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.
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Acknowledgments
This study was financially supported by the National Natural Science Foundation of China (71701152), the Research Program of Science and Technology Commission in Shanghai (18510745800), and the Fundamental Research Funds for the Central Universities (22120180067). The authors also wish to acknowledge Beijing Subway Co., Ltd, for providing basic data during the research.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 1 • January 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Nov 2, 2018
Accepted: Apr 24, 2019
Published online: Oct 28, 2019
Published in print: Jan 1, 2020
Discussion open until: Mar 28, 2020
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