Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

Most of the existing connected vehicle (CV)-based traffic control models require a critical penetration rate. If the critical penetration rate cannot be reached, then data from traditional sources (e.g., loop detectors) need to be added to improve the performance. However, it can be expected that over the next 10 years or longer, the CV penetration will remain at a low level. This paper presents a real-time detector-free adaptive signal control with low penetration of CVs ([Formula: see text]10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function. If no CV is observed during one signal cycle, historical traffic volume is used to generate signal timing plans. The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates. Results show that the new model outperforms well-tuned actuated control regarding delay reduction, in all scenarios under only 10% penetrate rate. The results also suggest that the accuracy of historical traffic volume plays an important role in the performance of the algorithm.

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Integrated Transit Priority and Rail/Emergency Preemption in Real-Time Traffic Adaptive Signal Control

Journal of Intelligent Transportation Systems ◽

10.1080/15472450490437799 ◽

2004 ◽

Vol 8 (2) ◽

pp. 101-115 ◽

Cited By ~ 32

Author(s):

Pitu B. Mirchandani ◽

David E. Lucas

Keyword(s):

Real Time ◽

Signal Control ◽

Adaptive Signal Control ◽

Real Time Traffic ◽

Adaptive Signal

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A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control

Algorithms ◽

10.3390/a12020038 ◽

2019 ◽

Vol 12 (2) ◽

pp. 38 ◽

Cited By ~ 1

Author(s):

Zhihong Yao ◽

Yibing Wang ◽

Wei Xiao ◽

Bin Zhao ◽

Bo Peng

Keyword(s):

Real Time ◽

Traffic Flow ◽

Signal Control ◽

Dynamic Traffic ◽

Traffic Flow Prediction ◽

Adaptive Signal Control ◽

Flow Prediction ◽

Vehicle Delay ◽

Rolling Optimization ◽

Adaptive Signal

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

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