Adaptive signal control for urban traffic network gridlock

2016 ◽  
Author(s):  
Nan Li ◽  
Guangzhou Zhao
Keyword(s):  
Urban Traffic ◽  
Signal Control ◽  
Traffic Network ◽  
Adaptive Signal Control ◽  
Urban Traffic Network ◽  
Adaptive Signal

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.

scholarly journals BACKPRESSURE BASED TRAFFIC SIGNAL CONTROL CONSIDERING CAPACITY OF DOWNSTREAM LINKS

Transport ◽  
2020 ◽  
Vol 35 (4) ◽  
pp. 347-356
Author(s):  
Shenxue Hao ◽  
Licai Yang ◽  
Yunfeng Shi ◽  
Yajuan Guo
Keyword(s):  
Queue Length ◽  
Control Algorithm ◽  
Mesh Networks ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Network ◽  
Maximum Throughput ◽  
Urban Traffic Network

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic 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.

Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System

2021 ◽  
pp. 036119812110130
Author(s):  
S M A Bin Al Islam ◽  
Mehrdad Tajalli ◽  
Rasool Mohebifard ◽  
Ali Hajbabaie
Keyword(s):  
Travel Time ◽  
Market Share ◽  
Signal Control ◽  
Market Penetration ◽  
Travel Time Reliability ◽  
Traffic Demand ◽  
Adaptive Signal Control ◽  
Loop Detector ◽  
Traffic Performance ◽  
Adaptive Signal

The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability.

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