An extended smart driver model considering electronic throttle angle changes with memory
Congzhi Wu(武聪智)1,2,3, Hongxia Ge(葛红霞)1,2,3,†, and Rongjun Cheng(程荣军)1,2,3
1 Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China; 2 Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China; 3 National Traffic Management Engineering and Technology Research Centre Ningbo University Sub-centre, Ningbo 315211, China
Abstract Based on the fact that the electronic throttle angle effect performs well in the traditional car following model, this paper attempts to introduce the electronic throttle angle into the smart driver model (SDM) as an acceleration feedback control term, and establish an extended smart driver model considering electronic throttle angle changes with memory (ETSDM). In order to show the practicability of the extended model, the next generation simulation (NGSIM) data was used to calibrate and evaluate the extended model and the smart driver model. The calibration results show that, compared with SDM, the simulation value based on the ETSDM is better fitted with the measured data, that is, the extended model can describe the actual traffic situation more accurately. Then, the linear stability analysis of ETSDM was carried out theoretically, and the stability condition was derived. In addition, numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow. The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow, which shows the feasibility and superiority of the proposed model to a certain extent.
(Granular models of complex systems; traffic flow)
Fund: Project supported by the Natural Science Foundation of Zhejiang Province, China (Grant No. LY20G010004) and the the Program of Humanities and Social Science of Education Ministry of China (Grant No. 20YJA630008), and the K. C. Wong Magna Fund in Ningbo University, China.
Corresponding Authors:
Hongxia Ge
E-mail: gehongxia@nbu.edu.cn
Cite this article:
Congzhi Wu(武聪智), Hongxia Ge(葛红霞), and Rongjun Cheng(程荣军) An extended smart driver model considering electronic throttle angle changes with memory 2022 Chin. Phys. B 31 010504
[1] Bando M, Hasebe K and Nakayama 1995 Phys. Rev. E51 1035 [2] Newell G F 1961 Oper. Res.9 209 [3] Pipes L A 1953 J. Appl. Phys.24 274 [4] Ma C X, Wang H, Pan F Q and Wang X 2018 PLOS One13 e0198931 [5] Zhang Y, Wang S, Pan D B and Zhang G 2021 Phys. A561 125269 [6] Zhu W X and Li D Z 2018 Phys. A492 2154 [7] Zhu W X and Zhang H M 2018 Phys. A496 274 [8] Li F Y, Yang H, Yang B, Zheng T X and Zhang C 2018 Nolinear Dyn.93 1923 [9] Cao B G 2020 Phys. A539 122903 [10] Zhu W X and Zhang L D 2016 Phys. A449 265 [11] Ou H and Tang T Q 2018 Phys. A495 260 [12] Zhai C and Wu W T 2020 Eur. Phys. J. B93 52 [13] Qin Y Y and Wang H 2021 Transport. A-Transport Sci.17 59 [14] Jiao Y L, Cheng R J and Ge H X 2020 Math. Prob. Engineer.2020 1 [15] Jiang C T, Cheng R J and Ge H X 2019 Phys. A513 465 [16] Chang Y Y and Cheng R J 2019 Phys. A531 121751 [17] Cheng R J, Ge H X and Wang J F 2018 Appl. Math. Comp.332 493 [18] Zhai Q T, Ge H X and Cheng R J 2018 Phys. A490 774 [19] Cheng R J, Ge H X and Wang J F 2017 Phys. Lett. A381 2608 [20] Treiber M, Hennecke A and Helbing D 2000 Phys. Rev. E62 1805 [21] Hook J, Sedky S and Kondoz A 2021 Cogn. Syst. Res.65 40 [22] Guo L T, Zhao X M, Yu S W, Li X H and Shi Z K 2017 Phys. A471 436 [23] Moser D, Waschl H, Kirchsteiger H, Schmied R and del Re L 2015 Control Conference July 15-17, 2015, Linz, Austria, p. 3383 [24] Zong F, Wang M, Tang M, Li X Y and Zeng M 2021 IEEE Access9 66241 [25] Kesting A, Treiber M and Helbing D 2010 Phil. Trans. Royal Soc. A368 4585 [26] Treiber M and Kesting A 2018 Transport. Res. Part B-Meth.117 613 [27] Liu L, Zhu L L and Yang D 2016 Appl. Math. Comp.273 706 [28] Qin Y Y, Wang H and Ran B 2018 IET Intelligent Transport Systems12 921 [29] Milanes V and Shladover S E 2014 Transport. Res. Part C48 285 [30] Lu C R and Aakre A 2018 European Transport Research Review10 49 [31] Sharma A, Zheng Z D, Bhaskar A and Haque M M 2019 Transport.Res. Part B126 256 [32] Yu S W and Shi Z K 2015 Phys. A428 206 [33] Gipps P G 1981 Transport. Res. Part B15 105 [34] Ioannou P and Xu T 1994 IVHS Journal1 413
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