Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

doi:10.1088/1674-1056/acaa2f

Abstract In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory (LSTM) neural network optimized by attention mechanism (AM) and sparrow search algorithm (SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models (ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error (MSE) and reciprocal of time-to-collision (RTTC) of the ASL model are better than those of LSTM and intelligent diver model (IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However, it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

Keywords: car-following behavior deep learning weather condition heterogeneous traffic flow

Received: 20 September 2022
Revised: 07 December 2022
Accepted manuscript online: 09 December 2022

PACS:	05.60.-k	(Transport processes)
	45.70.Vn	(Granular models of complex systems; traffic flow)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 52072108), the Natural Science Foundation of Anhui Province, China (Grant No. 2208085ME148), and the Open Fund for State Key Laboratory of Cognitive Intelligence, China (Grant No. W2022JSKF0504).

Corresponding Authors: Hai-Jian Bai
E-mail: baihaijian@hfut.edu.cn

Cite this article:

Hai-Jian Bai(柏海舰), Chen-Chen Guo(过晨晨), Heng Ding(丁恒), Li-Yang Wei(卫立阳), Ting Sun(孙婷), and Xing-Yu Chen(陈星宇) Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism 2023 Chin. Phys. B 32 060507

[1] Hjelkrem O A and Ryeng E O2016 Accid. Anal. Prev. 95 227
[2] Chakrabarty N and Gupta K2013 Procedia. Soc. Behav. Sci. 104 1048
[3] Soria I, Elefteriadou L and Kondyli A2014 Simul. Modell. Pract. Theory. 40 208
[4] Hammit B E, Ghasemzadeh A, James R M, Ahmed M M and Young R K2018 Transp. Res. Part F 59 244
[5] Zheng L, Zhu C, He Z B, He T and Liu S S2019 Transportmetrica B 7 765787
[6] Li L, Jiang R, He Z B, Chen X Q and Zhou X S2020 Transp. Res. Part C 114 225
[7] Treiber M, Hennecke A and Helbing D2000 Phys. Rev. E 62 1805
[8] Jiang R, Wu Q S and Zhu Z J2001 Phys. Rev. E 64 017101
[9] Gazis D C, Herman R and Rothery R W1961 Oper. Res. 9 545
[10] Helbing D and Tilch B1998 Phys. Rev. E 58 133
[11] Bando M, Hasebe K, Nakayama A, Shibata A and Sugiyama Y1995 Phys. Rev. E 51 1035
[12] Gipps P G1981 Transp. Res. Part B 15 105
[13] Newell G F1961 Oper. Res. 9 209
[14] Huang Y, Yan X D, Li X M, Duan K, Gao Z J and Rakotonirainy A2022 Transportmetrica A
[15] Wei D L and Liu H C2013 Transp. Res. Part B 47 1
[16] Papathanasopoulou V and Antoniou C2015 Transp. Res. Part C 55 496
[17] He Z B, Zheng L and Guan W2015 Transp. Res. Part B 80 185
[18] Yu Y, He Z B and Qu X B2021 IEEE Trans. Cybern. 53 1405
[19] Chen T Y, Wong Y D, Shi X P and Yang Y Y 2021 Accid. Anal. Prev. 154 106061
[20] Li Y F, Lu X D, Ren C and Zhao H W2019 IEEE Access. 7 162778
[21] Kehtarnavaz N, Groswold N, Miller K and Lascoe P1998 IEEE Trans. Veh. Technol. 47 694
[22] Panwai S and Dia H F2007 IEEE Trans. Intell. Transp. Syst. 8 60
[23] Chong L, Abbas M M and Medina A2011 Transp. Res. Rec. 2249 44
[24] Zheng J, Suzuki K and Fujita M2013 Transp. Res. Part C 36 339
[25] Zhou M F, Qu X B and Li X P2017 Transp. Res. Part C 84 245
[26] Morton J, Wheeler T A and Kochenderfer M J2017 IEEE Trans. Intell. Transp. Syst. 18 1289
[27] Huang X L, Sun J and Sun J2018 Transp. Res. Part C 95 346
[28] Zhang X H, Sun J, Qi X and Sun J2019 Transp. Res. Part C 104 287
[29] Wang X, Jiang R, Li L, Lin Y L and Wang F Y2019 Physica A 514 786
[30] Wang X, Jiang R, Li L, Lin Y L, Zheng X H and Wang F Y2018 IEEE Trans. Intell. Transp. Syst. 19 910
[31] F. Hui, C. Wei, W. ShangGuan, R. Ando, S. Fang2022 Physica A 593 126869
[32] Ma L J and Qu S R2020 Transp. Res. Part C 120 102785
[33] Zhu M X, Wang Y H, Pu Z Y, Hu J Y, Wang X S and Ke R M2020 Transp. Res. Part C 117 102662
[34] Shi H T, Zhou Y, Wu K S, Wang X, Lin Y X, Ran B2021 Transp. Res. Part C 133 103421
[35] Hu Y P, Li Y, Huang H L, Lee J, Yuan C and Zou G Q2022 Accid Anal Prev. 165 106503
[36] Ali Y, Sharma A, Haque M M, Zheng Z and Saifuzzaman M2020 Accid Anal Prev. 144 105643
[37] Zong F, Wang M, Tang J J and Zeng M2022 Physica A 589 126625
[38] Xu J S, Hilker N, Turchet M, Al-Rijleh M K, Tu R, Wang A, Fallahshorshani M, Evans G and Hatzopoulou M2018 Transp. Res. Part D 62 90
[39] National Research Council HCM2010: highway capacity manual, 5th edn. 2010 Transp. Res. Board
[40] Montanino M and Punzo V2015 Transp. Res. Part B 80 82
[41] Khodayari A, Ghaffari A, Kazemi R and Braunstingl R2012 IEEE Trans. Syst. Man. Cybern. Part A 42 1440
[42] Thiemann C, Treiber M and Kesting A2008 Transp. Res. Rec: J. Transp. Res. Board. 2088 90
[43] Wang Z 2020 Expressway Traffic Flow Characteristic Analysis and Prediction in Rainy Environment, MS dissertation (Beijing: Beijing Jiaotong University) (in Chinese)
[44] Xu F J 2017 Calculation and Analysis of Urban Road Capacity Under Rainfall Condition, MS dissertation (Xi'an: Changan University) (in Chinese)
[45] Xue J K and Shen B2020 Syst. Sci. Control. Eng. 8 2234
[46] Jiang T, Sun H, Dai Y G and Liu D2020 J. Phys. Conf. Ser. 1607 012001
[47] Heinrich K, Zschech P, Janiesch C and Bonin M2021 Decis. Support. Syst. 143 113494
[48] Zhu M X, Wang X S, Tarko A and Fang S E2018 Transp. Res. Part C 93 425
[49] Sangster J, Rakha H and Du J2013 Transp. Res. Rec. 2390 20
[50] Qu X B, Wang S A and Zhang J2015 Transp. Res. Part B 73 91
[51] Vogel K2003 Accid. Anal. Prev. 35 427
[52] Jian M Y and Shi J2020 Saf. Sci. 122 104536
[53] Ding H, Pan H, Bai H J, Zheng X Y, Chen J and Zhang W H2022 Physica A 596 127154
[54] Chen D J, J. Laval, Zheng Z D and Ahn S2012 Transp. Res. Part B 46 744
[55] Laval J A2011 Transp. Res. Part B 45 385

[1]	Disruption prediction based on fusion feature extractor on J-TEXT Wei Zheng(郑玮), Fengming Xue(薛凤鸣), Zhongyong Chen(陈忠勇), Chengshuo Shen(沈呈硕), Xinkun Ai(艾鑫坤), Yu Zhong(钟昱), Nengchao Wang(王能超), Ming Zhang(张明),Yonghua Ding(丁永华), Zhipeng Chen(陈志鹏), Zhoujun Yang(杨州军), and Yuan Pan(潘垣). Chin. Phys. B, 2023, 32(7): 075203.
[2]	Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems Xiao-Gang Wang(汪小刚) and Hao-Yu Wei(魏浩宇). Chin. Phys. B, 2022, 31(9): 094202.
[3]	Development of an electronic stopping power model based on deep learning and its application in ion range prediction Xun Guo(郭寻), Hao Wang(王浩), Changkai Li(李长楷),Shijun Zhao(赵仕俊), Ke Jin(靳柯), and Jianming Xue(薛建明). Chin. Phys. B, 2022, 31(7): 073402.
[4]	Data-driven parity-time-symmetric vector rogue wave solutions of multi-component nonlinear Schrödinger equation Li-Jun Chang(常莉君), Yi-Fan Mo(莫一凡), Li-Ming Ling(凌黎明), and De-Lu Zeng(曾德炉). Chin. Phys. B, 2022, 31(6): 060201.
[5]	Fringe removal algorithms for atomic absorption images: A survey Gaoyi Lei(雷高益), Chencheng Tang(唐陈成), and Yueyang Zhai(翟跃阳). Chin. Phys. B, 2022, 31(5): 050313.
[6]	Review on typical applications and computational optimizations based on semiclassical methods in strong-field physics Xun-Qin Huo(火勋琴), Wei-Feng Yang(杨玮枫), Wen-Hui Dong(董文卉), Fa-Cheng Jin(金发成), Xi-Wang Liu(刘希望), Hong-Dan Zhang(张宏丹), and Xiao-Hong Song(宋晓红). Chin. Phys. B, 2022, 31(3): 033101.
[7]	Deep learning for image reconstruction in thermoacoustic tomography Qiwen Xu(徐启文), Zhu Zheng(郑铸), and Huabei Jiang(蒋华北). Chin. Phys. B, 2022, 31(2): 024302.
[8]	Modeling the heterogeneous traffic flow considering the effect of self-stabilizing and autonomous vehicles Yuan Gong(公元) and Wen-Xing Zhu(朱文兴). Chin. Phys. B, 2022, 31(2): 024502.
[9]	Learning physical states of bulk crystalline materials from atomic trajectories in molecular dynamics simulation Tian-Shou Liang(梁添寿), Peng-Peng Shi(时朋朋), San-Qing Su(苏三庆), and Zhi Zeng(曾志). Chin. Phys. B, 2022, 31(12): 126402.
[10]	RNAGCN: RNA tertiary structure assessment with a graph convolutional network Chengwei Deng(邓成伟), Yunxin Tang(唐蕴芯), Jian Zhang(张建), Wenfei Li(李文飞), Jun Wang(王骏), and Wei Wang(王炜). Chin. Phys. B, 2022, 31(11): 118702.
[11]	Soliton, breather, and rogue wave solutions for solving the nonlinear Schrödinger equation using a deep learning method with physical constraints Jun-Cai Pu(蒲俊才), Jun Li(李军), and Yong Chen(陈勇). Chin. Phys. B, 2021, 30(6): 060202.
[12]	High speed ghost imaging based on a heuristic algorithm and deep learning Yi-Yi Huang(黄祎祎), Chen Ou-Yang(欧阳琛), Ke Fang(方可), Yu-Feng Dong(董玉峰), Jie Zhang(张杰), Li-Ming Chen(陈黎明), and Ling-An Wu(吴令安). Chin. Phys. B, 2021, 30(6): 064202.
[13]	Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space Wanrun Jiang(姜万润), Yuzhi Zhang(张与之), Linfeng Zhang(张林峰), and Han Wang(王涵). Chin. Phys. B, 2021, 30(5): 050706.
[14]	Handwritten digit recognition based on ghost imaging with deep learning Xing He(何行), Sheng-Mei Zhao(赵生妹), and Le Wang(王乐). Chin. Phys. B, 2021, 30(5): 054201.
[15]	An image compressed sensing algorithm based on adaptive nonlinear network Yuan Guo(郭媛), Wei Chen(陈炜), Shi-Wei Jing(敬世伟). Chin. Phys. B, 2020, 29(5): 054203.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

Cite this article:

Online attention