A Prediction Method of Daily Traffic Accident Frequency at Black Spots Based on Bi-Directional Long Short-term Memory Networks

REN Yi; YANG Renfa; ZHOU Jibiao; HU Zhenghua; ZHANG Minjie

doi:10.3963/j.jssn.1674-4861.2023.02.004

Volume 41 Issue 2

Apr. 2023

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Article Navigation > Journal of Transport Information and Safety > 2023 > 41(2): 36-49

REN Yi, YANG Renfa, ZHOU Jibiao, HU Zhenghua, ZHANG Minjie. A Prediction Method of Daily Traffic Accident Frequency at Black Spots Based on Bi-Directional Long Short-term Memory Networks[J]. Journal of Transport Information and Safety, 2023, 41(2): 36-49. doi: 10.3963/j.jssn.1674-4861.2023.02.004

Citation:

REN Yi, YANG Renfa, ZHOU Jibiao, HU Zhenghua, ZHANG Minjie. A Prediction Method of Daily Traffic Accident Frequency at Black Spots Based on Bi-Directional Long Short-term Memory Networks[J]. Journal of Transport Information and Safety, 2023, 41(2): 36-49. doi: 10.3963/j.jssn.1674-4861.2023.02.004

Citation:

REN Yi, YANG Renfa, ZHOU Jibiao, HU Zhenghua, ZHANG Minjie. A Prediction Method of Daily Traffic Accident Frequency at Black Spots Based on Bi-Directional Long Short-term Memory Networks[J]. Journal of Transport Information and Safety, 2023, 41(2): 36-49. doi: 10.3963/j.jssn.1674-4861.2023.02.004

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A Prediction Method of Daily Traffic Accident Frequency at Black Spots Based on Bi-Directional Long Short-term Memory Networks

doi: 10.3963/j.jssn.1674-4861.2023.02.004

REN Yi^{1, 2
,},
YANG Renfa^{2
,
,},
ZHOU Jibiao²,
HU Zhenghua²,
ZHANG Minjie²

1.
College of Transportation Engineering, Chang'an University, Xi'an 710064, China
2.
School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China

Received Date: 2022-05-17
Available Online: 2023-06-19

Abstract

Abstract

It is of significance to provide timely warning of accidents to traffic management departments (TMDs) and the public. Therefore, a method that predicts traffic accident frequency at black spots is proposed, using a bidirectional long short-term memory neural network (BiLSTM). An improvement in k -value selection within the traditional K-means clustering algorithm increases the efficiency of identifying black spots and the number of accidents at each of black spots is collected and used to develop an accident time series dataset. The wavelet decomposition is used to denoise the time series; a multi-layer grid search method is employed to calibrate the model parameters such based on a BiLSTM network. Traffic flow, holidays, weather, and accident environment are set as external parameters of the model and the internal parameters are obtained from the accident time series via a sliding window method. Moreover, the frequencies of accidents at black spots in the next day are predicted based on the internal and external parameters, and then an early warning model for traffic accident at black spots is proposed based on the prediction; Lastly, the accident data collected by the TMD in the City of Ningbo, Province of Zhejiang from April 2020 to September 2021 are used as the test set, the frequencies of accidents at black spots in the next day are predicted based on the accident data from the previous 7 days, and the proposed BiLSTM model is compared with other prediction models such as gated recurrent unit (GRU), long short-term memory (LSTM), back propagation (BP) neural network, autoregressive integrated moving average (ARIMA), and support vector regression (SVR). Study results show that the average accuracy of the BiLSTM, GRU, LSTM, BP, ARIMA, and SVR models for predicting daily accident frequencies at each black spots is 93.1%, 88.8%, 88.0%, 85.2%, 84.4%, and 84.2%, respectively, and the root mean square error of the above models is 0.092, 0.146, 0.142, 0.147, 0.177, and 0.176, respectively. Study results indicate that the proposed method has a higher prediction accuracy and better robustness than the traditional methods. as the number of hidden layers and nodes, etc., and a prediction model for the accident frequency is developed
- traffic safety,
- accident black spots,
- adaptive K-means clustering,
- BiLSTM neural network,
- black spot identification,
- accident prediction

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References(24)

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