since the goal of the model was to get acceptable values for minor arterials, the results were deemed acceptable. Figure 7.8. Schultzville case study final assigned volumes. Table 7.17. RMSE comparison of modeled volumes with traffic counts. Functional Class Links ADT Error Percentage Error Acceptable Error Freeways 18 228,340 15,021 6.6% +/−7% Principal Arterials 90 538,210 37,674 7.0% +/−10% Minor Arterials 226 730,030 80,303 11.0% +/−15% Collectors 218 304,110 66,904 22.0% +/−25% Locals 14 20,000 10,400 52.0% +/−25%

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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Travel Behavior and Demand Analysis and Prediction

  • Reference work entry
  • First Online: 17 April 2019
  • Cite this reference work entry

significance of travel demand forecasting in everyday traffic

  • Konstadinos G. Goulias 3  

Part of the book series: Encyclopedia of Complexity and Systems Science Series ((ECSSS))

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  • Originally published in
  • R. A. Meyers (ed.), Encyclopedia of Complexity and Systems Science , © Springer-Verlag 2009
  • https://doi.org/10.1007/978-0-387-30440-3_565

A modeling method that accounts for the interdependent relationships among activities and persons to derive travel demand equations.

The incorporation of trends, cycles, and feedback mechanisms into a process of actively shaping our future. Desired futures are first defined in terms of performance measures and a combination of forecasting and backcasting methods are used to identify the right paths to follow in achieving these futures.

A method to represent the movement in space and time of the most elementary units of a phenomenon. When applied in traffic engineering the units are vehicles. When applied in travel behavior the units are persons and households. Multi-agent microsimulation allows to also represent human interaction with each person modeled as an agent.

The amount of travel within a time interval such as number of trips in a day, total amount of distance and total amount of travel time,...

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Goulias, K.G. (2009). Travel Behavior and Demand Analysis and Prediction. In: Kerner, B. (eds) Complex Dynamics of Traffic Management. Encyclopedia of Complexity and Systems Science Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8763-4_565

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Demand Forecasting: Concept, Significance, Objectives and Factors

significance of travel demand forecasting in everyday traffic

An organization faces several internal and external risks, such as high competition, failure of technology, labor unrest, inflation, recession, and change in government laws.

Therefore, most of the business decisions of an organization are made under the conditions of risk and uncertainty.

An organization can lessen the adverse effects of risks by determining the demand or sales prospects for its products and services in future. Demand forecasting is a systematic process that involves anticipating the demand for the product and services of an organization in future under a set of uncontrollable and competitive forces.

Some of the popular definitions of demand forecasting are as follows:

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According to Evan J. Douglas, “Demand estimation (forecasting) may be defined as a process of finding values for demand in future time periods.”

In the words of Cundiff and Still, “Demand forecasting is an estimate of sales during a specified future period based on proposed marketing plan and a set of particular uncontrollable and competitive forces.”

Demand forecasting enables an organization to take various business decisions, such as planning the production process, purchasing raw materials, managing funds, and deciding the price of the product. An organization can forecast demand by making own estimates called guess estimate or taking the help of specialized consultants or market research agencies. Let us discuss the significance of demand forecasting in the next section.

Significance of Demand Forecasting :

Demand plays a crucial role in the management of every business. It helps an organization to reduce risks involved in business activities and make important business decisions. Apart from this, demand forecasting provides an insight into the organization’s capital investment and expansion decisions.

The significance of demand forecasting is shown in the following points:

i. Fulfilling objectives:

Implies that every business unit starts with certain pre-decided objectives. Demand forecasting helps in fulfilling these objectives. An organization estimates the current demand for its products and services in the market and move forward to achieve the set goals.

For example, an organization has set a target of selling 50, 000 units of its products. In such a case, the organization would perform demand forecasting for its products. If the demand for the organization’s products is low, the organization would take corrective actions, so that the set objective can be achieved.

ii. Preparing the budget:

Plays a crucial role in making budget by estimating costs and expected revenues. For instance, an organization has forecasted that the demand for its product, which is priced at Rs. 10, would be 10, 00, 00 units. In such a case, the total expected revenue would be 10* 100000 = Rs. 10, 00, 000. In this way, demand forecasting enables organizations to prepare their budget.

iii. Stabilizing employment and production:

Helps an organization to control its production and recruitment activities. Producing according to the forecasted demand of products helps in avoiding the wastage of the resources of an organization. This further helps an organization to hire human resource according to requirement. For example, if an organization expects a rise in the demand for its products, it may opt for extra labor to fulfill the increased demand.

iv. Expanding organizations:

Implies that demand forecasting helps in deciding about the expansion of the business of the organization. If the expected demand for products is higher, then the organization may plan to expand further. On the other hand, if the demand for products is expected to fall, the organization may cut down the investment in the business.

v. Taking Management Decisions:

Helps in making critical decisions, such as deciding the plant capacity, determining the requirement of raw material, and ensuring the availability of labor and capital.

vi. Evaluating Performance:

Helps in making corrections. For example, if the demand for an organization’s products is less, it may take corrective actions and improve the level of demand by enhancing the quality of its products or spending more on advertisements.

vii. Helping Government:

Enables the government to coordinate import and export activities and plan international trade.

Objectives of Demand Forecasting :

Demand forecasting constitutes an important part in making crucial business decisions.

The objectives of demand forecasting are divided into short and long-term objectives, which are shown in Figure-1:

Objectives of Demand Forecasting

The objectives of demand forecasting (as shown in Figure-1) are discussed as follows:

i. Short-term Objectives:

Include the following:

a. Formulating production policy:

Helps in covering the gap between the demand and supply of the product. The demand forecasting helps in estimating the requirement of raw material in future, so that the regular supply of raw material can be maintained. It further helps in maximum utilization of resources as operations are planned according to forecasts. Similarly, human resource requirements are easily met with the help of demand forecasting.

b. Formulating price policy:

Refers to one of the most important objectives of demand forecasting. An organization sets prices of its products according to their demand. For example, if an economy enters into depression or recession phase, the demand for products falls. In such a case, the organization sets low prices of its products.

c. Controlling sales:

Helps in setting sales targets, which act as a basis for evaluating sales performance. An organization make demand forecasts for different regions and fix sales targets for each region accordingly.

d. Arranging finance:

Implies that the financial requirements of the enterprise are estimated with the help of demand forecasting. This helps in ensuring proper liquidity within the organization.

ii. Long-term Objectives:

a. Deciding the production capacity:

Implies that with the help of demand forecasting, an organization can determine the size of the plant required for production. The size of the plant should conform to the sales requirement of the organization.

b. Planning long-term activities:

Implies that demand forecasting helps in planning for long term. For example, if the forecasted demand for the organization’s products is high, then it may plan to invest in various expansion and development projects in the long term.

Factors Influencing Demand Forecasting:

Demand forecasting is a proactive process that helps in determining what products are needed where, when, and in what quantities. There are a number of factors that affect demand forecasting.

Some of the factors that influence demand forecasting are shown in Figure-2:

Factors Affecting Demand Forecasting

The various factors that influence demand forecasting (“as shown in Figure-2) are explained as follows:

i. Types of Goods:

Affect the demand forecasting process to a larger extent. Goods can be producer’s goods, consumer goods, or services. Apart from this, goods can be established and new goods. Established goods are those goods which already exist in the market, whereas new goods are those which are yet to be introduced in the market.

Information regarding the demand, substitutes and level of competition of goods is known only in case of established goods. On the other hand, it is difficult to forecast demand for the new goods. Therefore, forecasting is different for different types of goods.

ii. Competition Level:

Influence the process of demand forecasting. In a highly competitive market, demand for products also depend on the number of competitors existing in the market. Moreover, in a highly competitive market, there is always a risk of new entrants. In such a case, demand forecasting becomes difficult and challenging.

iii. Price of Goods:

Acts as a major factor that influences the demand forecasting process. The demand forecasts of organizations are highly affected by change in their pricing policies. In such a scenario, it is difficult to estimate the exact demand of products.

iv. Level of Technology:

Constitutes an important factor in obtaining reliable demand forecasts. If there is a rapid change in technology, the existing technology or products may become obsolete. For example, there is a high decline in the demand of floppy disks with the introduction of compact disks (CDs) and pen drives for saving data in computer. In such a case, it is difficult to forecast demand for existing products in future.

v. Economic Viewpoint:

Play a crucial role in obtaining demand forecasts. For example, if there is a positive development in an economy, such as globalization and high level of investment, the demand forecasts of organizations would also be positive.

Apart from aforementioned factors, following are some of the other important factors that influence demand forecasting:

a. Time Period of Forecasts:

Act as a crucial factor that affect demand forecasting. The accuracy of demand forecasting depends on its time period.

Forecasts can be of three types, which are explained as follows:

1. Short Period Forecasts:

Refer to the forecasts that are generally for one year and based upon the judgment of the experienced staff. Short period forecasts are important for deciding the production policy, price policy, credit policy, and distribution policy of the organization.

2. Long Period Forecasts:

Refer to the forecasts that are for a period of 5-10 years and based on scientific analysis and statistical methods. The forecasts help in deciding about the introduction of a new product, expansion of the business, or requirement of extra funds.

3. Very Long Period Forecasts:

Refer to the forecasts that are for a period of more than 10 years. These forecasts are carried to determine the growth of population, development of the economy, political situation in a country, and changes in international trade in future.

Among the aforementioned forecasts, short period forecast deals with deviation in long period forecast. Therefore, short period forecasts are more accurate than long period forecasts.

4. Level of Forecasts:

Influences demand forecasting to a larger extent. A demand forecast can be carried at three levels, namely, macro level, industry level, and firm level. At macro level, forecasts are undertaken for general economic conditions, such as industrial production and allocation of national income. At the industry level, forecasts are prepared by trade associations and based on the statistical data.

Moreover, at the industry level, forecasts deal with products whose sales are dependent on the specific policy of a particular industry. On the other hand, at the firm level, forecasts are done to estimate the demand of those products whose sales depends on the specific policy of a particular firm. A firm considers various factors, such as changes in income, consumer’s tastes and preferences, technology, and competitive strategies, while forecasting demand for its products.

5. Nature of Forecasts:

Constitutes an important factor that affects demand forecasting. A forecast can be specific or general. A general forecast provides a global picture of business environment, while a specific forecast provides an insight into the business environment in which an organization operates. Generally, organizations opt for both the forecasts together because over-generalization restricts accurate estimation of demand and too specific information provides an inadequate basis for planning and execution.

Steps of Demand Forecasting:

The Demand forecasting process of an organization can be effective only when it is conducted systematically and scientifically.

It involves a number of steps, which are shown in Figure-3:

Process of Demand Forecasting

The steps involved in demand forecasting (as shown in Figure-3) are explained as follows:

1. Setting the Objective:

Refers to first and foremost step of the demand forecasting process. An organization needs to clearly state the purpose of demand forecasting before initiating it.

Setting objective of demand forecasting involves the following:

a. Deciding the time period of forecasting whether an organization should opt for short-term forecasting or long-term forecasting

b. Deciding whether to forecast the overall demand for a product in the market or only- for the organizations own products

c. Deciding whether to forecast the demand for the whole market or for the segment of the market

d. Deciding whether to forecast the market share of the organization

2. Determining Time Period:

Involves deciding the time perspective for demand forecasting. Demand can be forecasted for a long period or short period. In the short run, determinants of demand may not change significantly or may remain constant, whereas in the long run, there is a significant change in the determinants of demand. Therefore, an organization determines the time period on the basis of its set objectives.

3. Selecting a Method for Demand Forecasting:

Constitutes one of the most important steps of the demand forecasting process Demand can be forecasted by using various methods. The method of demand forecasting differs from organization to organization depending on the purpose of forecasting, time frame, and data requirement and its availability. Selecting the suitable method is necessary for saving time and cost and ensuring the reliability of the data.

4. Collecting Data:

Requires gathering primary or secondary data. Primary’ data refers to the data that is collected by researchers through observation, interviews, and questionnaires for a particular research. On the other hand, secondary data refers to the data that is collected in the past; but can be utilized in the present scenario/research work.

5. Estimating Results:

Involves making an estimate of the forecasted demand for predetermined years. The results should be easily interpreted and presented in a usable form. The results should be easy to understand by the readers or management of the organization.

Related Articles:

  • 7 Criteria for Efficient Demand Forecasting
  • Beginners’ Guide to Demand Forecasting | Managerial Economics
  • Term Paper on Demand Forecasting | Economics
  • Cross Elasticity of Demand: Measurement, Types and Significance

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  3. Travel Demand Model

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  4. TPB's Four-Step Travel Model

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  5. Travel Demand Forecasting

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  6. Travel Demand Forecasting

    significance of travel demand forecasting in everyday traffic

VIDEO

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  3. Webinar 2

  4. Future of Transport with Anna Stewart, Reporter, CNN

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COMMENTS

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  2. Travel Demand Forecasting: Parameters and Techniques

    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 ...

  3. PDF Travel Demand Forecasting: Some Foundations and A Review

    Thus, the present state of the art of travel demand forecasting with a few exceptions allows only amount of travel to vary, i.e., to be the dependent variable. [Some demand models have been formulated and calibrated that forecast (long-run) residential location, car ownership, and modal split in one equation set (1, 30).

  4. Travel Demand Forecasting

    Travel Demand Forecasting is the process used to predict travel behavior and resulting demand for a specific future time frame, based on assumptions dealing with landuse, the number and character of tripmakers, and the nature of the transportation system. Travel demand forecasting utilizes a travel forecasting model and attempts to answer ...

  5. Travel Demand Forecasting: Parameters and Techniques

    Read Free Online. Buy Paperback: $67.00. 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. [read full description]

  6. An Overview Based on the Overall Architecture of Traffic Forecasting

    The contributions of this work are listed as follows: 1. We summarize and analyze traffic forecasting tasks by considering the overall architecture, including traffic data analysis, traffic data modeling, and traffic forecasting applications. 2. We review the open datasets and source resources for traffic forecasting.

  7. Forecasting Travel Demand

    This chapter describes methods and applications of travel demand forecasting techniques. It focuses on urban applications. The chapter discusses the basic principles, including a review of the commonly used four-step modeling paradigm, forecasting Transportation Demand Management (TDM), impacts and how forecasts can be applied to traffic impact analyses (TIAs).

  8. Demand Forecasting in Transport: Overview and Modeling Advances

    Transport demand forecasting models can be generally categorized according to the. steps involved in the traditional four-stage transport planning process (s ee Figure 1). These. steps include: (a ...

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    C O N T E N T S 1 Chapter 1 Introduction 1 1.1 Background 2 1.2 Travel Demand Forecasting: Trends and Issues 3 1.3 Overview of the Four-Step Travel Modeling Process 5 1.4 Summary of Techniques and Parameters 5 1.5 Model Validation and Reasonableness Checking 5 1.6 Advanced Travel Analysis Procedures 5 1.7 Case Study Applications 5 1.8 Glossary ...

  10. Introduction

    Introduction. Travel Demand Forecasting is a key component of the transportation engineer's technical repertoire. It allows the engineer to predict the volume of traffic that will use a given transportation element in the future, whether that element is an existing highway or a potential light-rail route. Like many other 'predictive ...

  11. Travel Demand Forecasting User Guide

    This document supplements the Project Development, Leadership, and Oversight discussion of Travel Demand Forecasting in the Regional Transitway Guidelines by providing additional information for topics discussed in the Guidelines. 1. INTRODUCTION. Travel demand modeling is an essential part of transportation planning for transitway investments.

  12. Improving the approaches of traffic demand forecasting in the big data

    Trip-based travel demand forecasting methods usually divide the travel forecasting unit in the form of "aggregate" (traffic zone), focusing on the population and the land use of the traffic zone. These methods usually take into account spatial coordination at the urban level, but ignore the actual travel needs and feelings of individual ...

  13. Full article: How accurate is the regional travel demand model in

    Travel demand forecasting is a process which helps in the estimation of the future trends in travel patterns as well as the travel times, using current trends in travel patterns and travel times. ... The significance value obtained for the sample size of 920 from the K-S test is greater than 0.05, indicating that the travel times are normally ...

  14. TF Resource

    What is the Travel Forecasting Resource ? This project came about due to a TRB research project designed to examine the state-of-the-practice in metropolitan travel forecasting. In the final report, Metropolitan Travel Forecasting: Current Practice and Future Direction, the committee identified many recommendations to improve travel demand ...

  15. Forecasting Travel Demand

    This chapter describes methods and applications of travel demand forecasting techniques. It focuses on urban applications. The chapter discusses the basic principles, including a review of the commonly used four-step modeling paradigm, forecasting Transportation Demand Management (TDM), impacts and how forecasts can be applied to traffic impact analyses (TIAs).

  16. PDF Transportation Planning and Travel Demand Forecasting

    Relates the number of trips being produced from a zone or site by time period to the land use and demographic characteristics found at that location. Assumptions: Trip-making is a function of land use. Trips are made for specific purposes (work, recreation) Different trip types are made at different times of the day.

  17. TRAVEL DEMAND MODELLING: Evolution of travel demand forecasting, new

    July 20th | Theme: Travel Demand Modeling Evolution of travel demand forecasting, new paradigms and technologies Curator: Alessandro Vacca, Systematica Moderator: Jonelle Hanson, Systematica Register in advance for this meeting. After registering, you will receive a confirmation email containing information about joining the meeting. Transport modelling has always played a decisive role in ...

  18. Travel Demand Forecasting: Parameters and Techniques

    A household activity/ travel survey was completed 3 years ago; and data from that survey, transit surveys, and traffic counts have been used by MPO staff to recalibrate the trip-based travel forecasting model for the area.

  19. Demand Forecasting in Transport: Overview and Modeling Advances

    Theodore Tsekeris, Charalambos Tsekeris: Demand forecasting in transport: overview and … 86 where yik are the generated passenger trips at traffic zone i with purpose k, HHi is the number of households at zone i, RiI is the trip production rate of households of income group G (e.g., between 1000 - 1500 Euro) and fik is the proportion (%) of total trips to be

  20. PDF Chapter 5 Traffic Demand Forecast

    Chapter 5 Traffic Demand Forecast One of the important objectives of traffic demand forecast in a transportation master plan study is to examine the concepts and policies in proposed plans by numerically indicators. It is, thus, to check whether plans provide sufficient capacity and structure performs functionally and

  21. Travel Behavior and Demand Analysis and Prediction

    Thebasic ingredients of an activity based approach for travel demand analysis [5,84] are: a) explicit treatment of travel as derived demand , i. e., participation in activities such as work, shop, and leisure motivate travel but travel could also be an activity as well (e. g., taking a drive). These activities are viewed as episodes (i. e ...

  22. Travel Behavior and Demand Analysis and Prediction

    This idea of the project underlies one of the most exciting developments in activitybased approaches to travel demand analysis and forecasting because seemingly unrelated activity and trip episodes can be viewed as parts of a "big-picture" and given meaning and purpose completing in this way models of human agency and explaining resistance ...

  23. Demand Forecasting: Concept, Significance, Objectives and Factors

    An organization can forecast demand by making own estimates called guess estimate or taking the help of specialized consultants or market research agencies. Let us discuss the significance of demand forecasting in the next section. Significance of Demand Forecasting: Demand plays a crucial role in the management of every business.