Traffic and Transportation Journal

Article

Cellular Automata Model for Traffic Flow with Optimised Stochastic Noise Parameter

Sheng LIU, Dewen KONG, SettingsLishan SUN

DOI:https://doi.org/10.7307/ptt.v34i4.4049

Keywords:heterogeneous traffic flow, stochastic noise parameter, cellular automata, car-following behaviour

Abstract

Based on the existing safe distance cellular automata model, an improved cellular automata model based on realistic human reactions is proposed in this paper, which aims to reproduce the characteristics of congested traffic flow. In the proposed model, the stochastic noise param-eter is optimised by considering driving behavioural dif-ference. The relative speed, gap and acceleration of the front vehicle are introduced into the optimised stochastic noise parameter oriented to describing the asymmetric acceleration behaviour of drivers in congestion. The sim-ulation results show that an uneven distribution of accel-eration trajectories of vehicles experiencing congestion exhibited on the spatial-temporal diagram of the pro-posed model is reproduced. Based on the analysis of the NGSIM, compared with the model with traditional sto-chastic noise parameter, the vehicles that move accord-ing to the proposed model can follow more easily and more realistically. Then the actual gap of vehicles can be better reflected by the proposed model and the change of vehicle speed is more stable. Additionally, the traffic efficiency from two aspects of flow and speed shows that the proposed model can significantly improve the traffic efficiency in the medium high density region.

References

[1] Qiu XP, Yu D, Sun RX, Yang D. Cellular automata model based on safety distance. Journal of Transportation Systems Engineering and Information Technology. 2015;15: 54-60. doi: 10.16097/j.cnki.1009-6744.2015.02.009.

[2] Li X, Wu Q, Jiang R. Cellular automaton model considering the velocity effect of a car on the successive car. Physical Review E. 2001;64: 066128. doi: 10.1103/PhysRevE.64.066128.

[3] Wang H, Jin CJ. Traffic Flow Theory and Application. Beijing: China Communications Press; 2020.

[4] Kong DW, Sun LS, Li J, Xu Y. Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata. Physica A. 2021;562: 125329. doi: 10.1016/j.physa.2020.125329.

[5] Ye L, Yamamoto T. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput. Physica A. 2018;512: 588-597. doi: 10.1016/j.physa.2018.08.083.

[6] Olia A, Razavi S, Abdulha B, Abdelgawad H. Traffic capacity implications of automated vehicles mixed with regular vehicles. Journal of Intelligent Transportation Systems. 2018;22: 244-262. doi: 10.1080/15472450.2017.1404680.

[7] Jian Z, Tie QT, Shao WY. An improved car-following model accounting for the preceding car’s taillight. Physica A. 2018;492: 1831-1837. doi: 10.1016/j.physa.2017.11.100.

[8] Shang XC, et al. Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior. Physica A. 2020;560: 125220. doi: 10.1016/j.physa.2020.125220.

[9] Bandini S, Mondini M, Vizzari G. Modelling negative interactions among pedestrians in high density situations. Transportation Research Part C. 2014;40: 251-270. doi: 10.1016/j.trc.2013.12.007.

[10] Jia B, Gao ZY, Li KP. Models and simulations of traffic system based on the theory of cellular automaton. Beijing: Science Press; 2007.

[11] Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. J. Phys. I France. 1992;2: 2221-2229. doi: 10.1051/jp1:1992277.

[12] Barlovic R, Santen L, Schadschneider A, Schreckenberg M. Metastable states in cellular automata for traffic flow. The European Physical Journal B. 1998;5: 793-800. doi: 10.1007/s100510050504.

[13] Kerner BS, Klenov SL, Wolf DE. Cellular automata approach to three-phase traffic theory. Journal of Physics A: Mathematical and General. 2002;35: 9971. doi: 10.1088/0305-4470/35/47/303.

[14] Li XB, Wu QS, Jiang R. Cellular automaton model considering the velocity effect of a car on the successive car. Physical Review E. 2001;64: 066128. doi: 10.1103/PhysRevE.64.066128.

[15] Knospe W, Santen L, Schadschneider A, Schreckenberg M. Towards a realistic microscopic description of highway traffic. Physica A. 2000;33: L477. doi: 10.1088/0305-4470/33/48/103.

[16] Jiang R, Wu QS. Cellular automata models for synchronized traffic flow. Journal of Physics A: Mathematical and General. 2003;36: 381. doi: 10.1088/0305-4470/36/2/307.

[17] Lee HK, Barlovic R, Schreckenberg M, Kim D. Mechanical restriction versus human overreaction triggering congested traffic states. Physical Review Letters. 2004;92: 238702. doi: 10.1103/PhysRevLett.92.238702.

[18] Lu B. Modeling and analysis of car-following behavior using data-driven methods. PhD thesis. Southwest Jiaotong University Chengdu; 2017.

[19] Guzman HA, Larraga ME, Alvarez-Icaza L, Carvajal J. A multi-gears cellular automata model for traffic flow based on kinetics theory. In: Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017). 2017. p. 153-158. doi: 10.2991/amms-17.2017.35.

[20] Qiu XP, Ma LN, Zhou XX, Yang D. The mixed traffic flow of manual-automated driving based on safety distance. Journal of Transportation Systems Engineering and Information Technology. 2016;16: 101-108+124. doi: 10.16097/j.cnki.1009-6744.2016.04.015.

[21] Yang D, et al. A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect. Physica A. 2015;424: 62-72. doi: 10.1016/j.physa.2014.12.020.

[22] Larraga ME, Alvarez-Icaza L. Cellular automaton model for traffic flow based on safe driving policies and human reactions. Physica A. 2010;389: 5425-5438. doi: 10.1016/j.physa.2010.08.020.

[23] Larraga ME, Alvarez-Icaza L.Cellular automata model for traffic flow with safe driving conditions. Chinese Physics B. 2014;23: 050701. doi: 10.1088/1674-1056/23/5/050701.

[24] Li X, Li XG, Xiao Y, Bin J. Modeling mechanical restriction differences between car and heavy truck in two-lane cellular automata traffic flow model. Physica A. 2016;451: 49-62. doi: 10.1016/j.physa.2015.12.157.

[25] Guzman HA, Larraga ME, Alvarez-Icaza L, Carvajal J. A cellular automata model for traffic flow based on kinetics theory, vehicles capabilities and driver reactions. Physica A. 2018;491: 528-548. doi: 10.1016/j.physa.2017.09.094.

[26] Kerner BS. Empirical macroscopic features of spatial-temporal traffic patterns at highway bottlenecks. Physical Review E. 2002;65: 046138. doi: 10.1103/PhysRevE.65.046138.

[27] Kerner BS, Klenov SL. Microscopic theory of spatial-temporal congested traffic patterns at highway bottlenecks. Physical Review E. 2003;68: 036130. doi: 10.1103/PhysRevE.68.036130.

[28] Kerner BS. Three-phase traffic theory and highway capacity. Physica A. 2004;333: 379-440. doi: 10.1016/j.physa.2003.10.017.

[29] Huang YX. Experimental research and modeling on the evolution of traffic oscillation. PhD thesis. University of Science and Technology of China; 2019.

[30] Liu CC. Analysis of the evolution characteristics of traffic flow induced by moving bottleneck. MS thesis. Beijing Jiaotong University; 2018.

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Published

07.07.2022

Issue

2022 (Vol 34), Issue 4

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