Benefits of connected vehicle signalized left-turn assist: Simulation-based study Article

Arafat, M, Hadi, M, Raihan, MA et al. (2021). Benefits of connected vehicle signalized left-turn assist: Simulation-based study . 4 10.1016/j.treng.2021.100065

cited authors

  • Arafat, M; Hadi, M; Raihan, MA; Iqbal, MS; Tariq, MT



  • Simulation modeling can play a major role in supporting the pre-deployment and post-deployment assessment of connected vehicle (CV) applications, particularly given the limited implementations of these applications and the low market penetrations of CV that can be expected in the near future. This study demonstrates the use of simulation modeling to assess the safety and mobility benefits of the Signalized Left Turn Assist (SLTA), a CV-based application at signalized intersections. The benefits of SLTA were analyzed in this study using a calibrated microscopic traffic simulation package. Real-world gap acceptance distribution was used in the calibration of the simulation model to better assess the impact of SLTA considering the real-world driver's behaviors at permissive left-turn signals. A method was developed to calibrate the utilized microscopic simulation model to reflect the real-world gap acceptance distributions. The study results show that SLTA can increase the left-turn capacity depending on the SLTA gap time parameter setting, reaching approximately 64.8%, 51.1%, and 35.9% when utilizing a gap time of 3 s, 4 s, and 5 s, respectively. In addition, the results show that with 100% utilization, the average delay for all vehicles can be reduced by approximately 58.4%. The safety benefits of the SLTA were determined utilizing surrogate measures based on vehicle trajectories generated by the microscopic traffic simulation model. The results show that by utilizing a 5-second predefined time gap in the SLTA application, the total number of observed crossing conflicts decreased from 6 conflicts per hour to zero conflicts per hour when the SLTA utilization rate increases from 0% to 100%.

publication date

  • June 1, 2021

Digital Object Identifier (DOI)


  • 4