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Predictive intelligent driver model for eco-driving using upcoming traffic signal information

Author

Listed:
  • Xin, Qi
  • Fu, Rui
  • Yuan, Wei
  • Liu, Qingling
  • Yu, Shaowei

Abstract

Without accounting for the signalized intersection constraints in the design of adaptive cruise control (ACC) system, the ACC-equipped connected vehicle (CV) traveling on signalized roadway should be taken over by driver at signalized intersection frequently and speed variations increase significantly. Aiming at addressing this issue, a predictive intelligent driver model (IDM) for eco-driving based on V2X communication is proposed by using upcoming traffic signal information, which can be regarded as the upper controller of ACC system. The intersection signal constraints considered in IDM is treated as a dummy preceding vehicle at red light and no barriers at green light to cope with the application extending problem. To reduce idling time at signalized intersection, an intersection passing decision is presented with model prediction to forecast the arrival time with downstream queue discharge time under consideration, and an eco-driving model with speed reduction strategy is proposed by solving the combined constraints of the signal phase and timing (SPaT) and the vehicle status. Numerical simulations show that taking the speed profile generated by eco-driving model as speed advisor can reduce idling times and fuel consumption levels in the vicinity of signalized intersection.

Suggested Citation

  • Xin, Qi & Fu, Rui & Yuan, Wei & Liu, Qingling & Yu, Shaowei, 2018. "Predictive intelligent driver model for eco-driving using upcoming traffic signal information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 806-823.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:806-823
    DOI: 10.1016/j.physa.2018.05.138
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    References listed on IDEAS

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    Cited by:

    1. Liao, Peng & Tang, Tie-Qiao & Wang, Tao & Zhang, Jian, 2019. "A car-following model accounting for the driving habits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 108-118.
    2. Chen, Dong & Zhao, Min & Sun, Dihua & Zheng, Linjiang & Jin, Shuang & Chen, Jin, 2020. "Robust H∞ control of cooperative driving system with external disturbances and communication delays in the vicinity of traffic signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. Kai Liu & Dong Liu & Cheng Li & Toshiyuki Yamamoto, 2019. "Eco-Speed Guidance for the Mixed Traffic of Electric Vehicles and Internal Combustion Engine Vehicles at an Isolated Signalized Intersection," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
    4. Ma, Xinjuan & Ge, Hongxia & Cheng, Rongjun, 2019. "Influences of acceleration with memory on stability of traffic flow and vehicle’s fuel consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 143-154.
    5. Xu, Ting & Jiang, Ruisen & Wen, Changlei & Liu, Meijun & Zhou, Jiehan, 2019. "A hybrid model for lane change prediction with V2X-based driver assistance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    6. Chen, Can & Cheng, Rongjun & Ge, Hongxia, 2019. "An extended car-following model considering driver’s sensory memory and the backward looking effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 278-289.
    7. Li, Dong & Ma, Changxi, 2022. "Research on lane change prediction model based on GBDT," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    8. Liu, Qingling & Xu, Xiaowen, 2024. "A platoon-based eco-driving control mechanism for low-density traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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