Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model

JIA Xingli; LI Shuangqing; YANG Hongzhi; CHEN Xingpeng

doi:10.3963/j.jssn.1674-4861.2022.05.007

Volume 40 Issue 5

Nov. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(5): 61-69

JIA Xingli, LI Shuangqing, YANG Hongzhi, CHEN Xingpeng. Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 61-69. doi: 10.3963/j.jssn.1674-4861.2022.05.007

Citation:

JIA Xingli, LI Shuangqing, YANG Hongzhi, CHEN Xingpeng. Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 61-69. doi: 10.3963/j.jssn.1674-4861.2022.05.007

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Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model

doi: 10.3963/j.jssn.1674-4861.2022.05.007

1.
School of Highway, Chang'an University, Xi'an 710064, China
2.
Xianyang Planning and Design Institute, Xianyang 712000, Shaanxi, China

Received Date: 2022-01-14
Available Online: 2022-12-05

Abstract

Abstract

In order to study the impacts of traffic incidents on freeway operation, a method for predicting the duration of freeway traffic incidents is studied. Time-dependent characteristics of traffic incidents on freeways are extracted from time series data based on the recurrent neural network (RNN) theory. The feature and the temporal attention layer of a long short-term memory (LSTM) network are combined to study the correlation between historical and current moment data. Based on attention (ATT) mechanism and the LSTM, a model for predicting the duration of traffic incidentson freeways is developed. Validation of the model is carried out based on traffic monitoring dataset collected in 2018 along the Xi'an Ring Freeway. The prediction accuracy of the proposed model is compared with the following models: back propagation neural network (BP), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). The impacts of different factors, including the types of events, weather conditions, types of vehicles, and traffic volume, on the duration is also analyzed. Study results indicate that under the condition of the same dataset, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) of the ATT-LSTM model is 24.43, 25.24%, and 21.17, respectively, which is better than that of other models. The "type of events" has the maximum weight of 0.375 among all of factors considered within the model, followed by the "number of lanes" "vehicle type" and "weather". By using the hourly traffic volume at the entrances and exits of interchanges as the correction parameter, the prediction accuracy is improved, and the MAE, MAPE, and RMSE of the model is decreased by 21.3%, 7.5%, and 16.9%, respectively. This study improves the prediction accuracy of the duration of traffic incidents on freeways and provides technical support for their safe and efficient operation.
- traffic safety,
- freeway,
- traffic incidents,
- duration,
- attention mechanism,
- long short-term memory network

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References(25)

References

[1]	姬杨蓓蓓, 张小宁, 孙立军. 交通事件持续时间预测方法综述[J]. 公路工程, 2008, 33(3): 72-79. doi: 10.3969/j.issn.1674-0610.2008.03.017 JIYANG B B, ZHANG X N, SUN L J. A review of the traffic incident duration prediction methods[J]. Highway Engineering, 2008, 33(3): 72-79. (in Chinese) doi: 10.3969/j.issn.1674-0610.2008.03.017
[2]	许宏科, 赵威, 杨孟, 等. 基于改进BPNN的高速公路交通事故持续时间预测[J]. 华东交通大学学报, 2020, 37(5): 60-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HDJT202005009.htm XU H K, ZHAO W, YANG M, et al. Prediction of expressway traffic accident duration based on the improved BPNN[J]. Journal of East China Jiaotong University, 2020, 37 (5): 60-65. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HDJT202005009.htm
[3]	LI R M, PEREIRA F C, BEN-AKIVA M E. Overview of traffic incident duration analysis and prediction[J]. European Transport Research Review, 2018, 10(2): 22-28. doi: 10.1186/s12544-018-0300-1
[4]	姬杨蓓蓓. 交通事件持续时间预测方法研究[D]. 上海: 同济大学, 2008. JIYANG B B. Research on prediction method of traffic incident duration[D]. Shanghai: Tongji University, 2008. (in Chinese)
[5]	王文博, 陈红, 韦凌翔. 交通事故时间序列预测模型研究[J]. 中国安全科学学报, 2016, 26(6): 52-56. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201606010.htm WANG W B, CHEN H, WEI L X. Research on traffic accident time series prediction model[J]. China Safety Science Journal, 2016, 26(6): 52-56. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201606010.htm
[6]	夏正丰. 高速公路交通事故持续时间概率预测模型[J]. 公路与汽运, 2016(3): 52-55. doi: 10.3969/j.issn.1671-2668.2016.03.014 XIA Z F. Probability prediction model of highway traffic accident duration[J]. Highways and Automotive Applications, 2016(3): 52-55. (in Chinese) doi: 10.3969/j.issn.1671-2668.2016.03.014
[7]	蓝岚, 路峰, 王军. 城市交通事故现场处理时间及影响因素分析[J]. 安全与环境学报, 2021, 21(3): 1173-1181. https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202103037.htm LAN L, LU F, WANG J. Analysis of the on-site processing time and the influential factors of the urban traffic accidents[J]. Journal of Safety and Environment, 2021, 21(3): 1173-1181. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202103037.htm
[8]	LIN L, WANG Q, SADEK A W. A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations[J]. Accident Analysis & Prevention, 2016, 91(6): 114-126.
[9]	GHOSB B, ASIF M T, DAUWELS J, et al. Dynamic prediction of the incident duration using adaptive feature set[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 20(11): 4019-4031.
[10]	GUO J H, LIU Z, HUANG W, et al. Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals[J]. IET Intelligent Transport Systems, 2018, 12(2): 143-150. doi: 10.1049/iet-its.2017.0144
[11]	熊励, 陆悦, 杨淑芬. 城市道路交通拥堵预测及持续时间研究[J]. 公路, 2017, 62(11): 125-134. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201711026.htm XIONG L, LU Y, YANG S F. Study on prediction and duration of urban road traffic congestion[J]. Highway, 2017, 62 (11): 125-134. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201711026.htm
[12]	翁剑成, 付宇, 林鹏飞, 等. 基于梯度推进决策树的日维度交通指数预测模型[J]. 交通运输系统工程与信息, 2019, 19 (2): 80-85. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201902012.htm WENG J C, FU Y, LIN P F, et al. GBDT method based on prediction model of daily dimension traffic index[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 80-85. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201902012.htm
[13]	CONG H Z, CHEN C, LIN P S, et al. Traffic incident duration estimation based on a dual-learning bayesian network model[J]. Transportation Research Record Journal of the Transportation Research Board, 2018, 2672(45): 196-209
[14]	何珂, 杨顺新, 郜勇刚. 基于PCA-RF组合模型的隧道交通事故持续时间预测[J]. 交通信息与安全, 2019, 37(5): 26-32. doi: 10.3963/j.issn.1674-4861.2019.05.004 HE K, YANG S X, GAO Y G. Prediction of traffic accident duration based in tunnels based on a pcarf combined model[J]. Journal of Transport Information and Safety, 2019, 37 (5): 26-32. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.05.004
[15]	李志帅, 吕宜生, 熊刚. 基于图卷积神经网络和注意力机制的短时交通流量预测[J]. 交通工程, 2019, 19(4): 15-19+28. https://www.cnki.com.cn/Article/CJFDTOTAL-DLJA201904003.htm LI Z S, LYU Y S, XIONG G. Short-term traffic flow prediction based on graph convolution neural network and attention mechanism[J]. Journal of Transportation Engineering, 2019, 19(4): 15-19+28. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLJA201904003.htm
[16]	HAMAD K, AL-RUZOUG R, ZEIADAE W, et al. Predicting incident duration using random forests[J]. Transportmetrica A: Transport Science, 2020, 16(3): 1269-1293.
[17]	LI L C, SHENG X, DU B, et al. A deep fusion model based on restricted boltzmann machines for traffic accident duration prediction[J]. Engineering Applications of Artificial Intelligence, 2020(93): 103686.
[18]	纪柯柯, 陈坚, 肖思瑶, 等. 文本数据驱动下的高速公路事故持续时间预测模型[J]. 交通信息与安全, 2020, 38(6): 9-16. doi: 10.3963/j.jssn.1674-4861.2020.06.002 JI K K, CHEN J, XIAO S Y, et al. Prediction model of expressway accident duration driven by text data[J]. Journal of Transport Information and Safety, 2020, 38(6): 9-16. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.06.002
[19]	ZOU Y J, LIN B, YANG X X, et al. Application of the bayesian model averaging in analyzing freeway traffic incident clearance time for emergency management[J]. Journal of Advanced Transportation, 2021, 2021(4): 1-9.
[20]	王婧娟, 陈庆奎. 1种时空注意力网络的交通预测模型[J]. 小型微型计算机系统, 2021, 42(2): 303-307. https://www.cnki.com.cn/Article/CJFDTOTAL-XXWX202102017.htm WANG J J, CHEN Q K. A traffic prediction model of spatio-temporal attention network[J]. Journal of Chinese Mini-Micro Computer Systems, 2021, 42(2): 303-307. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XXWX202102017.htm
[21]	龚兰兰, 凌兴宏. 长短时记忆网络公交短时客流预测实验设计[J]. 现代电子技术, 2021, 44(22): 97-100. https://www.cnki.com.cn/Article/CJFDTOTAL-XDDJ202122020.htm GONG L L, LING X H. Experimental design of short term passenger flow prediction of public transport based on long term and short term memory network[J]. Modern Electronic Technology, 2021, 44(22): 97-100. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XDDJ202122020.htm
[22]	王博文, 王景升, 王统一, 等. 基于长短时记忆网络的Encoder-Decoder多步交通流预测模型[J]. 重庆大学学报, 2021, 44(11): 71-80. https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202111009.htm WANG B W, WANG J S, WANG T Y, et al. Encoder-Decoder multi step traffic flow prediction model based on long short memory network[J]. Journal of Chongqing University, 2021, 44(11): 71-80. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202111009.htm
[23]	ESSIEN A, PETROUNIAS I, SAMPAIO S, et al. A deep-learning model for urban traffic flow prediction with traffic events mined from twitter[J]. World Wide Web-Internet and Web Information Systems, 2021, 24(4): 1345-1368
[24]	廖挥若, 杨燕. 基于注意力的时空神经网络城市区域交通流量预测[J]. 计算机应用研究, 2021, 38(10): 2935-2940. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202110009.htm LIAO H R, YANG Y. Predicting citywide traffic flow using attention-based spatial-temporal neural network[J]. Application Research of Computers, 2021, 38(10): 2935-2940. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202110009.htm
[25]	徐琛辉, 马明辉. 基于拉依达准则的交通数据粗大误差处理优化方法[J]. 上海工程技术大学学报, 2018, 32(1): 64-67. https://www.cnki.com.cn/Article/CJFDTOTAL-SGCJ201801015.htm XU C H, MA M H. Optimization method for gross error processing of traffic data based on pauta criterion[J]. Journal of Shanghai University of Engineering Science, 2018, 32(1): 64-67. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SGCJ201801015.htm

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