Daehyun You

Integrated Land Use-Transport Model System with Dynamic Time-Dependent Activity-Travel Microsimulation

The development of integrated land use–transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity–travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand–supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity–travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity–travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs.

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

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 composition and utilization patterns at the household level. The model system is described and results are presented of a validation and policy sensitivity analysis exercise demonstrating the efficacy of the model.

Joint Model of Weekend Discretionary Activity Participation and Episode Duration

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.

Multiple discrete-continuous model of activity participation and time allocation for home-based work tours

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 of the model. The model was found to perform well in replicating tour patterns in the estimation sample and responded in a behaviorally intuitive manner in the context of a policy sensitivity test.

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions

Microsimulation models that simulate travel demand at the level of individual travelers have been gaining increasing interest among practitioners. Transportation planning agencies across the country are steadily migrating to activity-based microsimulation models, which provide considerable flexibility when testing policy scenarios. Generating a synthetic population is the first step in the application of any activity-based model system and has therefore been a topic of extensive research in the activity-based modeling arena. Several researchers have developed population synthesizers that can generate synthetic populations and can match household- and person-level constraints at a specified geographic resolution (e.g., a census block group). However, although information for some control variables may be available at the specified spatial resolution, information for other control variables may be available only at a more aggregate spatial resolution. Ignoring control variables at different levels of spatial resolution could result in the generation of a synthetic population that would not be representative of the underlying population. However, there has been limited progress in the development of synthetic population generators that are capable of accommodating control variables at multiple spatial resolutions. This paper proposes a robust approach to control for constraints at multiple geographic resolutions when generating a synthetic population. The method is an extension of the iterative proportional updating algorithm previously proposed and implemented by the authors. A case study demonstrating the efficacy of the enhanced algorithm is presented.

Impacts of Incentive-Based Intervention on Peak Period Traffic: Experience from the Netherlands

Incentive-based travel demand management strategies are gaining increasing attention because they are generally considered more acceptable by the traveling public and policy makers. This study presented a detailed analysis and modeling effort aimed at understanding how incentives affected traveler choices by using data collected from a reward-based experiment conducted in 2006 in the Netherlands. The incentive-based scheme analyzed in this study included monetary rewards or credit toward obtaining a smartphone with a view to motivating commuters to change their choice of departure time out of the peak period or to shift their mode of travel. The mixed panel multinomial logit modeling approach adopted in this study was able to isolate the impacts of incentives on behavioral choices while accounting for variations in such impacts across socioeconomic groups that might have been due to unobserved individual preferences and constraints. The model also shed light on the effects of behavioral inertia, in which ...Incentive-based travel demand management strategies are gaining increasing attention because they are generally considered more acceptable by the traveling public and policy makers. This study presented a detailed analysis and modeling effort aimed at understanding how incentives affected traveler choices by using data collected from a reward-based experiment conducted in 2006 in the Netherlands. The incentive-based scheme analyzed in this study included monetary rewards or credit toward obtaining a smartphone with a view to motivating commuters to change their choice of departure time out of the peak period or to shift their mode of travel. The mixed panel multinomial logit modeling approach adopted in this study was able to isolate the impacts of incentives on behavioral choices while accounting for variations in such impacts across socioeconomic groups that might have been due to unobserved individual preferences and constraints. The model also shed light on the effects of behavioral inertia, in which individuals were prone to continue their past behavior even when it was no longer optimal. Finally, the study offered insights on the extent to which behavioral changes persisted after termination of the incentive period. In general, it was found that incentives were effective in changing behavior and overcame inertial effects; however, individuals largely reverted to their original behavior when the rewards were eliminated. This finding suggests that incentives need to be provided for a sustained period to bring about lasting change.

Tour Characterization Framework Incorporating Activity Stop–Sequencing Model System

Activity-based models (ABMs) adopt the notion of tours to model activity–travel patterns, because the concept of a tour closely mimics how individuals chain their activities in the real world. Each tour may be defined by a primary destination that corresponds to a primary purpose and may include a multitude of secondary stops on the way to the primary destination (the outbound half-tour) or on the way back (the inbound half-tour). This paper presents a tour characterization framework capable of simulating all secondary stops on a tour, the time allocated to each activity, and the sequence of stops on a tour. The first component simulates the mix of activities and their corresponding durations and was presented in an earlier paper. The current paper presents the stop-sequencing component, which is capable of determining the order in which activities will be pursued on outbound and inbound half-tours. Model estimation results and comparisons between observed and predicted stop-sequencing patterns are presented. The models were found to perform quite well in replicating the observed stop-making patterns. The overall tour characterization framework was designed to accommodate the continuous treatment of time in ABMs in practice.