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An Improved Cellular Automata Traffic Flow Model Considering Driving Styles

Author

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  • Tianjun Feng

    (School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun 130118, China
    Engineering Research Center of Traffic Disaster Prevention and Control in Cold Region, Changchun 130118, China)

  • Keyi Liu

    (School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun 130118, China
    Engineering Research Center of Traffic Disaster Prevention and Control in Cold Region, Changchun 130118, China)

  • Chunyan Liang

    (School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun 130118, China
    Engineering Research Center of Traffic Disaster Prevention and Control in Cold Region, Changchun 130118, China)

Abstract

An improved cellular automata model (CA model) considering driving styles is proposed to analyze traffic flow characteristics and study traffic congestion’s dissipation mechanism. The data were taken from a particular case in the Next Generation Simulation (NGSIM) program, which selected US-101 as the survey location from 7:50 a.m.–8:05 a.m. to investigate vehicle trajectory information. Different driving styles and the differences in vehicle parameters (speed, acceleration, deceleration, etc.) were obtained using principal component analysis and the k-means clustering method. The selected model was proposed for improvement based on analyzing the existing CA models and combining them with the actual road conditions. Considerations of driving styles and two operation mechanisms (over-acceleration and speed adaptation) were introduced in the improved model. The result obtained after the traffic simulation shows that the improved CA model is effective, and the mutual transformation of different traffic flow phases can be simulated. In the improved CA model, dissipating traffic congestion effectively and balancing the overall flow of the road are realized to improve the traffic capacity up to around 115% compared to the NaSch model and meet the demand of all kinds of drivers expecting to drive at the safest distance, which provides a theoretical basis for relieving traffic congestion. The various driving styles in terms of safety, comfort, and effectiveness are performed differently in the improved CA model. An aggressive driving style contributes to increasing traffic capacity up to around 181% compared to a calm driving style, while the calm style contributes to maintaining traffic flow stability.

Suggested Citation

  • Tianjun Feng & Keyi Liu & Chunyan Liang, 2023. "An Improved Cellular Automata Traffic Flow Model Considering Driving Styles," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:952-:d:1025351
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    References listed on IDEAS

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    1. Jin, Cheng-Jie & Wang, Wei, 2011. "The influence of nonmonotonic synchronized flow branch in a cellular automaton traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4184-4191.
    2. Cremer, M. & Ludwig, J., 1986. "A fast simulation model for traffic flow on the basis of boolean operations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(4), pages 297-303.
    3. Pan, Wei & Xue, Yu & He, Hong-Di & Lu, Wei-Zhen, 2018. "Impacts of traffic congestion on fuel rate, dissipation and particle emission in a single lane based on Nasch Model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 154-162.
    4. Kaur, Ramanpreet & Sharma, Sapna, 2017. "Analysis of driver’s characteristics on a curved road in a lattice model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 59-67.
    5. Helbing, Dirk, 1996. "Derivation and empirical validation of a refined traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 233(1), pages 253-282.
    6. Chmura, Thorsten & Herz, Benedikt & Knorr, Florian & Pitz, Thomas & Schreckenberg, Michael, 2014. "A simple stochastic cellular automaton for synchronized traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 332-337.
    7. Gao, Kun & Jiang, Rui & Wang, Bing-Hong & Wu, Qing-Song, 2009. "Discontinuous transition from free flow to synchronized flow induced by short-range interaction between vehicles in a three-phase traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(15), pages 3233-3243.
    8. Coifman, Benjamin & Li, Lizhe, 2017. "A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 362-377.
    9. Lárraga, M.E. & Alvarez-Icaza, L., 2010. "Cellular automaton model for traffic flow based on safe driving policies and human reactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(23), pages 5425-5438.
    10. Shuaiyang Jiao & Shengrui Zhang & Bei Zhou & Zixuan Zhang & Liyuan Xue, 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    11. Daganzo, Carlos F., 1995. "Requiem for second-order fluid approximations of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 277-286, August.
    12. Tian, Junfang & Treiber, Martin & Ma, Shoufeng & Jia, Bin & Zhang, Wenyi, 2015. "Microscopic driving theory with oscillatory congested states: Model and empirical verification," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 138-157.
    13. Kokubo, Satoshi & Tanimoto, Jun & Hagishima, Aya, 2011. "A new Cellular Automata Model including a decelerating damping effect to reproduce Kerner’s three-phase theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 561-568.
    14. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    15. Li, Xiaoqin & Fang, Kangling & Peng, Guanghan, 2017. "A new lattice model of traffic flow with the consideration of the drivers’ aggressive characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 315-321.
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    Cited by:

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    2. Liu, Keyi & Feng, Tianjun, 2023. "Heterogeneous traffic flow cellular automata model mixed with intelligent controlled vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Xiaoyuan Feng & Yue Chen & Hongbo Li & Tian Ma & Yilong Ren, 2023. "Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction," Sustainability, MDPI, vol. 15(9), pages 1-13, May.

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