Abstract
This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle's trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.
Original language | English |
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Pages (from-to) | 1463-1472 |
Number of pages | 10 |
Journal | Engineering |
Volume | 6 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2020 |
Funding
The authors would like to thank the two anonymous reviewers for their insightful comments that helped improve the original version of this paper. This research is partially supported by the connect cities with smart transportation (C2SMART) Tier 1 University Transportation Center (funded by US Department of Transportation (USDOT) ) at the New York University via a grant to the University of Washington ( 69A3551747124 ).
Funders | Funder number |
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USDOT | |
U.S. Department of Transportation | |
New York University | |
University of Washington | 69A3551747124 |
Keywords
- Connected vehicles
- Dynamic programming
- Mixed-integer nonlinear program
- Traffic signal coordination
- Two-level optimization