Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics.

Journal: Accident; analysis and prevention
PMID:

Abstract

Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safety evaluation as a potential alternative to traditional crash-based methods. However, the suitability of extreme value theory models in the before-after evaluation of engineering treatments needs to be rigorously tested. As such, this study proposes a traffic conflict-based before-after evaluation of a novel part-time protected right-turn signal strategy for right-turn or opposing-through crashes at signalised intersections. A part-time protected right-turn signal strategy refers to a signal arrangement where permissive and fully protected right-turn phasings are operated during peak and off-peak hours, respectively. A deep neural network-based computer vision technique was applied to extract the conflicts from a total of 654 h of video recordings (before period: 266 h and after period: 388 h) over seven treated approaches, and four matching control approaches at five signalised intersections in the city of Cairns, Australia. Using post encroachment time and post-collision velocity difference as traffic conflict measures, non-stationary bivariate generalised extreme value models were developed to estimate the severe and non-severe opposing-through crashes at signal cycle levels. The odds ratio analysis of model-predicted crash risks suggests that part-time protected right-turn signals reduce 67% and 81% of severe and non-severe opposing-through crashes at signalised intersections, respectively. Part-time protected right-turn signal strategy offers a good safety solution without precipitating need for capacity upgrades to accommodate queued right turners at signalised intersections.

Authors

  • Md Mohasin Howlader
    Queensland University of Technology (QUT), School of Civil and Environmental Engineering, Faculty of Engineering, Brisbane, QLD 4000, Australia. Electronic address: mdmohasin.howlader@connect.qut.edu.au.
  • Yasir Ali
    Loughborough University, School of Architecture, Building and Civil Engineering, Loughborough LE11 3TU, United Kingdom. Electronic address: y.y.ali@lboro.ac.uk.
  • Andrew Burbridge
    Department of Transport and Main Roads, Engineering & Technology Branch, Infrastructure Management and Delivery Division, Brisbane, Queensland 4000, Australia. Electronic address: andrew.z.burbridge@tmr.qld.gov.au.
  • Md Mazharul Haque
    Queensland University of Technology (QUT), School of Civil and Environmental Engineering, Faculty of Engineering, Brisbane, QLD 4000, Australia. Electronic address: m1.haque@qut.edu.au.