This paper discusses choice set generation and route choice model estimation for large-scale urban networks. Evaluating the effectiveness of Advanced Traveler Information Systems (ATIS) requires accurate models of how drivers choose routes based on their awareness of the roadway network and their perceptions of travel time. Many of the route choice models presented in the literature pay little attention to empirical estimation and validation procedures. In this paper, a route choice data set collected in Boston is described and the ability of several different route generation algorithms to produce paths similar to those observed in the survey is analyzed. The paper also presents estimation results of some route choice models recently developed using the data set collected.
An adaptation of the general logit kernel (LK) model to the route choice context is presented. Recent intelligent transportation systems applications have highlighted the need for better models of the behavioral processes involved in route choice. Several route choice models have been developed recently. However, few studies concentrated on the model estimation and applications for large urban networks. How LK can be adapted to route choice situations by suitably defining the elements of the model is described. The model is estimated using a sample formed from a route choice survey combined with network variables. Preliminary estimation results are presented and discussed.
This paper reviews key aspects of route guidance and information systems (RGISs), which disseminate to drivers messages with information and recommendations intended to assist their route choice decisions. It first summarizes the major features that characterize and distinguish different RGIS designs. An important distinction exists between non-predictive and predictive systems. In the former, guidance messages are based on measurements or estimates of prevailing network conditions, while, in the latter, messages are derived from forecasts of future conditions. For predictive systems, the key issue is consistency: ensuring that drivers’ reactions to guidance derived from forecasts do not invalidate those forecasts. It is shown that the determination of consistent guidance must be model based and can be formulated as a fixed-point problem. The modelling of driver response to guidance is a relatively new subject; the paper surveys the main issues, and relates these to the user-and system-level evaluation of RGISs. A final section identifies some areas of current research.
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