<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take real-time measures to guarantee the service reliability of transit networks. In this paper, we investigate a dynamic scheduling problem in a transit network, which takes account of the impact of disturbances on bus services. The objective is to minimize the total travel time of passengers in the transit network. A two-layer control method is developed to solve the proposed problem based on a hybrid control strategy. Specifically, relying on conventional strategies (e.g., holding, stop-skipping), the hybrid control strategy makes full use of the idle standby buses at the depot. Standby buses can be dispatched to bus fleets to provide temporary or regular services. Besides, deep reinforcement learning (DRL) is adopted to solve the problem of continuous decision-making. A long short-term memory (LSTM) method is added to the DRL framework to predict the passenger demand in the future, which enables the current decision to adapt to disturbances. The numerical results indicate that the hybrid control strategy can reduce the average headway of the bus fleet and improve the reliability of bus service.</p>
</abstract>

Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take real-time measures to guarantee the service reliability of transit networks. In this paper, we investigate a dynamic scheduling problem in a transit network, which takes account of the impact of disturbances on bus services. The objective is to minimize the total travel time of passengers in the transit network. A two-layer control method is developed to solve the proposed problem based on a hybrid control strategy. Specifically, relying on conventional strategies (e.g., holding, stop-skipping), the hybrid control strategy makes full use of the idle standby buses at the depot. Standby buses can be dispatched to bus fleets to provide temporary or regular services. Besides, deep reinforcement learning (DRL) is adopted to solve the problem of continuous decision-making. A long short-term memory (LSTM) method is added to the DRL framework to predict the passenger demand in the future, which enables the current decision to adapt to disturbances. The numerical results indicate that the hybrid control strategy can reduce the average headway of the bus fleet and improve the reliability of bus service.

A Hybrid Control Strategy for a Dynamic Scheduling Problem in Transit Networks

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Hybrid Control Strategy for a Dynamic Scheduling Problem in Transit Networks"}

Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take...

A Hybrid Control Strategy for a Dynamic Scheduling Problem in Transit Networks

Publié en ligne: 30 déc. 2022

Pages: 553 - 567

Reçu: 21 déc. 2021

Accepté: 27 juil. 2022

DOI: https://doi.org/10.34768/amcs-2022-0039

Mots clés
service reliability, transit network, proactive control method, deep reinforcement learning, hybrid control strategy

© 2022 Zhongshan Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Hybrid Control Strategy for a Dynamic Scheduling Problem in Transit Networks

Publié en ligne: 30 déc. 2022

Pages: 553 - 567

Reçu: 21 déc. 2021

Accepté: 27 juil. 2022

DOI: https://doi.org/10.34768/amcs-2022-0039

Mots clésservice reliability, transit network, proactive control method, deep reinforcement learning, hybrid control strategy

© 2022 Zhongshan Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
service reliability, transit network, proactive control method, deep reinforcement learning, hybrid control strategy