Citation

Yao J, Tan C, Tang K. Transp. Res. C Emerg. Technol. 2019; 109: 211-232.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.trc.2019.10.014

PMID

unavailable

Abstract

Traditional arterial coordination control models adopt aggregated demand from fixed detectors or manual counts as main input, which lack a self-feedback mechanism between the input and the optimization objective. With the population of connected vehicles and intelligent mobility, high-resolution trajectory data provide new possibilities for signal control evaluation and optimization. Therefore, the objective of this study is to propose a new arterial coordination control model for two-way arterial progression solely using sampled trajectories. Sampled trajectories are first used to extract prior arrival information and queuing states. Then, vehicle arrival time at the stop-line at each intersection is estimated as the function of signal timing parameters and prior arrival rates based on the shockwave theory, considering different sampled trajectory statuses. The delay of each sample vehicle is thus obtained as the difference between the actual arrival time and the projected arrival time. Sum of the delays of all the sampled trajectories ever travelling on the mainline of the arterial is selected to be the optimization objective, and the optimization problem is solved through a multi-sub-swarm Particle Swarm Optimization (PSO) algorithm. A simulation model is built to test the performance of the proposed model compared with the Synchro model and the MULTIBAND model under various demand scenarios.

RESULTS show that the performance of the proposed method is relatively satisfactory, which demonstrates that the optimization of fixed-time arterial coordination control solely using sample trajectories is feasible.

Language: en

Keywords

Coordination control; Delay minimization; Sampled trajectory data; Shockwave theory; Signalized arterial