An empirical study on aggregation of alternatives and its influence on prediction in car type choice models

Transportation - Assessing and predicting car type choices are important for policy analysis. Car type choice models are often based on aggregate alternatives. This is due to the fact that analysts...     

Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

Cognitive cost in route choice with real-time information: An exploratory analysis

Real-time traffic information is increasingly available to support route choice decisions by reducing the travel time uncertainty. However it is likely that a traveler cannot assess all available information on all alternative routes due to time constraints and limited cognitive capacity. This paper presents a model that is consistent with a general network topology and can potentially be estimated based on revealed preference data. It explicitly takes into account the information acquisition and the subsequent path choice. The decision to acquire information is assumed to be based on the cognitive cost involved in the search and the expected benefit defined as the expected increase in utility after the search. A latent class model is proposed, where the decision to search or not to search and the depth of the search are latent and only the final path choices are observed. A synthetic data set is used for the purpose of validation and ease of illustration. The data are generated from the postulated cognitive-cost model, and estimation results show that the true values of the parameters can be recovered with enough variability in the data. Two other models with simplifying assumptions of no information and full information are also estimated with the same set of data with significantly biased path choice utility parameters. Prediction results show that a smaller cognitive cost encourages information search on risky and fast routes and thus higher shares on those routes. As a result, the expected average travel time decreases and the variability increases. The no-information and full-information models are extreme cases of the more general cognitive-cost model in some cases, but not generally so, and thus the increasing ease of information acquisition does not necessarily warrant a full-information model.