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Journal of Advanced Transportation
Volume 2018, Article ID 7498594, 10 pages
https://doi.org/10.1155/2018/7498594
Research Article

Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment

College of Transportation, Jilin University, Changchun, Jilin Province 130022, China

Correspondence should be addressed to Song Xianmin; nc.ude.ulj@mxgnos

Received 16 October 2017; Revised 11 January 2018; Accepted 12 April 2018; Published 26 June 2018

Academic Editor: Antonino Vitetta

Copyright © 2018 Song Xianmin 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

Aiming at reducing per person delay, this paper presents an optimization method for Transit Signal Priority (TSP) considering multirequest under connected vehicle environment, which is based on the travel time prediction model. Conventional arrival time of transit depended on the detection information and the front road state, which restricted the effect of priority seriously. According to the bidirectional and real-time information transmission under connected vehicle environment, this paper establishes a more accurate forecasting model of bus travel time. Based on minimizing the total person delay at the intersection, the decision mechanism of multirequest is devised to meet the priority needs of buses with different arrival times. And the green time compensation algorithm is developed after considering the arrival information of the buses in the next cycle of compensational phase. Finally, the paper combines the COM interface of VISSIM and Matlab to achieve the proposed method under connected vehicle environment. Four control methods were tested when the VCR was 0.5, 0.7, and 0.9. The results illustrated that the proposed method reduced per person delay by 18.57%, 11.88%, and 18.96% and decreased the private vehicle delay by 3.73%, 7.62%, and 13.10%, respectively.