Route Choice Models

Abstract

We present alternative discrete choice models of route choice behavior in road networks. A route choice model has two components: 1. The generation of a choice set of alternative routes; and 2. The choice of route among the alternatives in the choice set. The route choice models used in traffic equilibrium tend to be very simple with implicit generation of very large path choice sets. The advent of Intelligent Transportation Systems has renewed the interest in modeling the effects of traffic information systems on route choice behavior. Developments in discrete choice methods have also led to the more sophisticated route choice models. We present results of a small sample route choice survey of MIT employees showing that only a small percentage of drivers select the minimum travel time, minimum distance or minimum generalized cost paths as calculated from the model of the road network. These results demonstrate that the usual deterministic route choice model can be rejected. We then focus on the formulation of probabilistic route choice models. We consider the problem of overlapping paths and the modified Logit solutions that have been developed: C-Logit and Path-Size Logit. We also consider applications to route choice of more general discrete choice models including Cross-Nested Logit, Probit and, ultimately, the Logit Kernel model, which is a flexible hybrid of Logit and Probit. The properties of the different models are examined using simple network examples. Finally, we present estimation results for the different choice models. The results show that the Logit Kernel model with a Path-Size term gives the best fit. The application of Probit and Logit Kernel models requires simulation. Among the closed-form models, the Cross-Nested Logit with a Path-Size term provides a slightly better fit then the Path-Size Logit. The Path-Size formulation appears to have significant explanatory power of the way travelers perceive the alternative paths in a road network.