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AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning

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  • Liu, Shan
  • Zhang, Ya
  • Wang, Zhengli
  • Gu, Shiyi

Abstract

Taxi cruising route planning has attracted considerable attention, and relevant studies can be broadly categorized into three main streams: recommending one or multiple areas, providing a detailed cruising route, and deriving the optimal routing policy. However, these studies depend on accurate pick-up/drop-off information, and seldom pay attention to cruising speed planning. In view of the rapid development of autonomous taxis, this study proposes AdaBoost-Bagging maximum entropy deep inverse reinforcement learning to learn cruising policy from experienced taxi drivers’ trajectories. Moreover, we develop a trajectory-based self-attention bidirectional LSTM model to adjust cruising speeds on different roads. Numerical experiments using real taxi trajectories in Chengdu, China demonstrate the effectiveness of our approach in learning taxi drivers’ policies and improving taxis’ operational efficiency.

Suggested Citation

  • Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:transe:v:177:y:2023:i:c:s136655452300220x
    DOI: 10.1016/j.tre.2023.103232
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    References listed on IDEAS

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    1. Liu, Shan & Jiang, Hai, 2022. "Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    2. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    3. Jeffery B. Greenblatt & Samveg Saxena, 2015. "Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles," Nature Climate Change, Nature, vol. 5(9), pages 860-863, September.
    4. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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