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Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies

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  • Vee-Liem Saw
  • Luca Vismara
  • Lock Yue Chew

Abstract

We study how intelligent buses serving a loop of bus stops learn a no-boarding strategy and a holding strategy by reinforcement learning. The no-boarding and holding strategies emerge from the actions of stay or leave when a bus is at a bus stop and everyone who wishes to alight has done so. A reward that encourages the buses to strive towards a staggered phase difference amongst them whilst picking up passengers allows the reinforcement learning process to converge to an optimal Q-table within a reasonable amount of simulation time. It is remarkable that this emergent behaviour of intelligent buses turns out to minimise the average waiting time of commuters, in various setups where buses move with the same speed or different speeds, during busy as well as lull periods. Cooperative actions are also observed, e.g., the buses learn to unbunch .

Suggested Citation

  • Vee-Liem Saw & Luca Vismara & Lock Yue Chew, 2020. "Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies," Complexity, Hindawi, vol. 2020, pages 1-18, August.
    • Handle: RePEc:hin:complx:7274254
    DOI: 10.1155/2020/7274254
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    1. Vismara, Luca & Chew, Lock Yue & Saw, Vee-Liem, 2021. "Optimal assignment of buses to bus stops in a loop by reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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