A Person-Based Adaptive Traffic Signal Control Method with Cooperative Transit Signal Priority

Wei Hsun Lee, Hsuan Chih Wang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Real-time traffic signal control has long been a critical way to improve traffic congestion. Transit Signal Priority (TSP) is seen as a cost-effective way to reduce travel time variability. Most of the previous studies develop real-time signal control systems on a vehicle basis, which is unable to efficiently provide preferential treatment on transit vehicles. Person-based signal control systems, which transform traffic delay computation units from vehicle to passenger, have been proposed to try to address this limitation. However, their models, optimizing signal plan cycle-by-cycle, cannot rapidly respond to traffic variations. This study proposes a Person-based Adaptive traffic signal control method with Cooperative Transit signal priority (PACT). In PACT, not only do Road-Side Units (RSUs) perform signal optimization, but also On-Board Units (OBUs) provide in-vehicle speed advisory to reduce delays. The interaction between RSU and OBU is conducted second-by-second, which has high adaptability to traffic variations. Experiments are performed based on real traffic data via traffic simulation platform SUMO. The results indicate that PACT can efficiently reduce delays of both bus passengers and auto passengers at a signalized intersection. Compared to preoptimized signal plans, the results show that each passenger on transit vehicles experiences 33%-70% decreases in delays, and each auto passenger experiences 3%-29% decreases in delays. PACT can reduce 80%-98% in delays when the occupancy weight factor is relatively large, showing the potential of extending PACT on performing signal preemption.

Original languageEnglish
Article number2205292
JournalJournal of Advanced Transportation
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management


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