Connected Vehicle–Based Adaptive Signal Control and Applications

2016 ◽  
Vol 2558 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Yiheng Feng ◽  
Mehdi Zamanipour ◽  
K. Larry Head ◽  
Shayan Khoshmagham
Keyword(s):  
Signal Control ◽  
Connected Vehicle ◽  
Adaptive Signal Control ◽  
Adaptive Signal

Real-Time Detector-Free Adaptive Signal Control with Low Penetration of Connected Vehicles

2018 ◽  
Vol 2672 (18) ◽  
pp. 35-44 ◽  
Author(s):  
Yiheng Feng ◽  
Jianfeng Zheng ◽  
Henry X. Liu
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Penetration Rate ◽  
Traffic Volume ◽  
Signal Control ◽  
Connected Vehicle ◽  
Estimation Model ◽  
Loop Detectors ◽  
Adaptive Signal Control ◽  
Adaptive Signal

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.

An adaptive signal control using connected-vehicle data

2019 ◽  
Vol 172 (2) ◽  
pp. 102-110 ◽  
Author(s):  
Linghui Xu ◽  
Jia Lu ◽  
Fengping Zhan ◽  
Shanglu He ◽  
Jian Zhang
Keyword(s):  
Signal Control ◽  
Connected Vehicle ◽  
Adaptive Signal Control ◽  
Vehicle Data ◽  
Adaptive Signal

Adaptive Coordination Based on Connected Vehicle Technology

2017 ◽  
Vol 2619 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Byungho Beak ◽  
K. Larry Head ◽  
Yiheng Feng
Keyword(s):  
Mixed Integer ◽  
Signal Control ◽  
Connected Vehicle ◽  
Average Delay ◽  
Mixed Integer Linear Program ◽  
Adaptive Signal Control ◽  
Connected Vehicle Technology ◽  
Vehicle Technology ◽  
Adaptive Control Algorithm ◽  
Adaptive Signal

This paper presents a methodology that integrates coordination with adaptive signal control in a connected vehicle environment. The model consists of two levels of optimization. At the intersection level, an adaptive control algorithm allocates the optimal green time to each phase in real time by using dynamic programming that considers coordination constraints. At the corridor level, a mixed-integer linear program is formulated on the basis of data from the intersection level to optimize offsets along the corridor. After the corridor-level algorithm solves the optimization problem, the optimized offsets are sent to the intersection-level algorithm to update the coordination constraints. The model was compared with actuated–coordinated signal control by means of Vissim simulation. The results indicate that the model can reduce average delay and average number of stops for both coordinated routes and the entire network.

scholarly journals A Platoon-Based Adaptive Signal Control Method with Connected Vehicle Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ning Li ◽  
Shukai Chen ◽  
Jianjun Zhu ◽  
Daniel Jian Sun
Keyword(s):  
Control Method ◽  
Model Performance ◽  
Urban Traffic ◽  
Mixed Integer ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicle ◽  
Adaptive Signal Control ◽  
The Individual ◽  
Adaptive Signal

One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.

A real-time adaptive signal control in a connected vehicle environment

2015 ◽  
Vol 55 ◽  
pp. 460-473 ◽  
Author(s):  
Yiheng Feng ◽  
K. Larry Head ◽  
Shayan Khoshmagham ◽  
Mehdi Zamanipour
Keyword(s):  
Real Time ◽  
Signal Control ◽  
Connected Vehicle ◽  
Adaptive Signal Control ◽  
Adaptive Signal
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