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.

scholarly journals Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Juyuan Yin ◽  
Peng Chen ◽  
Keshuang Tang ◽  
Jian Sun
Keyword(s):  
Mobile Internet ◽  
Urban Traffic ◽  
Internet Technology ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Trajectory Data ◽  
Adaptive Signal Control ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory ◽  
Adaptive Signal

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

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 An Adaptive Signal Control Method with Optimal Detector Locations

2019 ◽  
Vol 11 (3) ◽  
pp. 727 ◽  
Author(s):  
Senlai Zhu ◽  
Ke Guo ◽  
Yuntao Guo ◽  
Huairen Tao ◽  
Quan Shi
Keyword(s):  
Control System ◽  
Control Method ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Primary Role ◽  
Adaptive Traffic Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

The adaptive traffic signal control system is a key component of intelligent transportation systems and has a primary role in effectively reducing traffic congestion. The high costs of implementation and maintenance limit the applicability of the adaptive traffic signal control system, especially in developing countries. This paper proposes a low-cost adaptive signal control method that is easy to implement. Two detectors are installed in each vehicle lane at an optimal location determined by the proposed method to detect green and red redundancy time, based on which the original signal timing is adjusted through a signal controller. The proposed method is evaluated through case studies with low and high volume-to-capacity ratio intersections. The results show that the proposed adaptive signal control method can significantly reduce total traffic delay at intersections.

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.

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
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