Dynamic Cooperative Speed Optimization at Signalized Arterials with Various Platoons. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Wu, Xia; Zhao, Xiangmo; Xin, Qi; Yang, Qiaoli; Yu, Shaowei; Sun, Kang
• Is Part Of: Transportation research record. Volume 2673:Issue 5(2019)
• Page Start: 528
• Page End: 537
• Publication Date: 2019-05
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198119839964
• Languages: English
• ISSNs: 0361-1981

Abstract

Aggressive and inappropriate driving behaviors will lead to excessive fuel consumption. Both the Signal Phase and Timing (SPaT) and the status of preceding vehicles have significant impacts on driving behaviors. Drivers can obtain accurate SPaT information and the status of preceding vehicles via V2X communications. Many speed advisory strategies have been presented based on the consideration of this information. However, existing studies do not consider the cooperative optimization of multiple intersections and various platoons. Once connected vehicles travel through intersections with their own fuel-optimum trajectories, the following vehicles could be adversely affected by the preceding vehicles, leading to the following vehicles being stopped at the intersection. To address these problems, this paper presents an improved cooperative eco-driving model for when a vehicle passes two successive traffic signals during the green phase; a dynamic nonlinear programming algorithm is used to generate the optimal speed profile for various platoons considering the SPaT and the preceding vehicles' status. Numerous simulations on VISSIM for uninformed and connected vehicles haves been conducted to make comparison analysis. It is apparent that the proposed eco-driving model produces a significant fuel saving. In addition, cooperative optimization for the various platoons and separate optimization of multiple vehicles were performed to seek the most effective solution. The results indicated that systematic optimization (cooperative optimization of the all vehicles) is identified as the fuel-optimum approach in comparison to the separate optimization.