Joint Optimization of Signal Phasing and Timing and Vehicle Speed Guidance in a Connected and Autonomous Vehicle Environment

Xiao (Joyce) Liang, S. Ilgin Guler, Vikash V. Gayah

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


A joint traffic signal optimization algorithm is proposed which utilizes connected vehicle (CV) information to identify optimum signal timing and phasing plans while also providing speed guidance to individual vehicles to minimize total number of stopping maneuvers. The contribution of this paper is provision of speed guidance to both autonomous (AVs) and human-driven speed guidance-enabled vehicles (SGVs), recognizing that the latter may not fully comply with the speed guidance and would require some delay (i.e., reaction time) to implement it. The control algorithm is triggered at regular discrete time intervals during which CV information is used to identify the presence of non-CVs and incorporate them into signal timing decision-making. Optimal speeds are determined for any AVs or SGVs so that they can travel through the intersection at the expected departure time without stopping, considering both acceleration/deceleration and human reaction times. Simulation tests are performed under different CV, AV, and SGV penetration rates, while explicitly modeling the potential human errors and varying acceptance levels. The results suggest that average delay and number of stops decrease with higher CV penetration rate. Furthermore, the number of stops decreases as the ratio of both AVs and SGVs increases. While AVs are about 10% more efficient than SGVs, human-driven vehicles still provide a benefit even when they do not fully comply with speed guidance information. Sensitivity tests suggest that operation is not significantly affected by the range of human driver errors in speed compliance or range of reaction times.

Original languageEnglish (US)
Pages (from-to)70-83
Number of pages14
JournalTransportation Research Record
Issue number4
StatePublished - Apr 1 2019

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering


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