Multi-modal route choice modeling in a dynamic schedule-based transit network

Abstract

Route choice behavior has predominantly been analyzed from the angle of a single mode, most often the car. Considering route choice in the broader context of multi- modal networks yet opens the way to more complex policy analysis and wider applications. The main modeling challenges in this context are the limited availability in time of public transport services and the definition of alternatives to the observed path. This paper tackles these challenges by applying the recursive logit to model the choice of transit modes and route in a real network. The model is based on the assumption of a full available schedule. The approach presents numerous advantages. First, route choice preferences can be consistently estimated without generating choice sets of paths. Second, the model can be used to predict fast and accurately path choices in real network by sampling from estimated link choice probabilities. Although the network is much larger than previous applications of the RL model with over 1 million links, we obtain reasonable computational times. Third, the approach allows to include all transit services without restriction in one large-scale network, providing the possibility to estimate realistic rates of substitution between different attributes.