Assessment of Operational Effectiveness of SynchroGreen Adaptive Signal Control System in South Carolina

2021 ◽  
pp. 036119812110197
Author(s):  
Weimin Jin ◽  
M Sabbir Salek ◽  
Mashrur Chowdhury ◽  
Mohammad Torkjazi ◽  
Nathan Huynh ◽  
...  
Keyword(s):  
South Carolina ◽  
Control System ◽  
Travel Time ◽  
Geographical Area ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Travel Time Reliability ◽  
Operational Effectiveness ◽  
Adaptive Signal Control ◽  
Adaptive Signal

An adaptive signal control system (ASCS) can adjust signal timings in real time based on traffic demands. The operational benefits of ASCS vary depending on the type of ASCS, corridor characteristics, and geographical area. This paper evaluates the operational performance of 11 ASCS corridors located throughout South Carolina. These corridors are operated using SynchroGreen, one of several types of ASCS, developed by TrafficWare. Based on the operational analysis, it is found that when SynchroGreen is operational, it reduces the travel time on the corridor by an average of 6.4% and improves travel time reliability by an average of 31.4% compared with when the conventional traffic signal control system (e.g., pre-timed and actuated signal control) is operational. SynchroGreen reduces travel time on a corridor on average 61% of the time during a day and on average 77% of the time during peak periods. Additionally, SynchroGreen improves travel time reliability on average 53% of the time during a day and on average 52% of the time during peak periods. The operational effectiveness of SynchroGreen in reducing travel time and improving travel time reliability is consistent in both directions on an hourly basis for eight corridors and five corridors, respectively. Lastly, SynchroGreen is found to produce greater operational benefits by reducing travel time if the average speed of a corridor is lower than or equal to 35 mph and the number of signals on a corridor is more than 10.

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.

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.

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 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.

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