An integrated intelligent intersection control system for preventing rear-end and angled crashes: System design and deployment results

Accid Anal Prev. 2023 Oct:191:107219. doi: 10.1016/j.aap.2023.107219. Epub 2023 Jul 22.

Abstract

In view of the dynamic all-red extension (DARE) system's effectiveness in preventing angled crashes (Park et al., 2018), this study has further enhanced its function to contend with rear-end collisions with dynamic green extension (DGE). With such a function, the enhanced Integrated Intelligent Intersection control system (III-CS) is capable of dynamically terminating the green at the interval of the lowest rear-end collision risk, so as to prevent undesirable "max-out" under actuated signal control which often traps some vehicles in the dilemma zone during high-volume traffic conditions. To ensure its effectiveness in practice, the proposed III-CS has been designed with the following new features: (i) executing the DGE within a customized time window of the green phase to ensure the signal's effective coordination with its neighboring intersections; (ii) adopting the comparison-based heuristic for the DGE's real-time risk prediction so as to circumvent the computing and communications delays. The results of two after-deployment assessments show that the system's DARE has perfectly detected all red-light runners; 66.7 percent of the decisions by the DGE module were observed to achieve the control objective during the first field assessment. The DGE's performance in making optimal decisions has improved over time and reached the level of 81.3% in the second field evaluation. Other measures of effectiveness, such as the number of vehicles trapped in the dilemma zone and the average deceleration rate of the driving populations approaching the target intersection, have also evolved to the anticipated trend after the deployment.

Keywords: Angled crash; Dilemma zone; Dynamic green extension; Rear-end crash; Safety-based control.

MeSH terms

  • Accidents, Traffic* / prevention & control
  • Automobile Driving*
  • Decision Making
  • Humans
  • Light
  • Software