Moshe Ben-Akiva

Discrete choice methods and their applications to short-term travel decisions

Modeling travel behavior is a key aspect of demand analysis, where aggregate demand is the accumulation of individuals’ decisions. In this chapter, we focus on “short-term” travel decisions. The most important short-term travel decisions include choice of destination for a non-work trip, choice of travel mode, choice of departure time and choice of route. It is important to note that short-term decisions are conditional on long-term travel and mobility decisions such as car ownership and residential and work locations.

Activity-based disaggregate travel demand model system with activity schedules

We present an integrated activity-based discrete choice model system of an individuals activity and travel schedule, for forecasting urban passenger travel demand. A prototype demonstrates the system concept using a 1991 Boston travel survey and transportation system level of service data. The model system represents a persons choice of activities and associated travel as an activity pattern overarching a set of tours. A tour is defined as the travel from home to one or more activity locations and back home again. The activity pattern consists of important decisions that provide overall structure for the days activities and travel. In the prototype the activity pattern includes (a) the primary - most important - activity of the day, with one alternative being to remain at home for all the days activities; (b) the type of tour for the primary activity, including the number, purpose and sequence of activity stops; and (c) the number and purpose of secondary - additional - tours. Tour models include the choice of time of day, destination and mode of travel, and are conditioned by the choice of activity pattern. The choice of activity pattern is influenced by the expected maximum utility derived from the available tour alternatives.

Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions

This paper affords a stylized view of individual consumer choice decision-making appropriate to the study of many marketing decisions. It summarizes issues relating to consideration set effects on consumer judgment and choice. It discusses whether consideration sets really exist and, if so, the factors that affect their composition, structure, and role in decision-making. It examines some new developments in the measurement and modeling of consideration set effects on decision-making. The paper concludes with suggestions for needed research.

Discrete choice models with latent choice sets

Abstract The paper extends discrete choice models by adding an explicit probabilistic representation of the various alternatives considered by the individual in a choice situation. The motivation is to incorporate into a single framework of choice set generation modeling the effects of stochastic constraints or elimination criteria and the influence of attitudes and perceptions on the choice set generation process. The basic idea is to estimate a choice set generation model by using information contained in responses to alternative availability questions. The estimation approach jointly incorporates the information on individuals perceived choice set and the revealed preference information corresponding to the observed choice. The proposed methodology is emprically tested with a travel demand dataset.

Generalized random utility model

Abstract Researchers have long been focused on enriching Random Utility Models (RUMs) for a variety of reasons, including to better understand behavior, to improve the accuracy of forecasts, and to test the validity of simpler model structures. While numerous useful enhancements exist, they tend to be discussed and applied independently from one another. This paper presents a practical, generalized model that integrates many enhancements that have been made to RUM. In the generalized model, RUM forms the core, and then extensions are added that relax simplifying assumptions and enrich the capabilities of the basic model. The extensions that are included are: • Flexible Disturbances in order to allow for a rich covariance structure and enable estimation of unobserved heterogeneity through, for example, random parameters; • Latent Variables in order to provide a richer explanation of behavior by explicitly representing the formation and effects of latent constructs such as attitudes and perceptions; • Latent Classes in order to capture latent segmentation in terms of, for example, taste parameters, choice sets, and decision protocols; and • Combining Revealed Preferences and Stated Preferences in order to draw on the advantages of the two types of data, thereby reducing bias and improving efficiency of the parameter estimates. The paper presents a unified framework that encompasses all models, describes each enhancement, and shows relationships between models including how they can be integrated. These models often result in functional forms composed of complex multidimensional integrals. Therefore, an estimation method consisting of Simulated Maximum Likelihood Estimation with a kernel smooth simulator is reviewed, which provides for practical estimation. Finally, the practicality and usefulness of the generalized model and estimation technique is demonstrated by applying it to a case study.

SIMULATION LABORATORY FOR EVALUATING DYNAMIC TRAFFIC MANAGEMENT SYSTEMS

Advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS) are promising technologies for achieving efficiency in the operation of transportation systems. A simulation-based laboratory environment, MITSIMLab, is presented that is designed for testing and evaluation of dynamic traffic management systems. The core of MITSIMLab is a microscopic traffic simulator (MITSIM) and a traffic management simulator (TMS). MITSIM represents traffic flows in the network, and the TMS represents the traffic management system under evaluation. An important feature of MITSIMLab is its ability to model ATMS or ATIS that generate traffic controls and route guidance based on predicted traffic conditions. A graphical user interface allows visualization of the simulation, including animation of vehicle movements. An ATIS case study with a realistic network is also presented to demonstrate the functionality of MITSIMLab.

Incorporating random constraints in discrete models of choice set generation

This paper proposes a behavioral interpretation of the choice set generation process that is useful for structuring and specifying discrete models that incorporate this stage of choice. The key concept is the notion of random constraints, which is operationalized in the form of probabilistic choice set formation models; we thus explicitly recognize our imperfect understanding and possible lack of data about the process. Several such models are presented and discussed with respect to their behavioral plausibility and estimability. A related paper reports on the calibration of one such specification with work mode choice data from Sao Paulo, Brazil.

A theoretical and empirical model of trip chaining behavior

This paper addresses the theoretical and empirical issues involved in modeling complex travel patterns. Existing models have the shortcoming of not representing the interdependencies among trip links in trip chains with multiple non-home stops. A theoretical model based on utility theory and explicitly accounting for the trade-offs involved in the choice of multiple-stop chains is developed. Using this theoretical model, utility maximizing conditions for a households choice of a daily travel pattern are derived. The optimum travel pattern is described in terms of the number of chairs (tours) traveled on a given day and in terms of the number of stops (sojourns) made on each of those chains. For a given household, the form of the optimum pattern is a function of the transportation expenditures (time, cost) required to reach potential destinations. Constraints on the conditions of optimality due to the limited and discrete nature of travel pattern alternatives are also considered. Parameters of the general utility function were estimated empirically using actual travel data derived from a home interview survey taken in Washington, D.C. The multinomial logit model is used to relate utility scores for the alternative travel patterns to choice probabilities. The resulting parameter estimates agree with theoretical expectations and with empirical results obtained in other studies. In order to demonstrate the empirical and theoretical implications of the model, forecasts for various transportation policies (e.g., gasoline price increases, transit fare reductions), as made by this model and by other less complex models, are compared. The results of these comparisons indicate the need for expanding the scope of existing travel forecasting models to explicit considerations of trip chaining behavior.

Extended Framework for Modeling Choice Behavior

We review the case against the standard model of rational behavior and discuss the consequences of various ‘anomalies’ of preference elicitation. A general theoretical framework that attempts to disentangle the various psychological elements in the decision-making process is presented. We then present a rigorous and general methodology to model the theoretical framework, explicitly incorporating psychological factors and their influences on choices. This theme has long been deemed necessary by behavioral researchers, but is often ignored in demand models. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model system. We conclude with a research agenda to bring the theoretical framework into fruition.

Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin--Destination Flows

This paper examines two different approaches for real-time estimation/prediction of time-dependent Origin--Destination (O--D) flows. Both approaches lend themselves to formulation as state-space models. The first approach is an extension of previous work by the authors. The key idea in this approach is to define the state-vector in terms of deviations in O--D flows instead of the O--D flows themselves. We demonstrate that approximations to this model make the real-time estimation process computationally more tractable with little deterioration in quality of estimates. In the second approach, the state vector is defined in terms of deviations of departure rates from each origin and the shares headed to each destination. This approach attempts to capture the differential variation of departure rates and shares over time. Performance of the proposed models is evaluated using actual traffic data from different sources. Preliminary results indicate that the filtering procedure is robust and that, compared to the original model, a formulation based on departure rates and shares yields better predictions with some loss of accuracy in filtered estimates.