Data Fusion Approach for Evaluating Route Choice Models in Large-Scale Complex Urban Rail Transit Networks

Abstract

With the increases in both the urban rail transit (URT) network scale and complexity, a route choice model that was previously developed may not function properly anymore and therefore must be constantly evaluated if possible and updated whenever necessary. This paper develops and uses a posterior approach that fuses multisource data from both the automatic fare collection (AFC) and automatic train supervision (ATS) systems to provide accurate and intelligent evaluation of route choice models, especially for large-scale complex URT networks. A method to rebuild passengers’ journey one by one is put forward that makes the proposed approach work in a more disaggregate manner. Then, observed travel time (OTT), and simulated travel time (STT), which are deduced by fusing multisource data from AFC and ATS systems, are defined. Instead of using traditional manual-based methods, the evaluation of route choice models is conducted by comparing and testing the distributions of both OTTs and STTs, and two nonparametric statistical techniques (NPSTs) are adopted. Pilot case studies are conducted on the Beijing subway network and the results obtained clearly show that the approach can disaggregately evaluate the route choice model and can also be easily incorporated into an automatic evaluation procedure.