Investigating the Large-Scale Effects of Human Driving Behavior on Vehicular Traffic Flow

  • Manuel LindorferEmail author
  • Christian Backfrieder
  • Christoph F. Mecklenbräuker
  • Gerald Ostermayer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)


In recent years, understanding and modeling the human driver in the scope of traffic simulations has received considerable attention. With the advent and the ongoing development of new technologies in the field of Intelligent Transportation Systems, we are consequently moving towards an era where a majority of driving-related tasks will presumably be carried out by autonomous systems rather than humans. Notwithstanding, the transition from today’s conventional traffic to tomorrow’s highly automated traffic will not take place overnight. Up to that point, the available transportation infrastructure will most likely be shared among both human-driven and (partially) automated vehicles. Considering such scenarios of mixed traffic is therefore inevitable when developing new concepts and applications for the use in ITS, and requires a proper modeling of the human driver for simulation purposes. Although there have been diverse ways of integrating human factors with traffic simulation models, most existing studies focus on the impacts of human driving behavior in very constrained scenarios such as isolated platoons or bottleneck situations rather than on their large-scale effects. In this paper, we address this particular issue by performing large-scale simulations to investigate the impacts of human behavior on vehicular traffic flow under varying traffic conditions. We show how specific factors such as delayed reaction, distracted or anticipatory driving affect traffic efficiency and safety in terms of travel time, fuel consumption and accident frequency.


Human factors Driver behavior modelling Traffic simulation 



This project has been co-financed by the European Union using financial means of the European Regional Development Fund (EFRE). Further information to IWB/EFRE is available at


  1. 1.
    Pipes, L.A.: An operational analysis of traffic dynamics. J. Appl. Phys. 24, 274–281 (1953)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Newell, G.F.: Nonlinear effects in the dynamics of car-following. Oper. Res. 9, 209–229 (1961)CrossRefGoogle Scholar
  3. 3.
    Wiedemann, R.: Simulation des Straßenverkehrsflusses. Institute for Traffic Engineering, University of Karlsruhe (1974)Google Scholar
  4. 4.
    Gipps, P.: A behavioural car-following model for computer simulation. Transp. Res. Part B: Methodol. 15, 101–111 (1981)CrossRefGoogle Scholar
  5. 5.
    Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E 51, 1035–1042 (1995)CrossRefGoogle Scholar
  6. 6.
    Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62, 1805–1824 (2000)CrossRefGoogle Scholar
  7. 7.
    Kerner, B.S., Rehborn, H.: Experimental features and characteristics of traffic jams. Phys. Rev. E 53, R1297–R1300 (1996)CrossRefGoogle Scholar
  8. 8.
    Cassidy, M.J., Bertini, R.L.: Some traffic features at freeway bottlenecks. Transp. Res. Part B: Methodol. 33, 25–42 (1999)CrossRefGoogle Scholar
  9. 9.
    Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067 (2001)CrossRefGoogle Scholar
  10. 10.
    Treiber, M., Kesting, A., Helbing, D.: Delay, inaccuracies and anticipation in microscopic traffic models. Phys. A 360, 71–88 (2006)CrossRefGoogle Scholar
  11. 11.
    Andersen, G.J., Sauer, C.W.: Optical information for car-following: the driving by visual angle (DVA) model. Hum. Factors 49, 878–896 (2007)CrossRefGoogle Scholar
  12. 12.
    Kesting, A.: Microscopic modeling of human and automated driving – towards adaptive cruise control. University of Technology Dresden (2008)Google Scholar
  13. 13.
    Yang, S., Peng, H.: Development of an errorable car-following driver model. Veh. Syst. Dyn. 48, 751–773 (2009)CrossRefGoogle Scholar
  14. 14.
    Jin, S., Wang, D.H., Huang, Z.Y., Tao, P.F.: Visual angle model for car-following theory. Phys. A 390, 1931–1940 (2011)CrossRefGoogle Scholar
  15. 15.
    Lindorfer, M., Mecklenbräuker, C.F., Ostermayer, G.: Modeling the imperfect driver: incorporating human factors in a microscopic traffic model. IEEE Trans. Intell. Transp. Syst. 1–15 (2017). ISSN 1524-9050
  16. 16.
    Backfrieder, C., Ostermayer, G., Mecklenbräuker, C.F.: TraffSim – a traffic simulator for investigations of congestion minimization through dynamic vehicle rerouting. Int. J. Simul. Syst. Sci. Technol. 15, 38–47 (2014)Google Scholar
  17. 17.
    Shiffrin, R.M., Schneider, W.: Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychol. Rev. 84, 127–190 (1977)CrossRefGoogle Scholar
  18. 18.
    Green, M.: How long does it take to stop? Methodological analysis of driver perception-brake times. Transp. Hum. Factors 2, 195–216 (2000)CrossRefGoogle Scholar
  19. 19.
    May, D.: Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs (1990)Google Scholar
  20. 20.
    Ahmed, K.: Modeling drivers acceleration and lane changing behavior. Massachusetts Institute of Technology (1999)Google Scholar
  21. 21.
    Davis, L.: Modifications of the optimal velocity traffic model to include delay due to driver reaction time. Phys. A 319, 557–567 (2003)CrossRefGoogle Scholar
  22. 22.
    Regan, M.A.: New technologies in cars: human factors and safety issues. Ergon. Aust. 8, 6–15 (2004)Google Scholar
  23. 23.
    Stutts, J.: Distractions in everyday driving. Technical report, AAA Foundation for Traffic Safety (2003)Google Scholar
  24. 24.
    Dingus, T., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M., Hankey, J.: Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Natl. Acad. Sci. 113, 2636–2641 (2016)CrossRefGoogle Scholar
  25. 25.
    van Lint, H., Calvert, S., Schakel, W., Wang, M., Verbraeck, A.: Exploring the effects of perception errors and anticipation strategies on traffic accidents - a simulation study. In: Cassenti, D.N. (ed.) Advances in Human Factors in Simulation and Modeling, pp. 249–261. Springer, Cham (2018)CrossRefGoogle Scholar
  26. 26.
    Hamdar, S.: Driver behavior modeling. In: Handbook of Intelligent Vehicles, pp. 537–558. Springer, London (2012)Google Scholar
  27. 27.
    van Winsum, W.: The human element in car-following models. Transp. Res. Part F: Traffic Psychol. Behav. 2, 207–211 (1999)CrossRefGoogle Scholar
  28. 28.
    Tanida, K., Pöppel, E.: A hierarchical model of operational anticipation windows in driving an automobile. Cogn. Process. 7, 275–287 (2006)CrossRefGoogle Scholar
  29. 29.
    Lenz, H., Wagner, C., Sollacher, R.: Multi-anticipative car-following model. Eur. Phys. J. B – Condens. Matter Complex Syst. 7, 331–335 (1998)CrossRefGoogle Scholar
  30. 30.
    Eissfeldt, N., Wagner, P.: Effects of anticipatory driving in a traffic flow model. Eur. Phys. J. B – Condens. Matter Complex Syst. 33, 121–129 (2003)CrossRefGoogle Scholar
  31. 31.
    Treiber, M., Kesting, A.: Fuel consumption models. In: Traffic Flow Dynamics. Springer, Heidelberg (2013)Google Scholar
  32. 32.
    Backfrieder, C., Ostermayer, G., Mecklenbräuker, C.F.: Increased traffic flow through node-based bottleneck prediction and V2X communication. IEEE Trans. Intell. Transp. Syst. 18, 349–363 (2017)CrossRefGoogle Scholar
  33. 33.
    Backfrieder, C., Lindorfer, M., Mecklenbräuker, C.F., Ostermayer, G.: Impact of varying penetration rate of intelligent routing capabilities on vehicular traffic flow. In: 86th IEEE Vehicular Technology Conference (VTC-Fall) (2017, to be published)Google Scholar
  34. 34.
    Backfrieder, C., Ostermayer, G.: Modeling a continuous and accident-free intersection control for vehicular traffic in TraffSim. In: 8th European Modelling Symposium, pp. 333–337 (2014)Google Scholar
  35. 35.
    Lindorfer, M., Backfrieder, C., Mecklenbräuker, C.F., Ostermayer, G.: A stochastic driver distraction model for microscopic traffic simulations. In: 31st European Simulation and Modelling Conference (2017, to be published)Google Scholar
  36. 36.
    Liu, B.S., Lee, Y.H.: Effects of car-phone use and aggressive disposition during critical driving maneuvers. Transp. Res. Part F: Traffic Psychol. Behav. 8, 369–382 (2005)CrossRefGoogle Scholar
  37. 37.
    Cooper, J.M., Vladisavljevic, I., Medeiros-Ward, N., Martin, P.T., Strayer, D.L.: An investigation of driver distraction near the tipping point of traffic flow stability. Hum. Factors 51, 261–268 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Manuel Lindorfer
    • 1
    Email author
  • Christian Backfrieder
    • 1
  • Christoph F. Mecklenbräuker
    • 2
  • Gerald Ostermayer
    • 1
  1. 1.Research Group Networks and MobilityFH Upper AustriaHagenbergAustria
  2. 2.Institute of TelecommunicationsTU WienViennaAustria

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