| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics |
| Hong-Sheng Qi(祁宏生)1,† and Yu-Yan Ying(应雨燕)2 |
1 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;
2 College of Polytechnic, Zhejiang University, Hangzhou 310015, China |
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Abstract The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models, especially the car-following (CF) models. These models of the movement of vehicles serve as the backbone of traffic flow analysis, simulation, autonomous vehicle development, etc. Two-dimensional (2D) vehicular movement is basically stochastic and is the result of interactions between a driver's behavior and a vehicle's characteristics. Current microscopic models either neglect 2D noise, or overlook vehicle dynamics. The modeling capabilities, thus, are limited, so that stochastic lateral movement cannot be reproduced. The present research extends an intelligent driver model (IDM) by explicitly considering both vehicle dynamics and 2D noises to formulate a stochastic 2D IDM model, with vehicle dynamics based on the stochastic differential equation (SDE) theory. Control inputs from the vehicle include the steer rate and longitudinal acceleration, both of which are developed based on an idea from a traditional intelligent driver model. The stochastic stability condition is analyzed on the basis of Lyapunov theory. Numerical analysis is used to assess the two cases: (i) when a vehicle accelerates from a standstill and (ii) when a platoon of vehicles follow a leader with a stop-and-go speed profile, the formation of congestion and subsequent dispersion are simulated. The results show that the model can reproduce the stochastic 2D trajectories of the vehicle and the marginal distribution of lateral movement. The proposed model can be used in both a simulation platform and a behavioral analysis of a human driver in traffic flow.
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Received: 20 July 2022
Revised: 29 August 2022
Accepted manuscript online: 05 September 2022
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PACS:
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05.60.-k
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(Transport processes)
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45.70.Vn
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(Granular models of complex systems; traffic flow)
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| Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2021YFE0194400), the National Natural Science Foundation of China (Grant Nos. 52272314 and 52131202), the Fund for Humanities and Social Science from the Ministry of Education of China (Grant No. 21YJCZH116), and the Public Welfare Scientific Research Project (Grant No. LGF22E080007). |
Corresponding Authors:
Hong-Sheng Qi
E-mail: qihongsheng@zju.edu.cn
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Cite this article:
Hong-Sheng Qi(祁宏生) and Yu-Yan Ying(应雨燕) A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics 2023 Chin. Phys. B 32 044501
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[1] Sun Y, Ge H and Cheng R 2019 Physica A 534 122377
[2] Xu L, Li Y and Feng H Z 2018 Proceedings of the Chinese Automation Congress (CAC), November 3-December 2, 2018, Xian, China, p. 1146
[3] Li T, Ngoduy D, Hui F and Zhao X 2020 Transportmetrica B: Transport Dynamics 8 150
[4] Wang Z, Ge H and Cheng R 2020 Physica A 540 122988
[5] Navas F and Milanés V 2019 Transportation Research Part C: Emerging Technologies 108 167
[6] Jiang Y, Wang S, Yao Z, Zhao B and Wang Y 2021 Physica A 582 126262
[7] Liang J Y, Zhang L L, Luan X D, Guo J L, Lao S Y and Xie Y X 2017 Acta Phys. Sin. 66 194501 (in Chinese)
[8] Bando M, Hasebe K, Nakayama A, Shibata A and Sugiyama Y 1995 Phys. Rev. E 51 1035
[9] Ni X and Huang H 2019 Chin. Phys. B 28 098901
[10] Treiber M and Kesting A 2017 Transpation Research Procedia 23 174
[11] Azam M S, Bhaskar A and Haque M M 2022 Transpmetrica A: Transp. Sci. 18 367
[12] Delpiano R, Herrera M J C and Coeymans A J E 2015 Transpmetrica A: Transp. Sci. 11 636
[13] Sharath M N and Velaga N R 2020 Transportation Research Part C: Emerging Technologies 120 102780
[14] Tian J, Zhu C, Chen D, Jiang R, Wang G and Gao Z 2021 Transpation Research Part B: Methodological 143 160
[15] Gunay B 2007 Transpation Research Part B: Methodological 41 722
[16] Ni D H 2015 Traffic flow theory: Characteristics, Experimental Methods, and Numerical Techniques (Butterworth-Heinemann: Elsevier Science)
[17] Pipes L A 1953 J. Appl. Phys. 24 274
[18] Gazis D C, Herman R and Rothery R W 1961 Operations Research 9 545
[19] Gipps P G 1981 Transpation Research Part B: Methodological 15 105
[20] Helbing D, Hennecke A, Shvetsov V and Treiber M 2002 Mathematical and Computer Modelling 35 517
[21] Liu Y J, Zhang H L and He L 2012 Chin. Phys. Lett. 29 104502
[22] Delpiano R, Herrera J C, Laval J and Coeymans J E 2020 Transpation Research Part C: Emerging Technologies 114 504
[23] Li Y, Zhang L, Peeta S, Pan H, Zheng T, Li Y and He X 2015 Nonlinear Dyn. 80 227
[24] Peng G H 2020 Chin. Phys. B 29 084501
[25] Zeng J W, Qian Y S, Wei X T and Feng X 2018 Chin. Phys. B 27 124502
[26] Chen J, Chen J Y, Li M and Hu M B 2019 Chin. Phys. B 28 048901
[27] Jiang R, Jin C J, Zhang H M, Huang Y X, Tian J F, Wang W, Hu M B, Wang H and Jia B 2018 Transpation Research Part C: Emerging Technologies 94 83
[28] Bouadi M, Jia B, Jiang R, Li X and Gao Z Y 2022 arXiv: 2203.04877
[29] Laval J A, Toth C S and Zhou Y 2014 Transpation Research Part B: Methodological 70 228
[30] Xu T and Laval J 2020 Transpation Research Part B: Methodological 134 210
[31] Yuan K, Laval J, Knoop V L, Jiang R and Hoogendoorn S P 2017 Transp. B: Transp. Dyn. 7 915
[32] Tomas-Gabarron J B, Egea-Lopez E and Garcia-Haro J 2013 IEEE Trans. Intell. Transp. Syst. 14 1930
[33] Allen R W, Szostak H T, Klyde D H, Rosenthal T J and Owens K J 1992 Vehicle Dynamic Stability and Rollover (Michigan: National Highway Traffic Safety Administration)
[34] Rajamani R 2012 Vehicle Dynamics and Control (Boston, MA: Springer US)
[35] Makridis M, Fontaras G, Ciuffo B and Mattas K 2019 Transp. Res. Rec. 2673 762
[36] Kesting A, Treiber M and Helbing D 2010 Phil. Trans. R. Soc. A 368 4585
[37] Mao X R 1991 Stability of Stochastic Differential Equations with Respect to Semimartingales (Harlow: Longman) |
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