Intelligent buses in a loop service: Emergence of no-boarding and holding strategies

TL;DR

This paper demonstrates how reinforcement learning enables intelligent buses on a loop to develop no-boarding and holding strategies that optimize passenger wait times and promote cooperative behaviors like unbunching.

Contribution

It introduces a reinforcement learning framework for buses to autonomously learn no-boarding and holding strategies, leading to emergent cooperative behaviors and improved service efficiency.

Findings

Emergent no-boarding and holding strategies reduce passenger wait times.

Buses learn to unbunch and maintain optimal phase differences.

Strategies are effective under various bus frequency scenarios.

Abstract

We study how $N$ intelligent buses serving a loop of $M$ bus stops learn a \emph{no-boarding strategy} and a \emph{holding strategy} by reinforcement learning. The high level no-boarding and holding strategies emerge from the low level actions of \emph{stay} or \emph{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 people 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 have identical natural frequency, or different natural frequencies during busy as well as lull periods. Cooperative actions are also observed, e.g. the buses learn to \emph{unbunch}.