Showing papers by "Ram M. Pendyala published in 2014"

Joint Model of Weekend Discretionary Activity Participation and Episode Duration

Abstract: Research on travel demand modeling has primarily focused on weekday activity–travel patterns. However, weekend activities and travel constitute a major component of individuals’ overall weekly activity–travel participation. This paper describes a modeling effort that focuses on weekend activity–travel demand for discretionary events. This study bridges the gap in the literature by modeling participation in discretionary types of events, the duration of participation, and accompaniment type jointly in a simultaneous equations model system. A joint discrete–continuous modeling framework is formulated for analysis of these dimensions as a choice bundle. Specifically, the combination of event type and accompaniment type constitutes the discrete component, whereas the duration of participation constitutes the continuous component. The model uses a copula-based sample selection approach that ties the discrete choice error component with the duration error component in a flexible manner. The data used in the paper are drawn from the 2008–2009 National Household Travel Survey sample of the greater Phoenix metropolitan area in Arizona. The results from the estimation process highlight the presence of sample selection in the joint modeling context. Furthermore, the results also highlight the flexibility of copula models in capturing such sample selection. The best copula model results are used to generate hazard profiles for various alternative related duration intervals. The generated profiles highlight the inaccurate predictions obtained by the use of approaches that ignore the presence of sample selection.

Development of Vehicle Fleet Composition Model System for Implementation in Activity-Based Travel Model:

Abstract: The development of a vehicle fleet composition and utilization model system that may be incorporated into a larger activity-based travel demand model is described. It is of interest and importance to model household vehicle fleet composition and utilization behavior because the energy and environmental impacts of personal travel are dependent not only on the number of vehicles but also on the mix of vehicles that a household owns and the extent to which different vehicles are used. A vehicle composition (fleet mix) and utilization model system was developed for integration into the activity-based travel demand model that was being developed for the greater Phoenix metropolitan area in Arizona. At the heart of the vehicle fleet mix model system is a multiple discrete continuous extreme value model capable of simulating vehicle ownership and use patterns of households. Vehicle choices are defined by a combination of vehicle body type and age category and the model system is capable of predicting vehicle com...

Characteristics of Premium Transit Services that Affect Choice of Mode

Abstract: Traditionally, travel models use travel time and cost to assess the usefulness of each mode of transportation to make a particular trip. Other factors that affect the selection of mode are accounted for using a single constant term that represents other attributes. In many cases, these attributes represent conditions that may not be the same for all trips. Travel forecasting models would benefit by incorporating an expanded list of non-traditional attributes so that the probability of using transit to make a trip is more specifically related to the characteristics of a potential transit journey. Potential non-traditional transit characteristics include on-board and station amenities, reliability, span of service, and service visibility/ branding. These characteristics are not typically directly considered in travel forecasting models. This research sought to improve the understanding of the full range of determinants for transit travel behavior and to offer practical solutions to practitioners seeking to represent and distinguish transit characteristics in travel forecasting models. The key findings of this research include the value of non-traditional transit service attributes on travelers’ choice of mode, in particular the influence of awareness and consideration of transit service on modal alternatives, and the importance of traveler attitudes toward both awareness and consideration of transit and on the choice of transit or auto in mode choice. The appendices present detailed research results including a state-of-the-practice literature review, survey instruments, models estimated by the research team, model testing, and model implementation and calibration results. The models demonstrate an approach for including non-traditional transit service attributes in the representation of both transit supply (networks) and demand (mode choice models), reducing the magnitude of the modal specific constant term while maintaining the ability of the model to forecast ridership on specific transit services. The testing conducted in this project included replacing transit access and service modes, such as drive to light rail or walk to local bus, as alternatives in the mode choice model with transit alternatives defined by the elements of the path, such as a short walk to transit path, a no-transfer path, or a premium service path.

Multiple Discrete-Continuous Model of Activity Participation and Time Allocation for Home-Based Work Tours

Abstract: Activity-based travel demand models use the notion of tours or trip chains as the fundamental building blocks of daily traveler activity-travel patterns. Travelers may undertake a variety of tours during the course of a day, and each tour may include one or more stops where individuals participate in and devote time to the pursuit of activities. This paper presents a framework capable of simulating the complete composition of a tour and offers an approach to model the mix of activities and the time allocated to various activities in a tour. Embedded in the framework is a multiple discrete-continuous extreme value modeling component that was used to model the simultaneous decisions of participating in one or more activities in the course of a tour and of allocating time to each of the activities in the tour. The model was estimated with travel survey data collected in 2008 in the Greater Phoenix Metropolitan Area in Arizona. Validation and policy simulation exercises were conducted to examine the efficacy ...

Stochastic Frontier Estimation of Budgets for Kuhn-Tucker Demand Systems: Application to Activity Time-Use Analysis

Abstract: This paper proposes a stochastic frontier modeling approach to estimate budgets for the multiple discrete-continuous extreme value (MDCEV) model. The approach is useful when the underlying time and/or money budgets driving a choice situation are unobserved, but only the expenditures on the choice alternatives of interest are observed. Most MDCEV applications hitherto used the observed total expenditure on the choice alternatives as the budget to model expenditure allocation among different choice alternatives. This does not allow the possibility that changes in alternative attributes (such as prices) can lead to changes in total expenditure, but only allows a reallocation of the observed total expenditure among the different alternatives. The stochastic frontier approach helps address this issue by invoking the notion that consumers operate under latent budgets that can be conceived (and modeled) as the maximum possible expenditure they are willing to incur. The proposed method is applied to analyze the daily out-of-home activity participation and time-use patterns in a survey sample of non-working adults in Florida. First, a stochastic frontier regression is performed on the observed out-of-home activity time expenditure to estimate the unobserved out-of-home activity time frontier (OH-ATF). The estimated frontier is interpreted as a subjective limit or maximum possible time individuals can allocate to out-of-home activities and used as the time budget governing out-of-home time-use choices in an MDCEV model. Policy simulations demonstrate the value of the proposed method in allowing the total out-of-home activity time expenditure to either expand or shrink within the limit of the frontier implied by the stochastic frontier model. Substantive results suggest that land-use impacts on time-use choices are smaller than demographic impacts.

Analytical Travel Forecasting Approaches for Project-Level Planning and Design

Abstract: This report is an update to NCHRP Report 255: Highway Traffic Data for Urbanized Area Project Planning and Design and describes methods, data sources, and procedures for producing travel forecasts for highway project-level analyses. The report provides an evaluation of currently used methods and tools. The report also includes appropriate information sources and system-level methods (ranging from readily available practices to advanced practices) to address a variety of project development purposes, needs, and impacts. The report is intended to be used by transportation planning, operations, and project development staff to better support planning, design, and operations recommendations. The report is accompanied by a CD-ROM (CRP-CD-143) providing spreadsheet tools developed for project-level analyses as well as appendices from the contractor’s final report.

Extension of Activity-Based Modeling Approach to Incorporate Supply Side of Activities: Examples for Major Universities and Special Events

Abstract: Most of the recent advances in activity-based models (ABMs) have been on the demand side, that is, description of the individual needs for certain types of activities and travel as a function of person, household, and accessibility variables. The supply side of activities that describes characteristics of the locations where a certain activity can be undertaken remains largely unexplored. Two examples of specific activity generators for which the supply side of activity is essential for modeling are major universities and special events. Travel behavior and activity patterns of university students are different from that of the general population, and therefore modeling them with the necessary level of detail enhances the ABM forecasting ability. The same is true about special events such as sporting events. Special events participants can form a substantial part of the overall travel demand in the subarea on the event day, and therefore it is important to model them properly. Although previous studies ha...

Bayesian imputation of non‐chosen attribute values in revealed preference surveys

Abstract: Obtaining attribute values of non-chosen alternatives in a revealed preference context is challenging because non-chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non-chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non-chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non-chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non-chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones.

Characterizing Household Vehicle Fleet Composition and Count by Type in Integrated Modeling Framework

Abstract: There has been considerable interest, and consequent progress, in the modeling of household vehicle fleet composition and utilization in the travel behavior research domain. The multiple discrete-continuous extreme value (MDCEV) model is a modeling approach that has been applied frequently to characterize this choice behavior. One key drawback of the MDCEV modeling methodology is that it does not provide an estimate of the count of vehicles in each vehicle type alternative represented in the MDCEV model. Moreover, the classic limitations of the multinomial logit model, such as violations of the independence of irrelevant alternatives property in the presence of correlated alternatives and the inability to account for random taste variations, apply to the MDCEV model as well. A new methodological approach, developed to overcome these limitations, is applied in this paper to model vehicle fleet composition and count in each body type. The modeling methodology involves tying together a multiple discrete-cont...

Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator

Abstract: The application of a comprehensive model system of vehicle fleet composition and evolution is described; this model system is capable of taking a base-year vehicle fleet and making it evolve over time in annual time steps through the events of vehicle disposal, replacement, and acquisition. The model system is sensitive to a host of socioeconomic, demographic, built environment, and vehicle technology and price variables; this sensitivity makes it ideally suited for such an application. Coupled with a demographic forecasting model system that causes the population to evolve over time, the vehicle evolution simulator is able to predict changes in vehicle fleet composition, miles of travel, fuel consumption, and greenhouse gas emissions under a wide range of scenarios. On the basis of the findings from this study, future technological innovations (e.g., increase of driving range) and pricing levels (doubling of gas cost) will have greater impacts on vehicle fleet composition, utilization, energy consumption...

Using Synthetic Population Generation to Replace Sample and Expansion Weights in Household Surveys for Small Area Estimation of Population Parameters

Abstract: In this paper the authors illustrate the use of synthetic population generation methods to replace sample weights and expansion weights in household travel surveys. The authors use a combination of exogenous (US Census) and endogenous (the survey) data as the informants and in essence transfer information from the county level sample to the tracts. The method is based on a population synthesis approach called PopGen (PopGen 1.1, 2011) and is applied to the newly collected data in the California Household Travel Survey (CHTS). An illustration of using traditional sampling and expansion weights and synthetic population generation is illustrated at the tract level. The authors show synthetic population methods are able to recreate the entire spatial distribution of households and persons in small areas, recreate the variation that is lost when sampling. This method is capable of reproducing the variation in the real population and enables transferability without having to develop complicated methods. Moreover, it fills spatial gaps in data collection, produces a large database that is ready to be used in activity microsimulation, provides as byproducts sample and expansion weights, and offers the possibility to perform resampling for model estimation. However, additional testing and experimentation is also required.

Measuring the Travel Characteristics of a University Population: Experiences from the Design and Administration of a Web-based Travel Survey

Abstract: Institutions of higher education, particularly those with large student enrollments, constitute special generators that contribute in a variety of ways to the travel demand in a region. Despite the importance of university population travel characteristics in understanding and modeling activity-travel patterns and mode choice behavior in a region, such populations remain under-studied in the activity-travel behavior analysis arena. Traditional household travel surveys rarely capture or include a sample of university students that is large enough to infer travel behavior characteristics for this specific market segment. This paper reports on the process followed and experiences gained in the conduct of a comprehensive activity-travel survey at Arizona State University, one the largest universities in North America. An online survey was administered to the entire university population, including staff, students, and faculty, during a three week period and the resulting data set serves as a valuable resource for modeling and analyzing university-generated travel demand. This paper focuses on the survey design and administration process, as well as the assembly and weighting of the resulting data set. Adopting a carefully planned survey process that involves close coordination with the university administration proved critical to the collection of a usable and rich travel survey data set for the university population.

Exploring the Characteristics of Short Trips: Implications for Walk Mode Choice

Abstract: People undertake many short trips, which may be defined as those under five miles, or under two miles, or even under one mile in length. Although these trips have lengths that make them candidates for bicycling or walking, i.e., the use of sustainable non-motorized modes of transport, it is found that a substantial share of these short trips are undertaken by car. Although there has been some research into the reasons why short trips are not largely undertaken by walk and bicycle, much remains to be learned about the nature of short trips and the potential constraints that limit the ability of travelers to use non-motorized modes for these trips. This paper offers a detailed examination of short trips, with a view to exploring the potential factors inhibiting the use of walk and bicycle modes for these trips. The paper offers a detailed descriptive analysis of short trips in two major metropolitan regions using data from the most recent 2008-2009 National Household Travel Survey in the United States. It is found that trip chaining patterns may be playing a significant role in preventing more walking and bicycling. Based on a characterization of short trips in the survey data sets, the paper offers planning and policy strategies that may help bolster the share of walking and bicycling for short trips.

Dynamic, Integrated Model System: Jacksonville-Area Application

Abstract: This report will be of interest to professionals who use travel demand and network assignment models as part of the transportation planning process The goal of this research was to improve urban-scale modeling and network procedures to address operations or spot improvements that affect travel time choice, route choice, mode choice, reliability, or emissions Such improvements may include traveler information, pricing, reversible lanes, and improved bottlenecks Operational improvements like these are difficult to model on an urban-area scale using existing tools A secondary goal was to facilitate further development and deployment of these or similar procedures The goals were addressed by building a proof-of-concept dynamic integrated model in two urban areas: Jacksonville, Florida, and Sacramento, California The integration of the activity-based demand model DaySim and a Dynamic Traffic Assignment (DTA) model, TRANSIMS, in Jacksonville, Florida, is the subject of this report Both DaySim and TRANSIMS are open-source products Integration means that a feedback loop was built between the demand and network assignment model systems All the demographic and network data required to run the model set were assembled, and the feedback between the demand model and the DTA was tested in Jacksonville, Florida, and Burlington, Vermont The model set is structured so that it can be run in a long-range planning mode, a short-term operations mode, or a combined mode A companion report and model set are available for the application in Sacramento, California This work has the same objective but uses DynusT for the highway network assignment and adds a schedule-based transit assignment called FAST-TrIPs DaySim was also used as the demand model Both model sets and software start-up guides are available from the Federal Highway Administration

Are Multiple-Choice Questions Suitable for a Final Examination in a STEM Course?

Abstract: Dr. Autar Kaw is a professor of mechanical engineering at the University of South Florida. He was named the 2012 U.S. Professor the Year (Doctoral Institutions) by the Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education. The U.S. Professor of the Year award is the highest honor in the nation for undergraduate teaching. He received his BE Honors degree in Mechanical Engineering from Birla Institute of Technology and Science (BITS), India in 1981, and his degrees of Ph.D. in 1987 and M.S. in 1984, both in Engineering Mechanics from Clemson University, SC. He joined University of South Florida in 1987.

The application of an integrated behavioral activity-travel simulation model for pricing policy analysis

Abstract: This chapter demonstrates the feasibility of applying an integrated microsimulation model of activitytravel demand and dynamic traffic assignment for analyzing the impact of pricing policies on traveler activity-travel choices. The model system is based on a dynamic integration framework wherein the activity-travel simulator and the dynamic traffic assignment model communicate with one another along the continuous time axis so that trips are routed and simulated on the network as and when they are generated. This framework is applied to the analysis of a system-wide pricing policy for a small case study site to demonstrate how the model responds to various levels of pricing. Case study results show that trip lengths, travel time expenditures, and vehicle miles of travel are affected to a greater degree than activity-trip rates and activity durations as a result of pricing policies. Measures of change output by the model are found to be consistent with elasticity estimates reported in the literature.

TCRP H-37 Characteristics of Premium Transit Services That Affect Mode Choice: Key Findings and Results

Abstract: This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings are that awareness and consideration of transit services is significantly different than the perfect awareness and consideration of all modes which is an underlying assumption of mode choice and forecasting models. Furthermore, inclusion of non-traditional transit attributes and attitudes can maintain or improve the ability of mode choice models to predict the usage of premium transit modes while reducing the weight on modal constants that vary between transit sub-modes. Additional methods and analyses are necessary to bring these results into practice. This paper focuses on the key findings and results of the research of the value of non-traditional transit service attributes on modal choice, the influence of awareness and consideration of transit service on modal alternatives, and the importance of traveler attitudes on both awareness and consideration of transit and on the choice of transit or auto in mode choice. The models estimated to support these findings are described, but not in detail, due to the space limitations, but are available in the Transit Cooperative Research Program H-37A Final Report. The paper also documents the findings of the implementation testing, which concludes that including path choices and non-traditional transit service attributes in mode choice models can reduce the weight of the modal constants.

An Integrated Latent Construct Modeling Framework for Predicting Physical Activity Engagement and Health Outcomes

Abstract: The health and well-being of individuals is related to their activity-travel patterns. Indeed, there is increasing interest in drawing connections between activity-travel indicators and public health outcomes. Many of the indicators related to physical activity participation, sedentary activity participation (such as watching television or sitting at the computer for extended periods), and extent of bicycling and walking are measures that public health professionals would be interested in connecting to health outcomes such as body mass index (BMI), blood pressure, and overall state of health. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, despite the widespread recognition of the importance of attitudes and lifestyle preferences on activity engagement patterns and mode use, activity-based models fail to include such variables in the model specification. Engagement in physical activities, and the use of bicycle and walk modes, are likely to be influenced by the lifestyle preferences and attitudes of individuals. But, such lifestyle preferences and attitudes are rarely, if ever, measured in surveys rendering it difficult to explicitly include such measures in activity model specifications. This study constitutes an initial attempt to fill this gap by adopting Bhat's (2014) Generalized Heterogeneous Data Model (GHDM) model system in which latent constructs that describe an individual's health consciousness and physical activity propensity are modeled as a function of observed socio-economic and demographic characteristics. The resulting latent constructs, together with socio-economic and demographic variables, are then used to predict a number of activity engagement outcomes (describing frequency and duration of participation in various types of activities - both physically active and sedentary) and health outcomes (such as body mass index, self-reported health well-being, and blood pressure). The entire system of equations is estimated jointly through the use of the maximum approximate composite marginal likelihood (MACML) estimation approach that greatly simplifies the evaluation of the likelihood function and brings about computational efficiency in the estimation of simultaneous equations model systems that involve a mixture of dependent variable types. The model system is applied using the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample. The findings of the paper show that latent constructs, health consciousness and physical activity propensity, are related to socio-economic and demographic variables. These latent constructs play a significant role in shaping activity-travel and mode use patterns, with those who are more health conscious or inclined towards physically active lifestyles reporting higher levels of physical activity engagement and better health outcomes. Given the significance of the latent variables in explaining activity engagement and mode use, activity-based microsimulation models may be enriched in terms of the model specification through the inclusion of such latent variables that are themselves functions of observed socio-economic and demographic variables collected in travel surveys. There has been a reluctance historically to include attitudinal and lifestyle preference variables in model specifications because such variables are not typically measured in travel surveys, and more importantly, they are difficult to forecast into the future. However, the approach proposed in this paper, where latent variables are functions of observed variables and can be included in models of activity-travel behavior, offers a mechanism by which such latent attitudinal and lifestyle constructs can be included in models of activity-travel demand. The study is not without its limitations. Due to the nature of the study, the survey data set used for this modeling effort had to include both activity-travel indicators as well as health indicators. The 2005-2006 National Health and Nutrition Examination Survey (NHANES) offered such a data set, but this data set suffered from the drawback that it did not include any built environment, contextual, or network level of service variables - all of which invariably affect activity-travel indicators and possibly health outcomes as well. Future research and data collection efforts should attempt to include all of the variables of interest so that contextual variables may be accounted for in the model specification. 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Residential Geolocation of Households in a Large-Scale Activity-Based Microsimulation Model and Development of a High Definition Spatial Distribution of Vehicle Miles Traveled

Abstract: This paper presents a methodology to distribute the Traffic Analysis Zone (TAZ) level synthesized households and their members to parcels according to the household and parcel attributes. Three Multinomial Logit (MNL) models are estimated to represent the residence location association of households and land parcels. The estimated models are then used in an algorithm that assigns three different types of households to locations in the Los Angeles County. Daily Vehicle Miles Traveled (VMT) of each household is assigned in this way to the parcel each household is assigned to using the algorithm. The method illustrated here shows the feasibility of doing this assignment using millions of parcels and households. It also shows the results are reasonable and the authors are able to estimate VMT at specific locations and for spatially disaggregate jurisdictions enabling the assessment of policies at very fine levels of resolution. In addition, these findings and related maps challenge the claim that central city residents travel less miles and suburban residents travel more.