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Traffic signal priority control for public transport rapid transit based on a step-by-step prediction algorithm

Author

Listed:
  • Kwang-Myong Jon

    (Kim Il Sung University)

  • Kwang-Bok Han

    (Kim Il Sung University)

  • Chon-Il Jo

    (Kim Il Sung University)

  • Chon-Ung Kim

    (Kim Il Sung University)

Abstract

In recent years, the problem of urban traffic congestion has become serious as the urban population increases rapidly around the world. In many countries, the widespread use of public transport (PT) in cities is a trend to solve the problems of urban environmental pollution and traffic congestion. The main point is to increase the efficiency of PTs. Although several methodologies have been proposed to reduce the travel time of PTs in urban road networks, there is still a gap in the research on efficient methods to reduce them effectively in relation to other vehicles. This paper proposes a traffic signal priority control method to reduce the travel time of PTs such as buses and trolleybuses based on a step-by-step prediction algorithm of the traffic flows at intersections. Based on the formularization of the queue lengths to the inflow and outflow of vehicles at intersections with four approaches, the next phase traffic signal control strategy is established using an algorithm which predicts the queue length and verifies its advantages. The formula of the queue length is updated by applying weight factors to the public transports and is applied to the prediction algorithm, of which efficiency is proved in various conditions. Simulation of Urban Mobility (SUMO), an open source traffic simulator, is used for verification. Simulation results show that the proposed step-by-step prediction algorithm remarkably reduces the waiting time of PTs while the weight factor is increased. Compared to the Longest Queue First Algorithm, the average waiting time of PTs is reduced by 30%, even when the influence on other vehicles is taken into account.

Suggested Citation

  • Kwang-Myong Jon & Kwang-Bok Han & Chon-Il Jo & Chon-Ung Kim, 2025. "Traffic signal priority control for public transport rapid transit based on a step-by-step prediction algorithm," Public Transport, Springer, vol. 17(1), pages 267-280, March.
  • Handle: RePEc:spr:pubtra:v:17:y:2025:i:1:d:10.1007_s12469-024-00364-9
    DOI: 10.1007/s12469-024-00364-9
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    References listed on IDEAS

    as
    1. Sakhawat Hossan & Naushin Nower, 2020. "Fog-based dynamic traffic light control system for improving public transport," Public Transport, Springer, vol. 12(2), pages 431-454, June.
    2. Peter Bindzar & Daniel Macuga & Jaroslaw Brodny & Magdalena Tutak & Marcela Malindzakova, 2022. "Use of Universal Simulation Software Tools for Optimization of Signal Plans at Urban Intersections," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    3. Lu, Ke & Du, Pingping & Cao, Jinde & Zou, Qiming & He, Tianjia & Huang, Wei, 2019. "A novel traffic signal split approach based on Explicit Model Predictive Control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 105-114.
    4. Guo, Jianhua & Kong, Ye & Li, Zongzhi & Huang, Wei & Cao, Jinde & Wei, Yun, 2019. "A model and genetic algorithm for area-wide intersection signal optimization under user equilibrium traffic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 92-104.
    5. MD Sultan Ali & Henrick Haule & John Kodi & Priyanka Alluri & Thobias Sando, 2023. "Transferability of a calibrated microscopic simulation model parameters for operational assessment of transit signal priority," Public Transport, Springer, vol. 15(3), pages 791-812, October.
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