Traditional Travel Demand Models Research Articles

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

Investigation of Freight Agents’ Interaction Considering Partner Selection and Joint Decision Making

Freight transportation plays an increasingly important role in sustainable development. However, freight travel demand has not been understood comprehensively, due to its unique features: freight activities are the result of collaboration among freight agents. It distinguishes freight transportation from passenger transportation, in which travel decisions are made mostly by individuals. Specifically, two processes in the collaboration can be observed: partner selection and joint decision making. Using the supplier-customer collaboration as an example, partner selection is a process for suppliers and customers to evaluate their potential partners and select the best one. Joint decision making allows suppliers and customers to seek common interests and make compromises. As a traditional travel demand model cannot model the two processes effectively, this research develops an innovative econometric model, spatial matching model, to bridge the gap. The proposed model is specified based on freight agents’ behavioral, estimated by Bayesian MCMC methods, and demonstrated by numerical examples. The proposed model and estimation methods can recover the coefficient values in the econometric models, and establish the relationship between the influential factors and the observed matching behavior. The analysis improves the understanding of freight travel demand in a behavioral-consistent manner and enriches the body of freight demand modeling literature.

Modeling the Socio-Economic Impacts of Covid-19 Using an Improved Traditional Travel Demand Model

The emergence of the COVID-19 virus and subsequent pandemic has caused unprecedented disruption across many aspects of life across the globe. The implications have necessitated a re-evaluation of travel demand models to include the effects that mass isolation and quarantines have on roadway networks. The present paper is focused on data and methodologies to change travel demand models to account for differences encountered during the COVID-19 pandemic. Increased telecommunicating, coupled with store closures and restrictions, within the majority of business categories, has drastically changed traffic patterns. The results indicate that travel has decreased significantly, as businesses have altered their operating practices, requiring employees to work from home due to the restrictions implemented by the government. The present methodology considers a variety of potential alterations to travel patterns resulting from the presence of COVID-19. Because different amounts of reduced employment and increased telecommuting are considered, the present methodology and associated analysis show that different strategies proposed or implemented by the government can be modeled. The results provided in this paper, and the associated model, thus provide a means to account for the new traffic modelling reality resulting from the presence of COVID-19. Such travel modeling is important in regard to forecasts of future travel demand, associated transportation planning, allocation of present and future transportation resources, resulting economic benefits and challenges, and the well-being of our society, as an operationally efficient transportation network, and the associated quality of life, are maintained.

Understanding mode choice decisions for shopping mall trips in metro cities of developing countries

The traditional travel demand model for an urban region generally considers work trips as the major constituent which subsequently attracts the maximum focus on mode choice analysis. However, with the increase in traffic-related problems coupled with the evolving lifestyle of people in general, it has become imperative to focus on trips intended for other purposes as well. In most of the urban scenarios, shopping trips constitute the second most frequented trips after work trips. The present study aims to develop a mode choice model exclusively for shopping malls in Mumbai city and determine the factors which influence a trip maker’s behavior of mode choice in the context of a developing country. Choice of mode for shopping mall trips is important in relation to the impact of the vehicular traffic generated from the shopping malls on the adjacent road networks as well as in the planning of the parking space allocations at the malls along with providing critical insights on the influential factors to the transport planners for policy recommendation. Revealed Preference (RP) survey technique is used to collect 650 samples of data regarding trip makers’ choice of mode to shopping malls by interviewing individuals at various shopping malls across Mumbai. A Multinomial Logit (MNL) model is initially developed, followed by a Nested Logit (NL) model by grouping the private modes in a single nest. The significance level of the variables used in both the models are mostly consistent. Both the models reveal that travel time has a significant role in the mode choice behavior. While walking time and access time notably affect the utility of public transport mode, factors like number of accompanying persons and driving license possession also influence the private modes significantly. Socio-demographic characteristics like age, gender and occupation are also found to be critical in the mode choice behavior for shopping mall trips. A comparative analysis of the MNL and NL models reveals that the NL model outperforms the MNL model both in terms of goodness of fit and prediction success rate. However, both the models (MNL with a predictive success rate of over 72% and NL with a predictive success rate of about 77%) fairly captures the factors affecting the mode choice behavior for trips to shopping malls. The study can help policy makers to understand the factors affecting the choice of mode for such trips and can aid in planning strategies to minimize traffic congestion resulting from the increasing number of shopping malls.

GPU-based Parallel Computing for Activity-based Travel Demand Models

Activity-based travel demand models (ABMs) are gaining popularity in the field of traffic modeling because of their high level of detail compared to traditional travel demand models. Due to this, however, ABMs have high computational requirements, making ABMs hard to use for analysis and optimization purposes. We address this problem by relying on the concept of parallel computing using a computer’s graphics processing unit (GPU). To illustrate the potential of GPU computing for ABM, we present a pilot study in which we compare the observed computation time of an ABM GPU implementation that we built using NVIDIA’s CUDA framework with similar, non-parallel implementations. We conclude that speed-ups up to a factor 50 can be realized, enabling the use of ABMs both for fast analysis of scenarios and for optimization purposes.

Estimating Current and Potential Bicycle Use for Statewide Planning

As interest in bicycling for transportation and recreation has increased, state transportation agencies and other planning organizations must prioritize ways in which to spend their limited resources. This paper describes a geographic information system–based analysis that categorizes state-owned highways across Vermont on the basis of potential bicycle use through the leverage of readily available data. This methodology allows a planning agency to make decisions on the basis of statewide information and avoids the costs of a traditional travel demand model. It informs decision makers about where to invest additional funds to improve on-road bicycle facilities and to prioritize maintenance to maximize value. It also provides the groundwork for bicycle-related performance standards and to identify critical gaps in high-demand bicycle corridors.

A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa, Florida

Activity-based travel demand modeling (ABTDM) has often been viewed as an advanced approach, due to its higher fidelity and better policy sensitivity. However, a review of the literature indicates that no study has been undertaken to investigate quantitatively the differences and accuracy between an ABTDM approach and a traditional four-step travel demand model. In this paper we provide a comparative analysis against each step – trip generation, trip distribution, mode split, and network assignment – between an ABTDM developed using travel diary data from the Tampa Bay Region in Florida and the Tampa Bay Regional Planning Model (TBRPM), an existing traditional four-step model for the same area. Results show salient differences between the TBRPM and the ABTDM, in terms of modeling performance and accuracy, in each of the four steps. For example, trip production rates calculated from the travel diary data are found to be either double or a quarter less than those used in the TBRPM. On the other hand, trip attraction rates computed from activity-based travel simulations are found to be either more than double or one tenth less than those used in the TBRPM. The trip distribution curves from the two models are similar, but both average and peak trip lengths of the two are significantly different. Mode split analyses show that the TBRPM may underestimate driving trips and fail to capture any usage of alternative modes, such as taxi and nonmotorized (e.g., walking and bicycling) modes. In addition, the ABTDMs are found to be less capable of reproducing observed traffic counts when compared to the TBRPM, most likely due to not considering external and through trips. The comparative results presented can help transportation engineers and planners better understand the strengths and weaknesses of the two types of model and this should assist decision-makers in choosing a better modeling tool for their planning initiatives.

Computation Model of Network Scale for Urban Rail Transit Based on Mutualism Model

In order to compute the network scale of urban rail transit, the paper proposed a mutualist model describing the network scale, through investigating the main influencing factor of network scale-the population and the relationship between population and network. The equilibrium point of different equations was given practical meaning.calibrating the key physical quantities, the nonlinear differential equations have been solved by using Runge–Kutta. Applying the model to predict the population and network scale in main city in Hefei in 2020 and long future, compared with the traditional travel demand model and network service coverage model. The result are basically accordant, so the new model is feasible.The theory model provides a new means of quantitative analysis method for urban rail transit network planning, So it has important theoretical and practical significance.

TRANSIMS Implementation for a Small Network and Comparison with Enhanced Four-Step Model

Travel demand forecasting is a major tool to assist decision makers in transportation planning. While the conventional four-step trip-based approach is the dominant method to perform travel demand analysis, behavioral advances have been made in the past decade. This paper proposes and applies an enhancemnt to the four-step travel demand analysis model called Sub-TAZ. Furthermore, as an initial step toward activity-based models, a TRANSIMS Track-1 approach is implemented utilizing a detailed network developed in Sub-TAZ approach. The conventional four-step, Sub-TAZ, and TRANSIMS models were estimated in a small case study for Fort Meade, Maryland, with zonal trip tables. The models were calibrated and validated for the base year (2005), and the forecasted results for the year (2010) were compared to actual ground counts of traffic volume and speed. The study evaluated the forecasting ability of TRANSIMS versus the conventional and enhanced four-step models and provided critical observations concerning strategies for the further implementation of TRANSIMS.BACKGROUND Traffic pattern prediction is necessary for infrastructure improvement, and travel demand modeling provides tools to forecast travel patterns under various conditions. This modeling involves a series of mathematical equations that represent how people make travel choices. Traditional travel demand models use the four-step method, which was introduced in the 1950s and has been used widely in transportation planning. Although the four-step method has been practical in producing aggregate forecasts, it has some shortcomings. For example, in short-range planning networks, existing and newly constructed roads become congested much faster than forecasted (TRB 2007) and the performance of current four-step models is not always satisfactory. Additionally, these models are not behavioral in nature and as a result they are unable to represent the time chosen for travel, travelers’ responses to demand policies (e.g., toll roads, road pricing, and transit vouchers), non-motorized

Challenges and Opportunities in Applying High-Fidelity Travel Demand Model for Improved Network-Wide Traffic Estimation: A Review and Discussion

Abstract: Estimating traffic volume at a link level is important to transportation planners, traffic engineers, and policy makers. More specifically, this vital parameter has been used in transportation planning, traffic operations, highway geometric design, pavement design, and resource allocation. However, traditional factor approach, regression-­‐based models, and artificial neural network models failed to present network-­‐wide traffic volume estimates because they rely on traffic counts for model development, and they all have inherent weaknesses. A review to previous research work and the state-­‐of-­‐practice clearly indicates that the Traditional Four-step Travel Demand Model (TFTDM) was generally based on large traffic analysis zones (TAZs) and networks consisting of high functional-class roads only. Consequently, this conventional modeling framework yielded a limited number of link traffic assignments with fairly high estimation errors. In the light of these facts and the obvious need of accurate network-wide traffic estimates, this review is conducted. In particular, this paper provides an extensive review of using traditional travel demand models for improved network-­‐wide traffic volume estimation. The paper then focuses on the challenges and opportunities in achieving high-fidelity travel demand model (HFTDM). This review has revealed that, opportunities in relation to both technological advances and intelligent data present a substantial potential in developing the proposed HFTDM for a much more accurate traffic estimation at a network-­‐wide level. Finally, the paper concludes with key findings from the review and provides a few recommendations for future research related to the topic.

Integrating a traffic router and microsimulator into a land use and travel demand model

This paper describes one of the first known attempts at integrating a dynamic and disaggregated land-use model with a traffic microsimulator and compares its predictions of land use to those from an integration of the same land-use model with a more traditional four-step travel demand model. For our study area of Chittenden County, Vermont, we used a 40-year simulation beginning in 1990. Predicted differences in residential units between models for 2030 broken down by town correlated significantly with predicted differences in accessibility. The two towns with the greatest predicted differences in land use and accessibility are also the towns that currently have the most severe traffic bottlenecks and poorest route redundancy. Our results suggest that this particular integration of a microsimulator with a disaggregated land-use model is technically feasible, but that in the context of an isolated, small metropolitan area, the differences in predicted land use are small.

Estimating Mobile-Source Greenhouse Gas Emissions in Los Angeles County, California: Web-Based Tool for Calculation of Benefits of Transportation Improvement Projects for Reduced Emissions

California's Sustainable Communities and Climate Protection Act of 2008 (Senate Bill 375, enacted in 2008) requires jurisdictions to identify changes to policies and systems to reduce mobile source greenhouse gas (GHG) emissions. As a result, local public officials and planners need flexible tools beyond traditional travel demand models to estimate the impacts of transportation policy and investment decisions on carbon emissions. Although several tools and calculators offer high-level estimates of reductions in GHG emissions in multiple sectors, the available tools and calculations generally lack local contexts and sensitivities, which are the primary drivers of the potential to reduce GHG emissions at the project level. Given this analytic need, the Los Angeles County Metropolitan Transportation Authority (LACMTA), California, recognized the connection between the data estimation needs of the legislation and the existing calculation capabilities of the web-based platform for its congestion mitigation fee program. The agency partnered with Cambridge Systematics to develop a tool to estimate the project-level impacts on the carbon emissions of a diverse range of local projects included in the congestion mitigation fee program. The tools for sketch planning developed for LACMTA offer easy data entry, a transparent framework, a web-accessible interface, and immediate reporting of results while remaining sensitive to local conditions. The tool's accessible design was central to its successful use in the development of the sustainable communities strategy of the Gateway Cities Council of Governments. This paper presents the policy context that guided tool development; highlights of the development process and methodology; a summary of the testing, delivery, and application of the sustainable communities strategy of the Gateway Cities Council of Governments; and a view of the tool's limitations and opportunity for enhancements.

Mind the Gap

Airport employees often account for a substantial share of daily airport access trips, yet current planning for airport landside access in the United States focuses almost exclusively on identifying and addressing the needs of air passengers. Airport worker travel patterns are normally studied by using the standard journey-to-work components of travel demand models and generally do not capture the unique factors affecting this group's commuting behavior. The Port Authority Trans-Hudson (PATH) commissioned a preliminary study evaluating the economic feasibility of extending PATH rail service to Newark Liberty International Airport, New Jersey. An empirical analysis of survey data collected during this effort suggests that the traditional travel demand model approaches to studying airport worker commuting patterns often mischaracterize the travel patterns and attitudes of a large airport access market segment. This research finds that the omission of airport worker input in the planning process is likely to result in the overestimation of public transit demand and may only exacerbate the optimism bias that has typically affected rail transportation investments in the United States.

Estimating Transborder Trips Integrating Binational Communities as Single Transportation System: Learning from Binational Model for El Paso, Texas–Ciudad Juarez, Mexico

This research presents the application of the TRANUS integrated land use and travel demand modeling software to estimate transborder origin–destination (O-D) pairs by developing a travel demand model (TDM) that integrates the two border communities in a binational conurbation into a single transportation and economic system. Commercial and personal ties generate a tremendous amount of transborder traffic within binational conurbations along the U.S.–Mexican border. With so much interaction, individual transportation networks from each country become a single transportation system connected through the ports of entry. Conventionally, each side of the border is modeled separately or through the use of external zones—the area is not treated as a single, interconnected system. This research presents a methodology to estimate transborder O-D pairs and traffic flows with TRANUS. The case study of a binational TDM covering the El Paso, Texas–Ciudad Juarez, Mexico conurbation is presented. Results demonstrate encouraging findings from using TRANUS to estimate transborder O-D pairs for private trips. Furthermore, the methodology developed can be replicated in any conurbation along the U.S.–Mexican border with a relatively limited data collection effort. Although traditional TDMs require extensive and expensive household surveys, which are difficult to conduct in binational settings, this research shows that it is possible to generate a reasonably reliable set of binational O-D matrices at a significantly lower cost and time investment by using TRANUS.

A Bayesian approach for modeling origin–destination matrices

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis–Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows.

Neural Network-Based Combined Synthetic Model of Trip Generation and Distribution

Some direct synthetic models for trip generation, distribution and model choice have been previously proposed in order to substitute the traditional four-step sequential travel demand model. However, the previous model based approach has limitations in real world applications where we have to deal with a large number of zones and various parameters effecting travel demand. In this study, we propose a neural network-based combined model for trip generation, distribution and modal spilt. The experimental results showed that the proposed direct demand model using the neural network model is an attractive proposition, particularly in areas where we have to deal with a large number of zones to determine future trips.

A comparative analysis of using trip-based versus link-based traffic data for regional mobile source emissions estimation

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis.

Development of an Empirically Based Area-Type Model

Area type is often used in traditional four-step travel demand models as a predictive variable. Most often it is a factor in mode choice, but it is also commonly used in trip generation as well. Yet a reliable, fore-castable, and measurable definition of area type is generally not documented in most travel demand models. In fact, there is little literature on the subject of predicting area type in the context of travel demand models. This can be troublesome when models are applied to long-range forecasts where significant changes in population and employment result in changes in the general character of an area. With Delphi methods, geographic information system analysis, and discriminant analysis, this study documents the development of an empirically based area-type model in the Tampa Bay region. Because in many ways Tampa Bay's urban form is typical of many cities in the United States, the results should be readily transferable to other urban areas.

Exploration of Long-Distance Interregional Commuting Issues: Analysis of Northern California Interregional Commuters Using Census Data and Focus Group Interviews

Recent national and local travel surveys indicate substantial increases in types of travel that are not well explained by most applications of traditional travel demand models. Understanding phenomena such as nonwork travel, the travel patterns of nontraditional households, or long-distance interregional commuting requires new basic research. A promising approach to primary travel demand research was developed and is described. A case study of interregional commuting in Northern California that combined quantitative analysis of census and travel survey data with focus group techniques is presented. Census data provided the basis for defining various types of commuters; the characteristics of these types were then elaborated via the focused interviews including “rolling focus groups” aboard commuter vans. Approximately 40 commuters were interviewed in depth. The interviews illuminated important issues that clarify the census and travel survey data. The findings indicate that an affordable single-family home is the primary motivation of interregional commuters; a variety of personal, household, and schedule adaptations make commuting palatable; and key prerequisites for interregional commuting include a job and lifestyle that permit adherence to a routine. Interregional commuters appear to be good candidates for shared-ride modes.

Exploration of Long-Distance Interregional Commuting Issues: Analysis of Northern California Interregional Commuters Using Census Data and Focus Group Interviews

Recent national and local travel surveys indicate substantial increases in types of travel that are not well explained by most applications of traditional travel demand models. Understanding phenomena such as nonwork travel, the travel patterns of nontraditional households, or long-distance interregional commuting requires new basic research. A promising approach to primary travel demand research was developed and is described. A case study of interregional commuting in Northern California that combined quantitative analysis of census and travel survey data with focus group techniques is presented. Census data provided the basis for defining various types of commuters; the characteristics of these types were then elaborated via the focused interviews including rolling focus groups aboard commuter vans. Approximately 40 commuters were interviewed in depth. The interviews illuminated important issues that clarify the census and travel survey data. The findings indicate that an affordable single-family hom...