Estimating Safety Impacts of Adaptive Signal Control Technology Using a Full Bayesian Approach

Adaptive signal control technology (ASCT) is a traffic management strategy that optimizes signal timing based on real-time traffic demand. Although the primary intent of ASCT is to improve the operational performance of signalized intersections, the technology may also have substantial safety benefits. This study explored the potential safety benefits of the ASCT strategy deployed at signalized intersections in Florida, U.S. An observational before-after full Bayes (FB) approach with a comparison group was adopted to develop crash modification factors (CMFs) for total crashes, rear-end crashes, and specific crash severity levels (fatal plus injury [FI], and property damage only [PDO] crashes). The analysis was based on 20 intersections equipped with ASCT and their corresponding 40 comparison intersections without ASCT. The ASCT deployment was found to significantly reduce total crashes by 7.8% (CMF = 0.922), rear-end crashes by 8.7% (CMF = 0.913), and PDO crashes by 8.1% (CMF = 0.919). The 8.6% reduction in FI crashes (CMF = 0.914) was not significant at a 90% Bayesian credible interval. These findings provide researchers and practitioners with an effective means to quantify the safety benefits of the ASCT strategy and conduct economic appraisals of ASCT deployments.

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

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

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.

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

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.