Transferability of real-time safety performance functions for signalized intersections

Accid Anal Prev. 2019 Aug:129:263-276. doi: 10.1016/j.aap.2019.05.029. Epub 2019 Jun 6.

Abstract

Optimizing traffic signals in real-time for safety performance can be executable in the era of Connected Vehicles (CVs) when real-time information on vehicle positions and trajectories is available. To achieve this, real-time safety models are needed to understand how changes in signal controllers affect safety in real-time. Recently, several real-time safety models were developed for signalized intersections that relate various dynamic traffic parameters to the number of rear-end traffic conflicts at the signal cycle level. The traffic parameters included: traffic volume, maximum queue length, shock wave speed and area, and platoon ratio. For wider application of these models to other jurisdictions, the transferability of these models needs to be examined. Therefore, this paper aims to investigate the transferability of several signalized intersections real-time safety models to new jurisdictions. Two corridors of signalized intersections in California and Atlanta were used in the analysis as destination jurisdictions. Detailed vehicle trajectories for these corridors were obtained from the Next Generation Simulation (NGSIM) data. Various transferability analysis approaches were applied. The transferability of the real-time safety models was evaluated with and without a local calibration for the model parameters at the new jurisdictions. Several goodness-of-fit measures were examined to assess the ability of the developed models to predict traffic conflicts. Overall, the results showed that the real-time safety models are transferable, which confirms the validity of using them for real-time safety evaluation of signalized intersections.

Keywords: Effect of signal design on safety; Real time safety evaluation; Safety performance functions; Traffic conflict analysis; Transferability analysis.

MeSH terms

  • Accidents, Traffic / prevention & control*
  • Automobile Driving / statistics & numerical data
  • Built Environment*
  • California
  • Georgia
  • Humans
  • Logistic Models
  • Risk Assessment
  • Safety