TY - JOUR

T1 - A heuristic method to optimize generic signal phasing and timing plans at signalized intersections using Connected Vehicle technology

AU - Liang, Xiao (Joyce)

AU - Guler, S. Ilgin

AU - Gayah, Vikash V.

N1 - Funding Information:
Partial funding for this research was provided by the Pennsylvania State University (PSU) University Graduate Fellowship (UGF) program.
Funding Information:
Partial funding for this research was provided by the Pennsylvania State University (PSU) University Graduate Fellowship (UGF) program.

PY - 2020/2

Y1 - 2020/2

N2 - This paper develops a flexible, real-time traffic signal control algorithm to optimize both phase durations and phase sequences at four-approach intersections with conflicting left-turns, based on information obtained from connected vehicles. Vehicle-to-infrastructure communications are assumed to provide the location of all connected vehicles near the signalized intersection at regular time intervals and this information is used to identify the presence of non-connected vehicles that are stopped at the intersection. All detected and identified vehicles are used to identify naturally occurring platoons in the traffic stream. The signal control algorithm then selects the optimal sequence that these platoons should discharge through the intersection to minimize average delay of all identified vehicles. Since all possible departure sequences for platoons of vehicles are considered, the problem is computationally difficult. Hence, several heuristic methods are proposed to determine optimal platoon departure sequences. These heuristics include an intelligent tree search and multiple types of genetic algorithms, including a newly developed genetic algorithm that perserves phase order sequence that is vital to this problem. Comparisons between these heuristics and the global optimal solution suggest that the heuristics are able to provide similar operational performance with significant reductions in total computation time required, such that the algorithm can be applied in real-time. In general, the intelligent tree search appears to outperform the genetic algorithm approaches in terms of operational performance but has computational requirements that increase exponentially with the number of platoons identified at the intersection. Meanwhile, the genetic algorithm methods tend to be more scalable but slightly less efficient. Overall, the results are promising for the application of the proposed flexible signal control algorithm at real intersections.

AB - This paper develops a flexible, real-time traffic signal control algorithm to optimize both phase durations and phase sequences at four-approach intersections with conflicting left-turns, based on information obtained from connected vehicles. Vehicle-to-infrastructure communications are assumed to provide the location of all connected vehicles near the signalized intersection at regular time intervals and this information is used to identify the presence of non-connected vehicles that are stopped at the intersection. All detected and identified vehicles are used to identify naturally occurring platoons in the traffic stream. The signal control algorithm then selects the optimal sequence that these platoons should discharge through the intersection to minimize average delay of all identified vehicles. Since all possible departure sequences for platoons of vehicles are considered, the problem is computationally difficult. Hence, several heuristic methods are proposed to determine optimal platoon departure sequences. These heuristics include an intelligent tree search and multiple types of genetic algorithms, including a newly developed genetic algorithm that perserves phase order sequence that is vital to this problem. Comparisons between these heuristics and the global optimal solution suggest that the heuristics are able to provide similar operational performance with significant reductions in total computation time required, such that the algorithm can be applied in real-time. In general, the intelligent tree search appears to outperform the genetic algorithm approaches in terms of operational performance but has computational requirements that increase exponentially with the number of platoons identified at the intersection. Meanwhile, the genetic algorithm methods tend to be more scalable but slightly less efficient. Overall, the results are promising for the application of the proposed flexible signal control algorithm at real intersections.

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U2 - 10.1016/j.trc.2019.11.008

DO - 10.1016/j.trc.2019.11.008

M3 - Article

AN - SCOPUS:85076836766

VL - 111

SP - 156

EP - 170

JO - Transportation Research Part C: Emerging Technologies

JF - Transportation Research Part C: Emerging Technologies

SN - 0968-090X

ER -