A Dynamic Holding Approach to Stabilizing a Bus Line Based on the Q-learning Algorithm with Multistage Look-ahead
Sheng-Xue He, Jian-Jia He, Shi-Dong Liang, June Qiong Dong, Peng-Cheng, Yuan

TL;DR
This paper introduces an adaptive bus holding control method combining approximate dynamic programming with multi-stage look-ahead and neural networks, effectively stabilizing bus headways and reducing bunching in high-frequency bus lines.
Contribution
It presents a novel dynamic holding strategy integrating multi-stage look-ahead with Q-learning and neural networks for real-time bus operation stabilization.
Findings
Effectively stabilizes bus headways and reduces bunching.
Produces more evenly distributed headways compared to traditional strategies.
Shortens passenger waiting times significantly.
Abstract
The unreliable service and the unstable operation of a high frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method which combines the classic approximate dynamic programming (ADP) with the multi-stage look-ahead mechanism. The holding time, that is the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remained observation period. The multi-stage look-ahead mechanism introduced into the…
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Taxonomy
TopicsTransportation Planning and Optimization · Transportation and Mobility Innovations · Traffic control and management
