Deep Reinforcement Learning Based Dynamic Bus Timetable Scheduling with Bidirectional Constraints

Xie, Jiahao; Lin, Zhuo; Yin, Jieli; Lai, Zhaoyu; Wang, Xijun; Chen, Xiang

doi:10.1007/978-981-97-5803-6_8

Communications in Computer and Information Science

2024

DOI: 10.1007/978-981-97-5803-6_8

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Deep Reinforcement Learning Based Dynamic Bus Timetable Scheduling with Bidirectional Constraints

Jiahao Xie,

Zhuo Lin,

Jieli Yin

et al.

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Bus Schedule Time Prediction Based on LSTM-SVR Model

Ge,

Yang,

et al. 2024

Mathematics

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With the acceleration of urbanization, urban bus scheduling systems are facing unprecedented challenges. Traditional bus scheduling provides the original schedule time and the planned time of arrival at the destination, where the schedule time is the departure time of the bus. However, various factors encountered during the drive result in significant differences in the driving time of the bus. To ensure timely arrivals, the bus scheduling system has to rely on manual adjustments to optimize the schedule time to determine the actual departure time. In order to reduce the scheduling cost and align the schedule time closer to the actual departure time, this paper proposes a dynamic scheduling model, LSTM-SVR, which leverages the advantages of LSTM in capturing the time series features and the ability of SVR in dealing with nonlinear problems, especially its generalization ability in small datasets. Firstly, LSTM is used to efficiently capture features of multidimensional time series data and convert them into one-dimensional effective feature outputs. Secondly, SVR is used to train the nonlinear relationship between these one-dimensional features and the target variables. Thirdly, the one-dimensional time series features extracted from the test set are put into the generated nonlinear model for prediction to obtain the predicted schedule time. Finally, we validate the model using real data from an urban bus scheduling system. The experimental results show that the proposed hybrid LSTM-SVR model outperforms LSTM-BOA, SVR-BOA, and BiLSTM-SOA models in the accuracy of predicting bus schedule time, thus confirming the effectiveness and superior prediction performance of the model.

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Bus Schedule Time Prediction Based on LSTM-SVR Model

Ge,

Yang,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

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Deep Reinforcement Learning Based Dynamic Bus Timetable Scheduling with Bidirectional Constraints

Cited by 1 publication

References 20 publications

Bus Schedule Time Prediction Based on LSTM-SVR Model

Bus Schedule Time Prediction Based on LSTM-SVR Model

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