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Accurately predicting the bus travel time is important for improving bus service levels and enhancing the attractiveness of the bus service. This study first integrated multi-source data to construct multiple influencing factors, including bus arrival time data, spatial attributes of bus stops and routes, smart card data, and weather data. Random forest was then used to quantitatively analyze the impact of various influencing factors on bus travel time. The convolutional neural network-long short-term memory-attention mechanism (CNN-LSTM-ATTENTION) model was proposed to predict the bus travel time. One month’s multi-source data from Beijing was used to build the model. The results indicate that the travel time of the previous bus has the highest importance, therefore it is necessary to incorporate this feature for predicting bus arrival time to enhance prediction accuracy. The CNN-LSTM-ATTENTION model can reduce the mean square error by up to 29.7% compared to the traditional LightGBM model. Considering the travel time of the previous bus reduces the mean square error by 12.8% compared to without considering it.

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Bus Travel Time Prediction Based on Multiple Influencing Factors and CNN-LSTM-ATTENTION Network

  • 会议论文

  • 发表日期：

    2025年

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  • Pengfei Lin,
  • Yuzhuo Chen,
  • Yinuo Ouyang,
  • Nuo Yang,
  • Jiancheng Weng

摘要

Accurately predicting the bus travel time is important for improving bus service levels and enhancing the attractiveness of the bus service. This study first integrated multi-source data to construct multiple influencing factors, including bus arrival time data, spatial attributes of bus stops and routes, smart card data, and weather data. Random forest was then used to quantitatively analyze the impact of various influencing factors on bus travel time. The convolutional neural network-long short-term memory-attention mechanism (CNN-LSTM-ATTENTION) model was proposed to predict the bus travel time. One month’s multi-source data from Beijing was used to build the model. The results indicate that the travel time of the previous bus has the highest importance, therefore it is necessary to incorporate this feature for predicting bus arrival time to enhance prediction accuracy. The CNN-LSTM-ATTENTION model can reduce the mean square error by up to 29.7% compared to the traditional LightGBM model. Considering the travel time of the previous bus reduces the mean square error by 12.8% compared to without considering it.