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Three-phase traffic theory and highway capacity

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  • Kerner, Boris S.

Abstract

Hypotheses and some results of the three-phase traffic theory by the author are compared with results of the fundamental diagram approach to traffic flow theory. A critical discussion of model results about congested pattern features which have been derived within the fundamental diagram approach to traffic flow theory and modeling is made. The empirical basis of the three-phase traffic theory is discussed and some new spatial–temporal features of the traffic phase “synchronized flow” are considered. A probabilistic theory of highway capacity is presented which is based on the three-phase traffic theory. In the frame of this theory, the probabilistic nature of highway capacity in free flow is linked to an occurrence of the first order local phase transition from the traffic phase “free flow” to the traffic phase “synchronized flow”. A numerical study of congested pattern highway capacity based on simulations of a KKW cellular automata model within the three-phase traffic theory is presented. A congested pattern highway capacity which depends on features of congested spatial–temporal patterns upstream of a bottleneck is studied.

Suggested Citation

  • Kerner, Boris S., 2004. "Three-phase traffic theory and highway capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 379-440.
    • Handle: RePEc:eee:phsmap:v:333:y:2004:i:c:p:379-440
    DOI: 10.1016/j.physa.2003.10.017
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    Cited by:

    1. Wu, Xinkai & Liu, Henry X. & Geroliminis, Nikolas, 2011. "An empirical analysis on the arterial fundamental diagram," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 255-266, January.
    2. Minh Sang Pham Do & Ketoma Vix Kemanji & Man Dinh Vinh Nguyen & Tuan Anh Vu & Gerrit Meixner, 2023. "The Action Point Angle of Sight: A Traffic Generation Method for Driving Simulation, as a Small Step to Safe, Sustainable and Smart Cities," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    3. Guan, Wei & He, Shuyan, 2008. "Statistical features of traffic flow on urban freeways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 944-954.
    4. Huiming Duan & Xinping Xiao & Lingling Pei, 2017. "Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model," Complexity, Hindawi, vol. 2017, pages 1-16, July.
    5. Redhu, Poonam & Gupta, Arvind Kumar, 2016. "Effect of forward looking sites on a multi-phase lattice hydrodynamic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 150-160.
    6. Muhammad Akram Adnan & Norliana Sulaiman & Tuan Badrol Hisham Tuan Besar, 2023. "Comparative Evaluation of US HCM & Malaysian HCM for a Multilane Highway," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(11), pages 134-151, November.
    7. Zhou, Shirui & Ling, Shuai & Zhu, Chenqiang & Tian, Junfang, 2022. "Cellular automaton model with the multi-anticipative effect to reproduce the empirical findings of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    8. Xia, Dong & Zheng, Linjiang & Tang, Yi & Cai, Xiaolin & Chen, Li & Sun, Dihua, 2022. "Dynamic traffic prediction for urban road network with the interpretable model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    9. Junwei Zeng & Yongsheng Qian & Fan Yin & Leipeng Zhu & Dejie Xu, 2022. "A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate," Computational and Mathematical Organization Theory, Springer, vol. 28(2), pages 178-192, June.
    10. Jabari, Saif Eddin & Zheng, Jianfeng & Liu, Henry X., 2014. "A probabilistic stationary speed–density relation based on Newell’s simplified car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 205-223.
    11. Han, Yu & Zhang, Mingyu & Guo, Yanyong & Zhang, Le, 2022. "A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    12. Qiang Shang & Yang Yu & Tian Xie, 2022. "A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    13. Hu, Xiaojian & Qiao, Longqi & Hao, Xiatong & Lin, Chenxi & Liu, Tenghui, 2022. "Research on the impact of entry points on urban arterial roads in the framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    14. Jing, Dian & Yao, Enjian & Chen, Rongsheng, 2023. "Moving characteristics analysis of mixed traffic flow of CAVs and HVs around accident zones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    15. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
    16. Wang, Lichao & Yang, Min & Li, Ye & Hou, Yiqi, 2022. "A model of lane-changing intention induced by deceleration frequency in an automatic driving environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    17. Hu, Xiaojian & Hao, Xiatong & Wang, Han & Su, Ziyi & Zhang, Fang, 2020. "Research on on-street temporary parking effects based on cellular automaton model under the framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    18. Qian, Wei-Liang & F. Siqueira, Adriano & F. Machado, Romuel & Lin, Kai & Grant, Ted W., 2017. "Dynamical capacity drop in a nonlinear stochastic traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 328-339.
    19. Liu, Qingchao & Cai, Yingfeng & Jiang, Haobin & Lu, Jian & Chen, Long, 2018. "Traffic state prediction using ISOMAP manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 532-541.
    20. Tang, Jinjun & Liu, Fang & Zhang, Weibin & Zhang, Shen & Wang, Yinhai, 2016. "Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 635-648.

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