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Anticipation model based on a modified fuzzy logic approach

Anticipation model based on a modified fuzzy logic approach

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Car-following behaviour is an important problem in terms of road safety, since it represents, alone, almost 70% of road accidents caused by not maintaining a safe braking distance between the moving cars. The inappropriate anticipation of drivers to keep safety distance is the main reason for accidents. In this study, the authors present an artificial intelligence anticipation model for car-following problem based on a fuzzy logic approach. This system will estimate the velocity of the leading vehicle in the near future. Moreover, they have replaced the old methods used in the third step of fuzzy logical approach, the defuzzification, by a novel method based on a metaheuristic algorithm, i.e. Tabu search, in order to adapt effectively to the environment's instability. The results of experiments, conducted using the next generation simulation dataset to validate the proposed model, indicate that the vehicle trajectories simulated based on the new model are in compliance with the actual vehicle trajectories in terms of deviation and estimated velocities. Moreover, they show that the proposed model guarantees road safety in terms of harmonisation between the gap distance and the calculated safety distance.

References

    1. 1)
      • 1. Treiber, M., Kesting, A., Helbing, D.: ‘Delays, inaccuracies and anticipation in microscopic traffic models’, Physica A, 2006, 360, pp. 7188.
    2. 2)
      • 2. Papageorgiou, M.: ‘Concise encyclopedia of traffic and transportation systems’, in ‘Traffic and transportation systems’ (Pergamon Press, Munich, Germany, 1991, 1st edn.).
    3. 3)
      • 3. Sameh, E.H.., Alexis, D., Stéphane, E.: ‘How to combine reactivity and anticipation: the case of conflicts resolution in a simulated road traffic’. Proc. Int. Conf. Int. Workshop on Multi-Agent Systems and Agent-Based Simulation, Berlin, Heidelberg, 2002, pp. 8296.
    4. 4)
      • 4. René, M., Alexis, C., Jean-Michel, A., et al: ‘Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation’, Appl. Intell., 2008, 28, pp. 121138.
    5. 5)
      • 5. Golias, J., Yannis, G., Antoniou, C.: ‘Impact of advanced driver assistance systems on urban traffic network conditions’, Eur. J. Transp. Infrastruct. Res., 2001, 1, pp. 277289.
    6. 6)
      • 6. Zadeh, L.A.: ‘Fuzzy sets’, J. Inf. Control, 1965, 8, pp. 338353.
    7. 7)
      • 7. Yager, R.R., Zadeh, L.A.: ‘An Introduction to fuzzy logic applications in intelligent Systems’ (Springer, New York, NY, USA, 1992), p. 165.
    8. 8)
      • 8. ‘U.S. Federal Highway Administration. Next Generation Simulation Program (NGSIM)’. Available at http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm, accessed 14 August 2018.
    9. 9)
      • 9. Aghabayk, K., Sarvi, M., Young, W.: ‘A state-of-the-art review of car-following models with particular considerations of heavy vehicles’, Transp. Rev. A, Transnatl. Transdiscip. J., 2015, 35, pp. 82105.
    10. 10)
      • 10. Saifuzzaman, M., Zheng, Z.: ‘Incorporating human-factors in car-following models: a review of recent developments and research needs’, J. Transp. Res. C, Emerg. Technol., 2014, 48, pp. 379403.
    11. 11)
      • 11. Elefteriadou, L.: ‘An Introduction to traffic flow theory’ (Springer-Verlag Press, New York, NY, USA, 2014), p. 251.
    12. 12)
      • 12. Bando, M., Hasebe, K., Shibata, A., et al: ‘Dynamical model of traffic congestion and numerical simulation’, Phys. Rev. E, 1995, 51, pp. 10351042.
    13. 13)
      • 13. Helbing, D., Tilch, B.: ‘Generalized force model of traffic dynamics’, Phys. Rev. E, 2001, 58, pp. 133138.
    14. 14)
      • 14. Jiang, R., Wu, Q., Zhu, Z.: ‘Full velocity difference model for a car-following theory’, Phys. Rev. E, 2001, 64.
    15. 15)
      • 15. Bullen, A.G.R.: ‘Development of compact micro-simulation for analysing freeway operations and design’, Transp. Res. Rec. J. Transp. Res. Board, 1982, 841, pp. 1518.
    16. 16)
      • 16. Ge, H., Dai, S., Xue, Y., et al: ‘Stabilization analysis and modified Korteweg–de Vries equation in a cooperative driving system’, Phys. Rev. E, 2005, 71.
    17. 17)
      • 17. Yu, L., Shi, Z.K., Zhou, B.: ‘Kink–antikink density wave of an extended car-following model in a cooperative driving system’, J. Commun. Nonlinear Sci. Num. Simul., 2008, 13, pp. 21672176.
    18. 18)
      • 18. Li, Y.F., Sun, D.H., Liu, W.N., et al: ‘Modeling and simulation for microscopic traffic flow based on multiple headway, velocity and acceleration difference’, J. Nonlinear Dyn., 2011, 66, pp. 1528.
    19. 19)
      • 19. Peng, G., Sun, D.: ‘A dynamical model of car-following with the consideration of the multiple information of preceding cars’, J. Phys. Lett. A, 2010, 374, pp. 16941698.
    20. 20)
      • 20. Gipps, P.G.: ‘A behavioural car-following model for computer simulation’, J. Transp. Res. B, Methodol., 1981, 15, pp. 105111.
    21. 21)
      • 21. Wilson, R.: ‘An analysis of Gipps's car-following model of highway traffic’, J. Appl. Math., 2001, 66, pp. 509537.
    22. 22)
      • 22. Barcelo, J., Ferrer, J., Grau, R., et al: ‘A route based version of the AIMSUN2 micro-simulation model’. Proc. Int. Conf. on Steps Forward. Intelligent Transport Systems World Congress, Yokohama, Japan, November 1995, p. 1971.
    23. 23)
      • 23. Liu, R., Van, V., Wating, D.P.: ‘DRACULA: dynamic route assignment combining user learning and microsimulation’. Proc. Int. Conf. on Transportation Planning Methods, London, England, September 1995, pp. 143152.
    24. 24)
      • 24. Yang, D., Zhu, L.L., Yu, D., et al: ‘An enhanced safe distance Car-following model’, J. Shanghai Jiaotong Univ. (Sci.), 2014, 19, pp. 115122.
    25. 25)
      • 25. Broqua, F., Lerner, G., Mauro, V., et al: ‘Cooperative driving: basic concepts and a first assessment of ‘intelligent cruise control’ strategies’. Proc. Int. Conf. on Advanced Telematics in Road Transport, Brussels, Belgium, 1991, pp. 908929.
    26. 26)
      • 26. Qiang, L., Lunhui, X., Zhihui, C., et al: ‘Simulation analysis and study on car-following safety distance model based on braking process of leading vehicle’. Proc. Int. Conf. on Intelligent Control and Automation, Taipei, Taiwan, June 2011, pp. 740743.
    27. 27)
      • 27. Zheng, L.J., Tian, C., Sun, D.H., et al: ‘A new car-following model with consideration of anticipation driving behavior’, J. Nonlinear Dyn., 2012, 70, pp. 12051211.
    28. 28)
      • 28. Deng, H., Michael-Zhang, H.: ‘Driver anticipation in car following’, Transp. Res. Rec. J. Transp. Res. Board, 2012, 2316, pp. 3137.
    29. 29)
      • 29. Yi-Rong, K., Di-Hua, S., Shu-Hong, Y.: ‘A new car-following model considering driver's individual anticipation behavior’, J. Nonlinear Dyn., 2015, 82, pp. 12931302.
    30. 30)
      • 30. Hao, H., Ma, W., Xu, H.: ‘A fuzzy logic-based multi-agent car-following model’, J. Transp. Res. C, Emerg. Technol., 2016, 69, pp. 477496.
    31. 31)
      • 31. McDonald, M., Wu, J., Brackstone, M.: ‘Development of a fuzzy logic based microscopic motorway simulation model’. Proc. Int. Conf. on Intelligent Transportation Systems, Boston, MA, USA, November 1997, pp. 8287.
    32. 32)
      • 32. Rosen, R.: ‘Anticipatory systems’ (Pergamon Press, Oxford, UK, 1985), p. 472.
    33. 33)
      • 33. Johnsson, G., Rumer, K.: ‘Drivers braking reaction times’, J. Hum. Factors Ergon. Soc., 1971, 13, pp. 2327.
    34. 34)
      • 34. Homburger, W., Keefer, L., McGrath, W.: ‘Transportation and traffic engineering handbook’ (Prentice-Hall Press, 1982), p. 883.
    35. 35)
      • 35. Olson, P.: ‘Parameters affecting stopping sight distance’, J. Natl. Coop. Highw. Res. Prog. Rep., 1984, 270.
    36. 36)
      • 36. Chen, Y., Wang, C.: ‘Vehicle safety distance warning system: a novel algorithm for vehicle safety distance calculating between moving cars’. Vehicular Technology Conf., Dublin, Ireland, April 2007, pp. 25702574.
    37. 37)
      • 37. Zadeh, L.A.: ‘The concept of a linguistic variable and its application to approximate reasoning’, J. Inf. Sci., 1975, 8, pp. 199249.
    38. 38)
      • 38. Massad, E., Ortega, N.R.S., De-Barros, L., et al: ‘Fuzzy logic in action: applications in epidemiology and beyond’, Part of the Studies in Fuzziness and Soft Computing book series, 8 (Springer-Verlag, Berlin, Heidelberg, Germany, 2008).
    39. 39)
      • 39. Bennajeh, A., Kebair, F., Ben-Said, L., et al: ‘Multiagent cooperation for decision-making in the car-following behavior’. Int. Conf. on Computational Collective Intelligence, Halkidiki, Greece, September 2016, pp. 391401.
    40. 40)
      • 40. Abhay, D.L., Alexander, M.H., Steven, L.J.: ‘Comparative study of simulated annealing, Tabu search, and the genetic algorithm for calibration of the microsimulation model’, J. Simul. Trans. Soc. Model. Simul. Int., 2017, 93, pp. 2133.
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