Multi-objective optimization of traffic signals based on vehicle trajectory data at isolated intersections

•Optimize signal timings based on sampled trajectories at isolated intersections.•Aggregation of sampled trajectories and Same-ratio Principles (SRPs) are proposed.•Evolution of sampled trajectories with varying signal timings are modeled explicitly.•The number of sampled trajectories plays the most...

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Vydané v:Transportation research. Part C, Emerging technologies Ročník 120; s. 102821
Hlavní autori: Ma, Wanjing, Wan, Lijuan, Yu, Chunhui, Zou, Li, Zheng, Jianfeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.11.2020
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ISSN:0968-090X, 1879-2359
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Abstract •Optimize signal timings based on sampled trajectories at isolated intersections.•Aggregation of sampled trajectories and Same-ratio Principles (SRPs) are proposed.•Evolution of sampled trajectories with varying signal timings are modeled explicitly.•The number of sampled trajectories plays the most critical role in the signalization. Existing fixed-time traffic signal optimization methods mainly use traffic volumes collected by infrastructure-based detectors (e.g., loop detectors). These infrastructure-based detectors generally have high maintenance costs and low coverage. With the deployment of probe vehicles, vehicle trajectory data provide more information about traffic states and can be utilized for signal timing. However, most related studies assume high penetration rates of probe vehicles or short sampling intervals. This paper develops a hierarchical multi-objective optimization framework to optimize fixed-time traffic signals based on sampled vehicle trajectories at isolated signalized intersections, which is applicable to low-resolution trajectory data. Cycle length and green splits are optimized under both under- and slightly over-saturated traffic conditions. The number of over-saturated phases and average vehicle delays are adopted as the primary and the secondary objectives, respectively. Note that the queues of over-saturated phases cannot be discharged. The queue length and traffic delay of over-saturated phases will go infinite as time goes. The consideration of the over-saturated phase number helps increase vehicle throughput and reduce queue length and traffic delay. The aggregation of sampled trajectory data during the same period across multiple cycles and Same-ratio Principles (SRPs) are proposed to compensate for the limitations of low penetration rates of probe vehicles. The evolution of sampled trajectories with varying signal timings are formulated explicitly. A sampled-trajectory-density method is proposed to identify over-saturated phases. Then a mixed integer non-linear programming (MINLP) model is formulated and transformed to a series of mixed integer linear programming (MILP) models by linearization and enumerating cycle lengths. Simulation studies validate the advantages of the proposed model over the one in Synchro Studio. Sensitivity analysis shows that: (1) the proposed model is applicable to Poisson vehicle arrivals; (2) the proposed model can handle sampling intervals as long as 15 s when sufficient sampled vehicle trajectories are collected; (3) the number of sampled trajectories has impacts on the performance of the proposed model instead of probe vehicle penetration rates, especially with under-saturated traffic; (4) cycle lengths of initial signal timing plans have no noticeable impacts on the required number of sampled trajectories when a short sampling interval is applied; and (5) the proposed model is insensitive to the quality of initial signal timing plans with under- and slightly over-saturated traffic. The proposed model is also implemented with field data to demonstrate its applicability in the real world.
AbstractList •Optimize signal timings based on sampled trajectories at isolated intersections.•Aggregation of sampled trajectories and Same-ratio Principles (SRPs) are proposed.•Evolution of sampled trajectories with varying signal timings are modeled explicitly.•The number of sampled trajectories plays the most critical role in the signalization. Existing fixed-time traffic signal optimization methods mainly use traffic volumes collected by infrastructure-based detectors (e.g., loop detectors). These infrastructure-based detectors generally have high maintenance costs and low coverage. With the deployment of probe vehicles, vehicle trajectory data provide more information about traffic states and can be utilized for signal timing. However, most related studies assume high penetration rates of probe vehicles or short sampling intervals. This paper develops a hierarchical multi-objective optimization framework to optimize fixed-time traffic signals based on sampled vehicle trajectories at isolated signalized intersections, which is applicable to low-resolution trajectory data. Cycle length and green splits are optimized under both under- and slightly over-saturated traffic conditions. The number of over-saturated phases and average vehicle delays are adopted as the primary and the secondary objectives, respectively. Note that the queues of over-saturated phases cannot be discharged. The queue length and traffic delay of over-saturated phases will go infinite as time goes. The consideration of the over-saturated phase number helps increase vehicle throughput and reduce queue length and traffic delay. The aggregation of sampled trajectory data during the same period across multiple cycles and Same-ratio Principles (SRPs) are proposed to compensate for the limitations of low penetration rates of probe vehicles. The evolution of sampled trajectories with varying signal timings are formulated explicitly. A sampled-trajectory-density method is proposed to identify over-saturated phases. Then a mixed integer non-linear programming (MINLP) model is formulated and transformed to a series of mixed integer linear programming (MILP) models by linearization and enumerating cycle lengths. Simulation studies validate the advantages of the proposed model over the one in Synchro Studio. Sensitivity analysis shows that: (1) the proposed model is applicable to Poisson vehicle arrivals; (2) the proposed model can handle sampling intervals as long as 15 s when sufficient sampled vehicle trajectories are collected; (3) the number of sampled trajectories has impacts on the performance of the proposed model instead of probe vehicle penetration rates, especially with under-saturated traffic; (4) cycle lengths of initial signal timing plans have no noticeable impacts on the required number of sampled trajectories when a short sampling interval is applied; and (5) the proposed model is insensitive to the quality of initial signal timing plans with under- and slightly over-saturated traffic. The proposed model is also implemented with field data to demonstrate its applicability in the real world.
ArticleNumber 102821
Author Zou, Li
Wan, Lijuan
Zheng, Jianfeng
Ma, Wanjing
Yu, Chunhui
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  givenname: Lijuan
  surname: Wan
  fullname: Wan, Lijuan
  email: wanlijuan29@tongji.edu.cn
  organization: The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
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  givenname: Chunhui
  surname: Yu
  fullname: Yu, Chunhui
  email: hughyu90@tongji.edu.cn
  organization: The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
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  givenname: Li
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  givenname: Jianfeng
  surname: Zheng
  fullname: Zheng, Jianfeng
  email: zhengjianfeng@didiglobal.com
  organization: Didi Chuxing Inc., Beijing, China
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Keywords Fixed-time signal optimization
Trajectory data
Mixed integer non-linear programming model
Low penetration rate of probe vehicle
Language English
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Snippet •Optimize signal timings based on sampled trajectories at isolated intersections.•Aggregation of sampled trajectories and Same-ratio Principles (SRPs) are...
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SubjectTerms Fixed-time signal optimization
Low penetration rate of probe vehicle
Mixed integer non-linear programming model
Trajectory data
Title Multi-objective optimization of traffic signals based on vehicle trajectory data at isolated intersections
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