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Car-Following Models: A Multidisciplinary Review (2304.07143v4)

Published 14 Apr 2023 in eess.SY, cs.AI, and cs.SY

Abstract: Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS). Insights from the model of car-following behavior help us understand the causes of various macro phenomena that arise from interactions between pairs of vehicles. Car-following models encompass multiple disciplines, including traffic engineering, physics, dynamic system control, cognitive science, machine learning, and reinforcement learning. This paper presents an extensive survey that highlights the differences, complementarities, and overlaps among microscopic traffic flow and control models based on their underlying principles and design logic. It reviews representative algorithms, ranging from theory-based kinematic models, Psycho-Physical Models, and Adaptive cruise control models to data-driven algorithms like Reinforcement Learning (RL) and Imitation Learning (IL). The manuscript discusses the strengths and limitations of these models and explores their applications in different contexts. This review synthesizes existing researches across different domains to fill knowledge gaps and offer guidance for future research by identifying the latest trends in car following models and their applications.

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References (76)
  1. Reuschel A. Fahrzeugbewegungen in der Kolonne. Österreichisches Ingenieur-Archiv. 1950;4:193-215. [The Movement of a Column of Vehicles when the Leading Vehicle Is Uniformly Accelerated or Decelerated. Mag. of the Austrian Engineer and Architect Assoc.]
  2. Edie LC. Car-following and steady-state theory for noncongested traffic. Operational Research. 1961;9(1):66-76.
  3. Rothery RW. Car following models. Trac Flow Theory. 1992.
  4. Brackstone M, McDonald M. Car-following: a historical review. Transportation Research Part F: Traffic Psychology and Behavior. 1999;2(4):181-96.
  5. Olstam JJ, Tapani A. Comparison of Car-following models. Swedish National Road and Transport Research Institute; 2004. Report No.: 960.
  6. Toledo T. Driving Behavior: models and challenges. Transport Reviews. 2007;27(1):65-84.
  7. Li Y, Sun D. Microscopic car-following model for the Traffic flow: the state of the art. Journal of Control Theory and Applications. 2012;10(2):133-43.
  8. Saifuzzaman M, Zheng Z. Incorporating human-factors in car-following models: a review of recent developments and research needs. Transportation Research Part C: Emerging Technologies. 2014;48:379-403.
  9. Herrey EM, Herrey H. Principles of Phys. Appl. to Traf. movements and road conditions. American Journal of Physics. 1945;13(1):1-14.
  10. Pipes LA. An operational Analysis of Traf. dynamics. Journal of Applied Physics. 1953;24(3):274-81.
  11. Kometani E, Sasaki T. On the stability of traffic flow (report-I). Journal of the Operations Research Society of Japan. 1958;2(1):11-26.
  12. Kometani E, Sasaki T. A safety index for traffic with linear spacing. Operations Research. 1959;7(6):704-20.
  13. Gipps PG. A Behavioral car-following model for Computer simulation. Transportation Research Part B: Methodological. 1981;15(2):105-11.
  14. Spyropoulou I. Simulation Using Gipps’ Car-Following Model—An In-Depth Analysis. Transportmetrica. 2007;3(3):231-45.
  15. Krauß S. Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics.
  16. Wilson RE. An Analysis of Gipps’ Car-following Model of Highway Traffic. IMA Journal of Applied Mathematics. 2001;66:509-37.
  17. Newell GF. A simplified car-following theory: a lower-order model. Transportation Research Part B: Methodological. 2002;36(3):195-205.
  18. Xu T, Laval J. Statistical inference for two-regime stochastic car-following models. Transportation Research Part B: Methodological. 2020 Apr 1;134:210-28.
  19. Wolfram S. Statistical mechanics of cellular automata. Reviews of Modern Physics. 1983;55(3):601.
  20. Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. Journal de physique I. 1992;2(12):2221-29.
  21. Takayasu M, Takayasu H. 1/f noise in a traffic model. fractals. 1993 Dec;1(04):860-6.
  22. Chakroborty P, Maurya AK. Microscopic analysis of cellular automata based traffic flow models and an improved model. Transport reviews. 2008 Nov 1;28(6):717-34.
  23. Daganzo CF. In Traffic flow, cellular automata = kinematic waves. Transportation Research Part B: Methodological. 2006;40(5):396-403.
  24. Mallikarjuna C, Rao KR. Cellular automata model for heterogeneous traffic. Journal of Advanced Transportation. 2009 Jul;43(3):321-45.
  25. Vasic J, Ruskin HJ. Cellular automata simulation of traffic including cars and bicycles. Physica A: Statistical Mechanics and its Applications. 2012 Apr 15;391(8):2720-9.
  26. Singh MK, Ramachandra Rao K. Simulation of Signalized Intersection with Non-Lane-Based Heterogeneous Traffic Conditions Using Cellular Automata. Transportation Research Record. 2023:03611981231211317.
  27. May AD, Keller HE. Non-integer car-following models. Highway Research Record. 1967;199(1):19-32.
  28. Heyes MP, Ashworth R. Further Research on car-following models. Transportation Research. 1972;6(3):287-91.
  29. Ceder A, May AD. Further evaluation of single-and two-regime Traffic flow models. Transportation Research Record. 1976;567.
  30. Bexelius S. An extended model for car-following. Transportation Research. 1968;2(1):13-21.
  31. Treiterer J, Myers J. The hysteresis phenomenon in Traffic flow. In: Transportation and Traffic Theory. 1974;6:13-38.
  32. Helbing D, Tilch B. Generalized force model of Traffic dynamics. Physical Review E. 1998;58(1):133.
  33. Nagatani T. Modified KdV equation for jamming transition in the continuum models of traffic. Physica A: Statistical Mechanics and its Applications. 1998;261(3-4):599-607.
  34. Treiber M, Helbing D. Memory effects in microscopic traffic models and wide scattering in flow-density data. Physical Review E. 2003 Oct 21;68(4):046119.
  35. Milanés V, Shladover SE. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C: Emerging Technologies. 2014;48:285-300.
  36. Treiber M, Kesting A. The Intelligent driver model with stochasticity-new insights into Traffic flow oscillations. Transportation Research Procedia. 2017;23:174-187.
  37. Forbes TW, Simpson ME. Driver-and-Vehicle Response in Freeway Deceleration Waves. Transportation Science. 1968;2(1):77-104.
  38. Michaels RM, Cozan LW. Perceptual and field factors causing lateral displacement. Highway Research Record. 1963;25.
  39. Todosiev EP. The action point model of the driver-vehicle System. The Ohio State University; 1963.
  40. Evans L, Rothery R. Experimental measurements of perceptual thresholds in car-following. 52nd Annual Meeting of the Highway Research Board. 1973.
  41. Fritzsche HT, Ag DB. A model for Traffic simulation. Traffic Engineering & Control. 1994;35(5):317-321.
  42. Ferrari P. The reliability of the motorway transport System. Transportation Research Part B: Methodological. 1988;22(4):291-310.
  43. Winsum W. The human element in car following models. Transportation Research Part F: Traffic Psychology and Behaviour. 1999;2:207-211.
  44. Kikuchi S, Chakroborty P. Car-following model based on fuzzy inference System. Transportation Research Record. 1992;82.
  45. Chakroborty P, Kikuchi S. Evaluation of the General Motors based car-following models and a proposed fuzzy inference model. Transportation Research Part C: Emerging Technologies. 1999;7(4):209-235.
  46. Chai C, Wong YD. Fuzzy cellular automata model for signalized intersections. Computer‐Aided Civil and Infrastructure Engineering. 2015 Dec;30(12):951-64.
  47. Suzuki H, Nakatsuji T. Effect of adaptive cruise Control (ACC) on Traffic throughput: numerical example on actual freeway corridor. JSAE review. 2003;24(4):403-410.
  48. Li PY, Shrivastava A. Traffic flow stability induced by constant time headway policy for adaptive cruise Control vehicles. Transportation Research Part C: Emerging Technologies. 2002;10(4):275-301.
  49. Helly W. Simulation of bottlenecks in single lane traffic flow. In: Theory of Traffic Flow proceedings; 1959. Amsterdam: Elsevier.
  50. Jin IG, Orosz G. Optimal Control of connected vehicle System with communication delay and driver reaction time. IEEE Transactions on Intelligent Transportation Systems. 2016;18(8):2056-2070.
  51. Zhang L, Orosz G. Motif-based design for connected vehicle System in presence of heterogeneous connectivity structures and time delays. IEEE Transactions on Intelligent Transportation Systems. 2016;17(6):1638-1651.
  52. Azizıaghdam ET, Alankuş OB. Longitudinal Control of Autonomous Vehicles Consisting Powertrain With Nonlinear Characteristics. IEEE Transactions on Intelligent Vehicles. 2022;7(1):133-142.
  53. Panwai S, Dia H. Neural agent car-following models. IEEE Transactions on Intelligent Transportation Systems. 2007;8(1):60-70.
  54. Wei D, Liu H. Analysis of asymmetric driving behavior using a self-learning approach. Transportation Research Part B: Methodological. 2013;47:1-14.
  55. Papathanasopoulou V, Antoniou C. Towards data-driven car-following models. Transportation Research Part C: Emerging Technologies. 2015 Jun 1;55:496-509.
  56. Ma L, Qu S. A sequence to sequence learning based car-following model for multistep predictions considering reaction delay. Transportation Research Part C: Emerging Technologies. 2020;120:102785.
  57. Ng AY, Russell S. Algorithms for inverse reinforcement learning. In ICML; 2000; Vol. 1. p. 2.
  58. Koutsopoulos HN, Farah H. Latent class model for car following behavior. Transportation Research Part B: methodological. 2012;46(5):563-578.
  59. Ioannu PA, Chien CC. Autonomous Intelligent Cruise Control. IEEE Transactions on Vehicular Technology. 1993;42(4):657-672.
  60. Desjardins C, Chaib-Draa B. Cooperative adaptive cruise Control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems. 2011;12(4):1248-1260.
  61. Ye L, Yamamoto T. Modeling connected and autonomous vehicles in heterogeneous traffic flow. Physica A: Statistical Mechanics and its Applications. 2018;490:269-277.
  62. Zhu WX, Zhang HM. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model. Physica A: Statistical Mechanics and its Applications. 2018;496:274-285.
  63. Talebpour A, Mahmassani HS. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation research part C: emerging technologies. 2016;71:143-163.
  64. Rahman MS, Abdel-Aty M. Longitudinal safety evaluation of connected vehicles’ platooning on expressways. Accident Analysis & Prevention. 2018;117:381-391.
  65. Garg M, Bouroche M. Can connected autonomous vehicles improve mixed traffic safety without compromising efficiency in realistic scenarios?. IEEE Transactions on Intelligent Transportation Systems. 2023 Jan 26.
  66. Rawashdeh ZY, Mahmud SM. A novel algorithm to form stable clusters in vehicular ad hoc networks on highways. Eurasip journal on wireless communications and networking. 2012 Dec;2012:1-3.
  67. Jia D, Ngoduy D. Platoon based cooperative driving model with consideration of realistic inter-vehicle communication. Transportation Research Part C: Emerging Technologies. 2016;68:245-264.
  68. Navas F, Milanés V. Mixing V2V-and non-V2V-equipped vehicles in car following. Transportation research part C: emerging technologies. 2019;108:167-181.
  69. Jia D, Ngoduy D. Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication. Transportation Research Part B: Methodological. 2016;90:172-191.
  70. Nishi R. Theoretical conditions for restricting secondary jams in jam-absorption driving scenarios. Physica A: Statistical Mechanics and its Applications. 2020;542:123393.
  71. Li H, Jin S. Intelligent vehicle car-following model based on cyber physical system and its simulation under mixed traffic flow. Physica A: Statistical Mechanics and its Applications. 2024 Jan 15;634:129482.
  72. Van Lint JWC, Calvert SC. A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modeling driver distraction. Transportation Research Part B: Methodological. 2018;117:63-86.
  73. Montanino M, Punzo V. Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns. Transportation Research Part B: Methodological. 2015 Oct 1;80:82-106.
  74. Arman MA, Tampère CM. Lane-level trajectory reconstruction based on data-fusion. Transportation Research Part C: Emerging Technologies. 2022 Dec 1;145:103906.
  75. Makridis MA, Kouvelas A. Adaptive physics-informed trajectory reconstruction exploiting driver behavior and car dynamics. Scientific Reports. 2023 Jan 20;13(1):1121.
  76. Barmpounakis E, Geroliminis N. On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment. Transportation research part C: emerging technologies. 2020 Feb 1;111:50-71.
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