Chandra R. Bhat

Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences

The use of simulation techniques has been increasing in recent years in the transportation and related fields to accommodate flexible and behaviorally realistic structures for analysis of decision processes. This paper proposes a randomized and scrambled version of the Halton sequence for use in simulation estimation of discrete choice models. The scrambling of the Halton sequence is motivated by the rapid deterioration of the standard Halton sequences coverage of the integration domain in high dimensions of integration. The randomization of the sequence is motivated from a need to statistically compute the simulation variance of model parameters. The resulting hybrid sequence combines the good coverage property of quasi-Monte Carlo sequences with the ease of estimating simulation error using traditional Monte Carlo methods. The paper develops an evaluation framework for assessing the performance of the traditional pseudo-random sequence, the standard Halton sequence, and the scrambled Halton sequence. The results of computational experiments indicate that the scrambled Halton sequence performs better than the standard Halton sequence and the traditional pseudo-random sequence for simulation estimation of models with high dimensionality of integration.

QUASI-RANDOM MAXIMUM SIMULATED LIKELIHOOD ESTIMATION OF THE MIXED MULTINOMIAL LOGIT MODEL

This paper proposes the use of a quasi-random sequence for the estimation of the mixed multinomial logit model. The mixed multinomial structure is a flexible discrete choice formulation which accommodates general patterns of competitiveness as well as heterogeneity across individuals in sensitivity to exogenous variables. The estimation of this model has been achieved in the past using the pseudo-random maximum simulated likelihood method that evaluates the multi-dimensional integrals in the log-likelihood function by computing the integrand at a sequence of pseudo-random points and taking the average of the resulting integrand values. We suggest and implement an alternative quasi-random maximum simulated likelihood method which uses cleverly crafted non-random but more uniformly distributed sequences in place of the pseudo-random points in the estimation of the mixed logit model. Numerical experiments, in the context of intercity travel mode choice, indicate that the quasi-random method provides considerably better accuracy with much fewer draws and computational time than does the pseudo-random method. This result has the potential to dramatically influence the use of the mixed logit model in practice; specifically, given the flexibility of the mixed logit model, the use of the quasi-random estimation method should facilitate the application of behaviorally rich structures in discrete choice modeling.

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes.

This paper proposes an econometric structure for injury severity analysis at the level of individual accidents that recognizes the ordinal nature of the categories in which injury severity are recorded, while also allowing flexibility in capturing the effects of explanatory variables on each ordinal category and allowing heterogeneity in the effects of contributing factors due to the moderating influence of unobserved factors. The model developed here, referred to as the mixed generalized ordered response logit (MGORL) model, generalizes the standard ordered response models used in the extant literature for injury severity analysis. To our knowledge, this is the first such formulation to be proposed and applied in the econometric literature in general, and in the safety analysis literature in particular. The MGORL model is applied to examine non-motorist injury severity in accidents in the USA, using the 2004 General Estimates System (GES) database. The empirical findings emphasize the inconsistent results obtained from the standard ordered response model. An important policy result from our analysis is that the general pattern and relative magnitude of elasticity effects of injury severity determinants are similar for pedestrians and bicyclists. The analysis also suggests that the most important variables influencing non-motorist injury severity are the age of the individual (the elderly are more injury-prone), the speed limit on the roadway (higher speed limits lead to higher injury severity levels), location of crashes (those at signalized intersections are less severe than those elsewhere), and time-of-day (darker periods lead to higher injury severity).

A mixed spatially correlated logit model: formulation and application to residential choice modeling

In recent years, there have been important developments in the simulation analysis of the mixed multinomial logit model as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class. In this paper, we bring these developments together to propose a mixed spatially correlated logit (MSCL) model for location-related choices. The MSCL model represents a powerful approach to capture both random taste variations as well as spatial correlation in location choice analysis. The MSCL model is applied to an analysis of residential location choice using data drawn from the 1996 Dallas-Fort Worth household survey. The empirical results underscore the need to capture unobserved taste variations and spatial correlation, both for improved data fit and the realistic assessment of the effect of sociodemographic, transportation system, and land-use changes on residential location choice.

Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns

The Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns (CEMDAP) is a microsimulation implementation of an activity-travel modeling system. Given as input various land use, sociodemographic, activity system, and transportation level-of-service attributes, the system provides as output the complete daily activity-travel patterns for each individual in each household of a population. The underlying econometric modeling framework and the software development experience associated with CEMDAP are described. The steps involved in applying CEMDAP to predict activity-travel patterns and to perform policy analysis are also presented. Empirical results obtained from applying the software to the Dallas-Fort Worth area demonstrate that CEMDAP provides a means of analyzing policy impacts in ways that are generally infeasible with the conventional four-stage approach.

AN ENDOGENOUS SEGMENTATION MODE CHOICE MODEL WITH AN APPLICATION TO INTERCITY TRAVEL

This article uses an endogenous segmentation approach to model mode choice. This approach jointly determines the number of market segments in the travel population, assigns individuals probabilistically to each segment, and develops a distinct mode choice model for each segment group. The author proposes a stable and effective hybrid estimation approach for the endogenous segmentation model that combines an Expectation-Maximization algorithm with standard likelihood maximization routines. If access to general maximum-likelihood software is not available, the multinomial-logit based Expectation-Maximization algorithm can be used in isolation. The endogenous segmentation model, and other commonly used models in the travel demand field to capture systematic heterogeneity, are estimated using a Canadian intercity mode choice dataset. The results show that the endogenous segmentation model fits the data best and provides intuitively more reasonable results compared to the other approaches.

A UNIFIED MIXED LOGIT FRAMEWORK FOR MODELING REVEALED AND STATED PREFERENCES: FORMULATION AND APPLICATION TO CONGESTION PRICING ANALYSIS IN THE SAN FRANCISCO BAY AREA

This paper formulates and applies a unified mixed-logit framework for joint analysis of revealed and stated preference data that accommodates a flexible competition pattern across alternatives, scale difference in the revealed and stated choice contexts, heterogeneity across individuals in the intrinsic preferences for alternatives, heterogeneity across individuals in the responsiveness to level-of-service factors, state-dependence of the stated choices on the revealed choice, and heterogeneity across individuals in the state-dependence effect. The estimation of the mixed logit formulation is achieved using simulation techniques that employ quasi-random Monte Carlo draws. The formulation is applied to examine the travel behavior responses of San Francisco Bay Bridge users to changes in travel conditions. The data for the study are drawn from surveys conducted as part of the 1996 San Francisco Bay Area Travel Study. The results of the mixed logit formulation are compared with those of more restrictive structures on the basis of parameter estimates, implied trade-offs among level-of-service attributes, heterogeneity and state-dependence effects, data fit, and substantive implications of congestion pricing policy simulations.

A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions

Auto ownership modeling plays an important role in travel demand analysis because it is a key determinant of the travel behavior of individuals and households. Discrete-choice auto ownership models use either an ordered-response choice mechanism or an unordered-response choice mechanism. The ordered-response mechanism is based on the hypothesis that an uni-dimensional continuous latent auto ownership propensity index determines the level of car ownership. The unordered-response mechanism is based on the Random Utility Maximization principle. This paper presents the underlying theoretical structures, and identifies the advantages and disadvantages, of the two alternative response mechanisms. The paper also compares the ordered-response mechanism (represented by the ordered-response logit model) and the unordered-response mechanism (represented by the multinomial logit model) empirically using several data sets. This comparative analysis offers strong evidence that the appropriate choice mechanism is the unordered-response structure. As a general guideline, auto ownership modeling must be pursued using the unordered-response class of models (such as the multinomial logit or probit model) and not using the ordered-response class of models (such as the ordered-response logit or probit).

A comprehensive daily activity-travel generation model system for workers

This paper develops a comprehensive representation to describe the activity-travel pattern of workers and proposes an analysis framework to model the activity-travel attributes identified in the representation. The analysis framework is based on a descriptive examination of activity-travel patterns of workers from two locations in the US. The paper also formulates an econometric methodology to estimate the component of the analysis framework involving the joint modeling of evening commute mode choice, number of evening commute stops, and number of stops after arriving home from work. The methodology is applied to an empirical analysis using data from an activity survey conducted in the Boston Metropolitan area and the effects of a variety of congestion-alleviation measures are examined. ©