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

Turn off MathJax

Article Contents

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

PDF( 4217 KB)

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

FullText(HTML)

References(24)

References

[1]	赵琳娜. 城市道路交通事故特点及解决对策[J]. 汽车与安全, 2018, 245(5): 68-70. https://www.cnki.com.cn/Article/CJFDTOTAL-QCAQ201805026.htm ZHAO L N. Characteristics of urban road traffic accidents and countermeasures[J]. Auto & Safety, 2018, 245(5): 68-70. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCAQ201805026.htm
[2]	GUO Q W. A combined prediction model composed of the GM(1, 1)model and the BP neural network for major road traffic accidents in China[R/OL]. (2022-04) [2023-05-23]. https://www.hindawi.com/journals/mpe/2022/8392759.
[3]	张志豪, 杨文忠, 袁婷婷, 等. 基于LSTM神经网络模型的交通事故预测[J]. 计算机工程与应用, 2019, 55(14): 249-253, 259. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201914037.htm ZHANG Z H, YANG W Z, YUAN T T, et al. Traffic accident prediction based on LSTM neural network model[J]. Computer Engineering and Applications, 2019, 55(14): 249-253, 259. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201914037.htm
[4]	ZHOU Z, WANG Y, XIE X, et al. RiskOracle: A minute-level citywide traffic accident forecasting framework[C]. The AAAI Conference on Artificial Intelligence, New York, USA: AAAI, 2020.
[5]	REN H L, SONG Y, WANG J W, et al. A deep learning approach to the citywide traffic accident risk prediction[C]. 21st International Conference on Intelligent Transportation Systems(ITSC), Piscataway, USA: IEEE Press, 2018.
[6]	杨文忠, 张志豪, 吾守尔·斯拉木, 等. 基于时间序列关系的GBRT交通事故预测模型[J]. 电子科技大学学报, 2020, 49(4): 615-621. https://www.cnki.com.cn/Article/CJFDTOTAL-DKDX202004021.htm YANG W Z, ZHANG Z H, WUSHOUER S, et al. GBRT traffic accident prediction model based on time series relationship[J]. Journal of University of Electronic Science and Technology of China, 2020, 49(4): 615-621. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DKDX202004021.htm
[7]	何亚雯. 基于季节性时间序列的道路交通事故数宏观态势分析及预测[D]. 北京: 北京交通大学, 2020. HE Y W. Macro situation analysis and prediction of road traffic accidents based on seasonal time series[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
[8]	马聪, 张生瑞, 马壮林, 等. 高速公路交通事故非线性负二项预测模型[J]. 中国公路学报, 2018, 31(11): 176-185. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201811020.htm MA C, ZHANG S R, MA Z L, et al. Nonlinear negative binomial regression model of expressway traffic accident frequency prediction[J]. China Journal of Highway and Transport, 2018, 31(11): 176-185. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201811020.htm
[9]	刘星良, 单珏, 刘唐志, 等. 基于交通流稳定性系数的高速公路交通事故实时风险预测[J]. 交通信息与安全, 2022, 40(4): 71-81. doi: 10.3963/j.jssn.1674-4861.2022.04.008 LIU X L, SHAN J, LIU T Z, et al. Real-time forecast models for traffic accidents on expressways using stability coefficients of traffic flow[J]. Journal of Transport Information and Safety, 2022, 40(4): 71-81. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.04.008
[10]	ALFRED L, AHMAD S. Forecasting road traffic accidents: grey system theory GM(1, 1)and grey entropy based approach(GMEPA)[J]. Archives of Current Research International, 2018, 13(2), 1-11.
[11]	LIU S B, WU C W. Road traffic accident forecast based on optimized Grey Verhulst Model[C]. Joint International Information Technology, Mechanical & Electronic Engineering Conference, Xi'an, China: Atlantis Press, 2016.
[12]	WU D L, WANG S M. Comparison of road traffic accident prediction effects based on SVR and BP neural network[C]. 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence(ICIBA), Chongqing, China: IEEE, 2020.
[13]	MARYAM P, ASRIN K, SATAR R, et al. Assessment and prediction of road accident injuries trend using time-series models in Kurdistan[J]. Burns & Trauma, 2018, 6(1): 1-8.
[14]	宋贺. 基于贝叶斯网络的道路危险货物罐车运输事故预测研究[D]. 北京: 北京交通大学, 2020. SONG H. Research on accident prediction of road dangerous goods tanker transportation based on Bayesian network[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
[15]	李兴兵, 黄力. 基于神经网络的区域交通事故数预测建模研究[J]. 信息系统工程, 2020, 317(5): 139-142, 144. https://www.cnki.com.cn/Article/CJFDTOTAL-XXXT202005064.htm LI X B, HUANG L. Research on prediction model of regional traffic accidents base on neural network[J]. China CIO News, 2020, 317(5): 139-142, 144. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XXXT202005064.htm
[16]	MA C X, DAI G W, ZHOU J B. Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5615-5624.
[17]	李文书, 邹涛涛, 王洪雁, 等. 基于双尺度长短期记忆网络的交通事故量预测模型[J]. 浙江大学学报(工学版), 2020, 54(8): 1613-1619. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202008021.htm LI W S, ZOU T T, WANG H T, et al. Highway traffic accident quantity prediction model based on dual-scale long short-term memory network[J]. Journal of Zhejiang University(Engineering Science), 2020, 54(8): 1613-1619. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202008021.htm
[18]	欧洋凯. 基于深度学习的交通预测方法[D]. 长沙: 湖南大学, 2021. OU Y K. Traffic prediction method based on deep learning[D]. Changsha: Hunan University, 2021. (in Chinese)
[19]	卢勇, 姚仕伟, 陈永胜, 等. 高速公路交通事故短时预测及节假日交通安全特征分析[J]. 公路, 2018, 63(11): 224-227. LU Y, YAO S W, CHEN Y S. et al. Short-term prediction of highway traffic accidents and analysis of traffic safety characteristics in holidays[J]. Highway, 2018, 63(11): 224-227. (in Chinese)
[20]	高珍, 高屹, 余荣杰, 等. 连续数据环境下的道路交通事故风险预测模型[J]. 中国公路学报, 2018, 31(4): 280-287. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804033.htm GAO Z, GAO Q, YU R J, et al. Road crash risk prediction model for continuous streaming data environment[J]. China Journal of Highway and Transport, 2018, 31(4): 280-287. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804033.htm
[21]	郭璘, 周继彪, 董升, 等. 基于改进K-means算法的城市道路交通事故分析[J]. 中国公路学报, 2018, 31(4): 270-279. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804032.htm GUO L, ZHOU J B, DONG S, et al. Analysis of urban road traffic accidents based on improved K-means algorithm[J]. China Journal of Highway and Transport, 2018, 31(4): 270-279. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804032.htm
[22]	陶莹, 杨锋, 刘洋, 等. K均值聚类算法的研究与优化[J]. 计算机技术与发展, 2018, 28(6): 90-92. https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ201806020.htm TAO Y, YANG F, LIU Y, et al. Research and optimization of K-means clustering algorithm [J]. Computer Technology and Development, 2018, 28(6): 90-92. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ201806020.htm
[23]	张嘉瓴. 基于数据挖掘技术的道路交通事故分析[D]. 大连: 大连理工大学, 2020. ZHANG J L. Analysis of road traffic accidents based on data mining technology[D]. Dalian: Dalian University of Technology, 2020. (in Chinese)
[24]	佘鹏果. 基于信号匹配和最优分解层的小波去噪方法研究[D]. 扬州: 扬州大学, 2014. SHE P G. Based on signal matching and the optimal decomposition level of wavelet denoising method research[D]. Yangzhou: Yangzhou University, 2014. (in Chinese)

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(18) / Tables(7)

Get Citation

PDF

XML

Article Metrics

Article views (991) PDF downloads(60)

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

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

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

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content