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Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Author

Listed:
  • Qiang Shang

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Tian Xie

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Yang Yu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a certain area with a single data source. This paper proposes a hybrid deep learning model based on multi-source incomplete data to predict the duration of countrywide traffic incidents in the U.S. The text data from the natural language description in the model were parsed by the latent Dirichlet allocation (LDA) topic model and input into the bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) hybrid network together with sensor data for training. Compared with the four benchmark models and three state-of-the-art algorithms, the RMSE and MAE of the proposed method were the lowest. At the same time, the proposed model performed best for durations between 20 and 70 min. Finally, the data acquisition was defined as three phases, and a phased sequential prediction model was proposed under the condition of incomplete data. The results show that the model performance was better with the update of variables.

Suggested Citation

  • Qiang Shang & Tian Xie & Yang Yu, 2022. "Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data," IJERPH, MDPI, vol. 19(17), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10903-:d:903876
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    References listed on IDEAS

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    1. Sai Chand & Zhuolin Li & Abdulmajeed Alsultan & Vinayak V. Dixit, 2022. "Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
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    Cited by:

    1. Huiping Li & Yunxuan Li, 2023. "A Novel Explanatory Tabular Neural Network to Predicting Traffic Incident Duration Using Traffic Safety Big Data," Mathematics, MDPI, vol. 11(13), pages 1-24, June.
    2. Yongdong Wang & Haonan Zhai & Xianghong Cao & Xin Geng, 2024. "A Novel Accident Duration Prediction Method Based on a Conditional Table Generative Adversarial Network and Transformer," Sustainability, MDPI, vol. 16(16), pages 1-16, August.

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    1. Huiping Li & Yunxuan Li, 2023. "A Novel Explanatory Tabular Neural Network to Predicting Traffic Incident Duration Using Traffic Safety Big Data," Mathematics, MDPI, vol. 11(13), pages 1-24, June.

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