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A Data-Driven Approach for Traffic Crash Prediction: A Case Study in Ningbo, China

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Abstract

In the past few years, fully connected Long Short-Term Memory (FC-LSTM) network has been widely used to predict traffic crashes in urban areas. This article attempts to improve the traditional prediction model by adopting Convolutional Long Short-Term Memory (ConvLSTM) network. ConvLSTM can effectively capture the spatial and temporal characteristics of traffic crashes within road network. It overcomes the shortcoming of the FC-LSTM model that ignores the spatial characteristics of traffic crashes. Therefore, the ConvLSTM model shows excellent performance when predicting traffic crashes. To verify the effectiveness of the ConvLSTM, this study uses historical crash data in the City of Ningbo to train the model and compares the result with that from FC-LSTM. The results show that ConvLSTM has better accuracy and lower loss values. Moreover, the model has higher calculation efficiency. Therefore, the ConvLSTM model is more suitable for predicting traffic crashes.

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Acknowledgments

The authors thank the kind help from the editor and anonymous referees, whose comments improve the quality of this paper.

Funding

This work was supported in part by National Natural Science Foundation of China (No. 52002282), Philosophy and Social Science Foundation of Zhejiang Province (No. 21NDJC163YB & 22NDQN279YB), Natural Science Foundation of Zhejiang Province (No. LQ19E080003 & LQ18D010008).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Ningbo University of Technology, Fenghua Rd. #201, Ningbo, 315211, Zhejiang Province, People’s Republic of China

    Zhenghua Hu

  2. College of Information Science & Electronic Engineering, Zhejiang University, Zheda Rd.38#, Hangzhou, 310027, Zhejiang Province, People’s Republic of China

    Zhenghua Hu & Kejie Huang

  3. Ningbo Jianan Intelligent Electric Co., Ltd, No. 711, Keji Road, Cixi City, Ningbo City, Zhejiang Province, People’s Republic of China

    Zhenghua Hu & Enyou Zhang

  4. Department of Transportation Engineering, Tongji University, Caoan Rd. #4800, Shanghai, 201804, China

    Jibiao Zhou

Authors
  1. Zhenghua Hu
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  2. Jibiao Zhou
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  4. Enyou Zhang
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Correspondence to Jibiao Zhou.

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Hu, Z., Zhou, J., Huang, K. et al. A Data-Driven Approach for Traffic Crash Prediction: A Case Study in Ningbo, China. Int. J. ITS Res. 20, 508–518 (2022). https://doi.org/10.1007/s13177-022-00307-3

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  • DOI: https://doi.org/10.1007/s13177-022-00307-3

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