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An Agent-Based Cellular Automata Model for Urban Road Traffic Flow Considering Connected and Automated Vehicles

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Green Transportation and Low Carbon Mobility Safety (GITSS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 944))

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Abstract

Considering the development of the vehicle to vehicle (V2V) technology and the popularisation of connected and automated vehicles (CAVs), for an extended period, urban roads will be in a mixed traffic flow scene where CAVs and human-driven vehicles (HDVs) coexist. This paper uses an agent-based cellular automata model to establish a micro-traffic simulation framework for urban roads, called the ABCA-MS model. Considering the characteristics of the intermittent flow of urban roads and signal light control, corresponding car-following and lane-changing rules are established and applied to simulate mixed traffic flow containing CAVs. The simulation results show that the traffic efficiency and the permeability of CAVs show a positive correlation; under the given traffic volume condition, the critical CAVs penetration rate for a traffic state change from congestion to unblocked is 0.4. When the penetration rate of CAVs is in the range of 0–0.4, the improvement of road traffic efficiency is the most significant, and the effect of improvement gradually slows down with the increase of CAVs penetration. Even with a low penetration rate of CAVS, the road capacity can be effectively improved, and the traffic pressure can be alleviated.

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References

  1. Bagloee SA, Tavana M, Asadi M, Oliver T (2016) Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J Mod Transp 24:284–303

    Article  Google Scholar 

  2. Hong D, Kimmel S, Boehling R, Camoriano N, Cardwell W, Jannaman G, Purcell A, Ross D, Russel E (2008) Development of a semi-autonomous vehicle operable by the visually-impaired. In: Proceedings of IEEE International conference on multisensor fusion and integration for intelligent systems, pp 539–544

    Google Scholar 

  3. Litman T (2015) Autonomous vehicle implementation predictions. Victoria Transport Policy Institute, No 15-3326

    Google Scholar 

  4. Fenton RE, Mayhan RJ (1991) Automated highway studies at the ohio state university-an overview. IEEE Trans Veh Technol 40:100–113

    Article  Google Scholar 

  5. Treiber M, Hennecke A, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulation. Phys Rev E 62:1805–1824

    Article  MATH  Google Scholar 

  6. Shladover SE, Desoer CA, Hedrick JK, Tomizuka M (1991) Automatic vehicle control developments in the PATH program. IEEE Trans Veh Technol 40:114–130

    Article  Google Scholar 

  7. Rajamani R, Tan HS, Law BK, Zhang WB (2000) Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons. IEEE Trans Control Syst Veh Technol 8:695–708

    Article  Google Scholar 

  8. Milanés V, Shladover SE, Spring J, Nowakowski C, Kawazoe H, Nakamura M (2014) Cooperative adaptive cruise control in real traffic situations. IEEE Trans Intell Transp Syst 15:296–305

    Article  Google Scholar 

  9. Milanés V, Shladover SE (2014) Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transp Res Pt C Emerg Technol 48:285–300

    Article  Google Scholar 

  10. Yao ZH, Xu T, Jiang YS, Hu R (2021) Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time. Phys A 561:125218

    Article  MathSciNet  MATH  Google Scholar 

  11. Yao ZH, Hu R, Jiang YS, Xu TR (2020) Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways. J Saf Res 75:262–274

    Article  Google Scholar 

  12. Yao ZH, Hu R, Wang Y, Jiang YS, Ran B, Chen YR (2019) Stability analysis and the fundamental diagram for mixed connected automated and human-driven vehicles. Phys A 533:121931

    Article  MathSciNet  MATH  Google Scholar 

  13. Jiang R, Wu Q, Zhu Z (2001) Full velocity difference model for a car-following theory. Phys Rev E 64:01701

    Article  Google Scholar 

  14. Kuang H, Xu ZP, Li XL, Lo SM (2017) An extended car-following model accounting for the average headway effect in intelligent transportation system. Phys A 471:778–787

    Article  MathSciNet  MATH  Google Scholar 

  15. Kerner BS (2018) Physics of automated driving in framework of three-phase traffic theory. Phys Rev E 97:042303

    Article  Google Scholar 

  16. Rios-Torres J, Malikopoulos AA (2018) Impact of partial penetrations of connected and automated vehicles on fuel consumption and traffic flow. IEEE Trans Intell Transp Syst 3:1–10

    Google Scholar 

  17. Gipps PG (1986) A model for the structure of lane-changing decisions. Transp Res Pt B-Methodol 20:403–414

    Article  Google Scholar 

  18. Wang M, Treiber M, Daamen W, Hoogendoorn SP, Arem BV (2013) Modelling supported driving as an optimal control cycle: framework and model characteristics. Transp Res Pt C-Emerg Technol 36:547–563

    Article  Google Scholar 

  19. Talebpour HS, Mahmassani SH (2011) Hamdar, multiregime sequential risk-taking model of car-following behavior specification, calibration, and sensitivity analysis. Transp Res Record 2260:60–66

    Article  Google Scholar 

  20. Talebpour HS (2016) Mahmassani, Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transp Res Pt C-Emerg Technol 71:143–163

    Article  Google Scholar 

  21. Jiang R, Wu QS (2003) Cellular automata models for synchronized traffic flow. J Phys A Math Gen 36:381–390

    Article  MathSciNet  MATH  Google Scholar 

  22. Kerner BS, Klenov SL, Wolf DE (2002) Cellular automata approach to three-phase traffic theory. J Phys A Math Gen 35:9971–10013

    Article  MathSciNet  MATH  Google Scholar 

  23. Lee HK, Barlovic R, Schreckenberg M, Kim D (2004) Mechanical restriction versus human overreaction triggering congested traffic states. Phys Rev Lett 92:1–4

    Article  Google Scholar 

  24. Jiang R, Wu QS (2006) The adaptive cruise control vehicles in the cellular automata model. Phys Lett A 359:99–102

    Article  Google Scholar 

  25. Yuan YM, Jiang R, Hu MB, Wu QS, Wang RL (2009) Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles: a hybrid modelling approach. Phys A 388:2483–2491

    Article  Google Scholar 

  26. Lo SC, Hsu CH (2010) Cellular automata simulation for mixed manual and automated control traffic. Math Comput Modell 51:1000–1007

    Article  MATH  Google Scholar 

  27. Yang D, Qiu XP, Ma LL, Liang HB (2017) Cellular automata–based modeling and simulation of a mixed traffic flow of manual and automated vehicles. Transp Res Record 2622:105–116

    Article  Google Scholar 

  28. Wu JC, Chen BK, Zhang K, Zhou J, Miao LX (2018) Ant pheromone route guidance strategy in intelligent transportation systems. Phys A 503:591–603

    Article  Google Scholar 

  29. Zhao HT, Liu XR, Chen XX, Lu JC (2018) Cellular automata model for traffic flow at intersections in internet of vehicles. Phys A 494:40–51

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhao HT, Zhao X, Lu JC, Xin LY (2020) Cellular automata model for urban road traffic flow considering internet of vehicles and emergency vehicles. J Comput Sci 47:101221

    Article  Google Scholar 

  31. Zhao HT, Lin L, Xu CP, Li ZX, Zhao X (2020) Cellular automata model under Kerner’s framework of three-phase traffic theory considering the effect of forward–backward vehicles in internet of vehicles. Phys A 553:124213

    Article  Google Scholar 

  32. Gao K, Jiang R, Hu SX, Wang BH, Wu QS (2007) Cellular-automaton model with velocity adaptation in the framework of Kerner’s three-phase traffic theory. Phys Rev E 76:1–7

    Article  Google Scholar 

  33. Chen BK, Sun D, Zhou J, Wong WF, Ding ZJ (2020) A future intelligent traffic system with mixed autonomous vehicles and human-driven vehicles. Inf Sci 529:59–72

    Article  MathSciNet  Google Scholar 

  34. Nagel K, Schreckenberg M (1992) A cellular automata model for freeway traffic. J Phys I(2):2221–2229

    Google Scholar 

  35. Tanimoto J, Futamata M, Tanaka M (2020) Automated vehicle control systems need to solve social dilemmas to be disseminated. Chaos Solitons Fractals 138:109861

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhou YJ, Zhu HB, Guo MM, Zhou JL (2020) Impact of CACC vehicles’ cooperative driving strategy on mixed four-lane highway traffic flow. Phys A 540:122721

    Article  Google Scholar 

  37. Vranken T, Sliwa B, Wietfeld C, Schreckenberg M (2021) Adapting a cellular automata model to describe heterogeneous traffic with human-driven, automated, and communicating automated vehicles. Phys A 570:125792

    Google Scholar 

  38. Wang J, Lv W, Jiang Y, Qin S, Li J (2021) A multi-agent based cellular automata model for intersection traffic control simulation. Phys A 584:126356

    Article  Google Scholar 

  39. Chowdhury D, Wolf DE, Schreckenberg M (1997) Particle hopping models for two-lane traffic with two kinds of vehicles: effects of lane changing rules. Phys A 235:417–439

    Article  Google Scholar 

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Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant No. 52072286, 72074149), and the Fundamental Research Funds for the Central Universities (Grant No. 2020VI002).

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Authors and Affiliations

  1. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China

    Wang Jinghui, Lv Wei, Jiang Yajuan, Qin Shuangshuang & Huang Guangchen

  2. China Research Center for Emergency Management, Wuhan University of Technology, Wuhan, China

    Lv Wei

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  1. Wang Jinghui
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  2. Lv Wei
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  3. Jiang Yajuan
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  4. Qin Shuangshuang
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  5. Huang Guangchen
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Corresponding author

Correspondence to Lv Wei .

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Editors and Affiliations

  1. Department of Transportation Engineering, Beijing Institute of Technology, Beijing, China

    Wuhong Wang

  2. School of Civil Engineering, Tsinghua University, Beijing, China

    Jianping Wu

  3. Beijing Institute of Technology, Beijing, China

    Xiaobei Jiang

  4. School of Civil Engineering, Tsinghua University, Beijing, China

    Ruimin Li

  5. Beijing Institute of Technology, Beijing, China

    Haodong Zhang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jinghui, W., Wei, L., Yajuan, J., Shuangshuang, Q., Guangchen, H. (2023). An Agent-Based Cellular Automata Model for Urban Road Traffic Flow Considering Connected and Automated Vehicles. In: Wang, W., Wu, J., Jiang, X., Li, R., Zhang, H. (eds) Green Transportation and Low Carbon Mobility Safety. GITSS 2021. Lecture Notes in Electrical Engineering, vol 944. Springer, Singapore. https://doi.org/10.1007/978-981-19-5615-7_16

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  • DOI: https://doi.org/10.1007/978-981-19-5615-7_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5614-0

  • Online ISBN: 978-981-19-5615-7

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