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Journal of Advanced Transportation
Volume 2017, Article ID 6374858, 10 pages
https://doi.org/10.1155/2017/6374858
Research Article

Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
2School of Automotive Studies, Clean Energy of Automotive Engineering Research Center, Tongji University, Shanghai 201804, China

Correspondence should be addressed to Yuchuan Du; nc.ude.ijgnot@udcy

Received 2 February 2017; Revised 7 April 2017; Accepted 23 April 2017; Published 23 May 2017

Academic Editor: Wai Yuen Szeto

Copyright © 2017 Yuxiong Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data.