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<p>In response to the common issue of traffic spillover at short-distance intersections, which tends to be overlooked due to its potential for self-dissipation, this paper conducts a study on predicting traffic spillover dissipation at short-distance intersections using the machine learning models. First, conditions for identifying traffic spillover and determining its dissipation at short-distance intersections are proposed based on traffic wave theory, and the traffic operation and data collection scenario for a short-distance intersection is built using traffic simulation software, VISSIM 11. Next, data sets required as inputs for the prediction model are collected and generated, based on queue length of stranded vehicles and the dissipation state of traffic spillover, to improve model interpretability. Finally, a two-stage prediction model for traffic spillover dissipation is constructed using a Bi-LSTM model. The first stage of the model predicts the queue length of stranded vehicles on the road segment, which is then used as feature data for the second stage to predict the spillover dissipation state between short-distance intersections. The results show that the model’s prediction of the queue length of stranded vehicles in the first stage outperforms the DT, CNN, RF, LSTM, and GRU models, with a prediction accuracy of 93.4%, which verifies the feasibility of selecting the Bi-LSTM model in this paper. The model’s prediction of traffic spillover dissipation state in the second stage outperforms single-stage prediction models, with the model achieving an accuracy of 92.88% in traffic spillover identification and 90.72% in traffic spillover dissipation state prediction. This validates the effectiveness of the two-stage prediction method proposed in this paper and is conducive to further improving the model’s prediction accuracy. The proposed method can accurately predict traffic spillover and their dissipation at short-distance intersections, enabling the targeted selection of signal control strategies to address traffic spillover issues, thereby effectively improving the capacity of short-distance intersections.</p>

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The two-stage prediction method for traffic spillover dissipation at short-distance intersections based on Bi-LSTM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Zijun Liang,
  • Ruihan Wang,
  • Xuejuan Zhan,
  • Tingting Hu,
  • Xiaoyan Li,
  • Yuqi Li

摘要

In response to the common issue of traffic spillover at short-distance intersections, which tends to be overlooked due to its potential for self-dissipation, this paper conducts a study on predicting traffic spillover dissipation at short-distance intersections using the machine learning models. First, conditions for identifying traffic spillover and determining its dissipation at short-distance intersections are proposed based on traffic wave theory, and the traffic operation and data collection scenario for a short-distance intersection is built using traffic simulation software, VISSIM 11. Next, data sets required as inputs for the prediction model are collected and generated, based on queue length of stranded vehicles and the dissipation state of traffic spillover, to improve model interpretability. Finally, a two-stage prediction model for traffic spillover dissipation is constructed using a Bi-LSTM model. The first stage of the model predicts the queue length of stranded vehicles on the road segment, which is then used as feature data for the second stage to predict the spillover dissipation state between short-distance intersections. The results show that the model’s prediction of the queue length of stranded vehicles in the first stage outperforms the DT, CNN, RF, LSTM, and GRU models, with a prediction accuracy of 93.4%, which verifies the feasibility of selecting the Bi-LSTM model in this paper. The model’s prediction of traffic spillover dissipation state in the second stage outperforms single-stage prediction models, with the model achieving an accuracy of 92.88% in traffic spillover identification and 90.72% in traffic spillover dissipation state prediction. This validates the effectiveness of the two-stage prediction method proposed in this paper and is conducive to further improving the model’s prediction accuracy. The proposed method can accurately predict traffic spillover and their dissipation at short-distance intersections, enabling the targeted selection of signal control strategies to address traffic spillover issues, thereby effectively improving the capacity of short-distance intersections.