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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211013036 ◽

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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