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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 6218363, 10 pages
Research Article

Commute Equilibrium for Mixed Networks with Autonomous Vehicles and Traditional Vehicles

1School of Management, Shanghai University, 99 Shangda Road, Shanghai 200444, China
2School of Economics and Management, Tongji University, 1239 Siping Road, Shanghai 200092, China
3College of Transport & Communications, Shanghai Maritime University, 1550 Haigang Road, Pudong, Shanghai 201306, China

Correspondence should be addressed to Yangbeibei Ji; nc.ude.uhs@bbyj

Received 9 May 2017; Revised 21 September 2017; Accepted 18 October 2017; Published 12 November 2017

Academic Editor: Xiaobo Qu

Copyright © 2017 Yangbeibei 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.


Recent development of autonomous vehicle (AV) provides new travel opportunities for citizens, and traditional vehicles (TVs) will still be used for a long time. Therefore, it is highly possible that both AVs and TVs will be used as travel modes in a city. In a transportation system with both AVs and TVs, the traffic pattern is worthy of studying. This paper investigates user equilibrium traffic pattern based on the traditional bottleneck model considering AVs and TVs. For both TVs and AVs, travel costs include queuing delay and schedule delay. However, they also have different components of travel costs; more specifically, for AVs, passengers have to pay a riding fare, and, for TVs, travelers encounter a walking time cost after parking their cars. For different combinations of travel demands and riding fare of AVs, analytical solutions of three different user equilibrium traffic patterns are obtained. Finally, numerical examples are provided to demonstrate the usefulness of the analytical models. Sensitivity analyses are examined to show the impacts of AV’s time-dependent fee and trip-based fixed fee on the traffic pattern and travel costs.