A hybrid control strategy for a dynamic scheduling problem in transit networks

• Autorzy: Liu Zhongshan , Yu Bin , Zhang Li , Wang Wensi

Identyfikatory

DOI

10.34768/amcs-2022-0039

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

service reliability; transit network; proactive control method; deep reinforcement learning; hybrid control strategy

PL

niezawodność usług; sieć tranzytowa; uczenie głębokie; kontrola hybrydowa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 4
• Strony: 553--567
• Opis fizyczny: Bibliogr. 39 poz., rys., tab., wykr.

Twórcy

autor

Liu Zhongshan

• School of Transportation Science and Engineering, Beihang University, Beijing 100191, PR China

autor

Yu Bin

• School of Transportation Science and Engineering, Beihang University, Beijing 100191, PR China

autor

Zhang Li

• School of Transportation Science and Engineering, Beihang University, Beijing 100191, PR China

autor

Wang Wensi

• School of Transportation Science and Engineering, Beihang University, Beijing 100191, PR China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)