Skip to main content
SpringerLink
Log in

Selfish Road Users – Case Studies on Rule Breaking Agents for Traffic Simulation

  • Conference paper
Multiagent System Technologies (MATES 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7598))

Included in the following conference series:

Abstract

Many studies in the context of traffic simulation investigate effects of new regulation strategies on travel times. However, in most cases it is assumed, that these regulations are followed by all actors. This work investigates the effects of non-compliance of simulated road user agents. In particular it is analyzed to what extent rule breaking agents have advantages on the cost of the remaining road users. For evaluation we take into account three different scenarios: Overtaking prohibition for trucks on motorways, bicycles pushing to the front at traffic lights and pedestrians crossing roads in aggressive manner. In all scenarios, the fraction of rule breaking agents is varied. Simulation results show that rule breaking does not always lead to disadvantages for the remaining road users.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 72.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ashton, W.D.: Gap-acceptance problems at a traffic intersection. Journal of the Royal Statistical Society. Series C (Applied Statistics) 20(2), 130–138 (1971), http://www.jstor.org/stable/2346461

    MathSciNet  Google Scholar 

  2. Baek, S.K., Minnhagen, P., Bernhardsson, S., Choi, K., Kim, B.J.: Flow improvement caused by agents who ignore traffic rules. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 80(1), 016111+ (2009), http://dx.doi.org/10.1103/PhysRevE.80.016111

    Article  Google Scholar 

  3. Blue, V.J., Adler, J.L.: Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Research Part B: Methodological 35(3), 293–312 (2001), http://www.sciencedirect.com/science/article/B6V99-41YG51Y-5/2/22693b4ec6fd5d3898831f17d2000bc6

    Article  Google Scholar 

  4. Boella, G., Lesmo, L.: A game theoretic approach to norms and agents. Cognitive Science Quaterly 2(3/4), 492–512 (2002)

    Google Scholar 

  5. Clarridge, A., Salomaa, K.: A Cellular Automaton Model for Car Traffic with a Slow-to-Stop Rule. In: Maneth, S. (ed.) CIAA 2009. LNCS, vol. 5642, pp. 44–53. Springer, Heidelberg (2009), http://dx.doi.org/10.1007/978-3-642-02979-0_8

    Chapter  Google Scholar 

  6. Dallmeyer, J., Lattner, A.D., Timm, I.J.: From GIS to Mixed Traffic Simulation in Urban Scenarios. In: Liu, J., Quaglia, F., Eidenbenz, S., Gilmore, S. (eds.) 4th International ICST Conference on Simulation Tools and Techniques, SIMUTools 2011, Barcelona, Spain, March 22-24, pp. 134–143. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brüssel (2011) ISBN 978-1-936968-00-8

    Google Scholar 

  7. Dallmeyer, J., Lattner, A.D., Timm, I.J.: GIS-based Traffic Simulation using OSM. In: Cervone, G., Lin, J., Waters, N. (eds.) Data Mining for Geoinformatics: Methods and Applications. Springer (accepted, 2012)

    Google Scholar 

  8. Dallmeyer, J., Lattner, A.D., Timm, I.J.: Pedestrian Simulation for Urban Traffic Scenarios. In: Bruzzone, A.G. (ed.) Proceedings of the Summer Computer Simulation Conference 2012. 44rd Summer Simulation Multi-Conference (SummerSim 2012), pp. 414–421. Curran Associates, Inc. (2012)

    Google Scholar 

  9. Dallmeyer, J., Schumann, R., Lattner, A.D., Timm, I.J.: Don’t Go with the Ant Flow: Ant-inspired Traffic Routing in Urban Environments. In: Seventh International Workshop on Agents in Traffic and Transportation (ATT 2012), Valencia, Spain (2012)

    Google Scholar 

  10. Dignum, F.: Autonomous agents with norms. Artificial Intelligence and Law 7, 69–79 (1999)

    Article  Google Scholar 

  11. Ehlert, P.A.M., Rothkrantz, L.J.M.: Microscopic traffic simulation with reactive driving agents. ITSC 2001 2001 IEEE Intelligent Transportation Systems Proceedings Cat No01TH8585 40(5), 860–865 (2001), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=948773

    Article  Google Scholar 

  12. Emmerich, H., Rank, E.: An improved cellular automaton model for traffic flow simulation. Physica A: Statistical and Theoretical Physics 234(3-4), 676–686 (1997), http://www.sciencedirect.com/science/article/B6TVG-3VSFH3D-1M/2/68da6d108491eafd93924e74226e428a

    Article  Google Scholar 

  13. Greenshield, B.D.: A study of traffic capacity. Proceedings of the 14th Annual Meeting Highway Research Board 14 468, 448–477 (1935)

    Google Scholar 

  14. Hafstein, S.F., Pottmeier, A., Wahle, J., Schreckenberg, M.: Cellular Automaton Modeling of the Autobahn Traffic in North Rhine-Westphalia. In: Troch, I., Breitenecker, F. (eds.) Proc. of the 4th MATHMOD, pp. 1322–1331 (2003)

    Google Scholar 

  15. Hennessy, D.A., Wiesenthal, D.L.: Gender, driver aggression, and driver violence: An applied evaluation. Sex Roles 44, 661–676 (2001), http://dx.doi.org/10.1023/A:1012246213617 , doi:10.1023/A:1012246213617

    Article  Google Scholar 

  16. Jason, L.A., Liotta, R.: Pedestrian jaywalking under facilitating and nonfacilitating conditions. Journal of Applied Behavior Analysis 15(15), 469–473 (1982), http://www.pubmedcentral.gov/articlerender.fcgi?artid=1308291

    Article  Google Scholar 

  17. Johnson, M., Newstead, S., Charlton, J., Oxley, J.: Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists. Accident Analysis & Prevention 43(1), 323–328 (2011), http://dx.doi.org/10.1016/j.aap.2010.08.030

    Article  Google Scholar 

  18. Johnson, M., Oxley, J., Cameron, M.: Cyclist bunch riding: A review of the literature. Tech. rep., Monash University Accident Research Centre (January 2009) iSBN: 0-7326-2355-3

    Google Scholar 

  19. Kerner, B.S., Klenov, S.L., Wolf, D.E.: Cellular automata approach to three-phase traffic theory. Journal of Physics A: Mathematical and General 35(47), 9971 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Kesting, A., Treiber, M., Helbing, D.: Agents for traffic simulation. Arxiv preprint arXiv08050300 physics.so, 325–356 (2008), http://arxiv.org/abs/0805.0300

  21. King, M.J., Soole, D.W., Ghafourian, A.: Relative risk of illegal pedestrian behaviours. In: Australasian Road Safety Research, Policing and Education Conference, pp. 768–778 (2008), http://eprints.qut.edu.au/17630/

  22. Knospe, W., Santen, L., Schadschneider, A., Schreckenberg, M.: A realistic two-lane traffic model for highway traffic. Journal of Physics A: Mathematical and General 35(15), 3369–3388 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  23. Krauss, S., Wagner, P., Gawron, C.: Continuous limit of the Nagel-Schreckenberg model. Phys. Rev. E 54(4), 3707–3712 (1996)

    Article  Google Scholar 

  24. Laagland, J.: How to model aggressive behavior in traffic simulation. In: Twente Student Conference on IT, Enschede 3 (2005)

    Google Scholar 

  25. Lattner, A.D., Dallmeyer, J., Paraskevopoulos, D., Timm, I.J.: Approximation of Pedestrian Effects in Urban Traffic Simulation by Distribution Fitting. In: Troitzsch, K.G., Möhring, M., Lotzmann, U. (eds.) Proceedings of the 26th EUROPEAN Conference on Modelling and Simulation, pp. 574–580 (2012) ISBN 978-0-9564944-4-3

    Google Scholar 

  26. Lattner, A.D., Dallmeyer, J., Timm, I.J.: Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments. In: Klügl, F., Ossowski, S. (eds.) MATES 2011. LNCS, vol. 6973, pp. 77–88. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  27. Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. Journal de Physique I 2(12), 2221–2229 (1992)

    Article  Google Scholar 

  28. Ottomanelli, M., Caggiani, L., Giuseppe, I., Sassanelli, D.: An Adaptive Neuro-Fuzzy Inference System for simulation of pedestrians behaviour at unsignalized roadway crossings. In: 14th Online World Conference on Soft Computing in Industrial Application 14 (2009)

    Google Scholar 

  29. Sharkin, B.S.: Road rage: Risk factors, assessment, and intervention strategies. Journal of Counseling and Development 82, 191–199 (2004)

    Article  Google Scholar 

  30. Speirs, E., Braidwood, R.: Quadstone paramics v5.0 technical notes. Tech. rep., Quadstone Paramics Ltd. (2004)

    Google Scholar 

  31. Tasca, L.: A review of the literature on aggressive driving research. In: Aggressive Driving Issues Conference (2000)

    Google Scholar 

  32. Virkler, M.R.: Prediction and measurement of travel time along pedestrian routes. Transportation Research Record No. 1636, Bicycle and Pedestrian Research 1636, 37–42 (1998) iSSN: 0361-1981, http://dx.doi.org/10.3141/1636-06

    Article  Google Scholar 

  33. Wiesenthal, D.L., Hennessy, D., Gibson, P.M.: The driving vengeance questionnaire (dvq): The development of a scale to measure deviant drivers’ attitudes. Violence and Victims 15(2), 115–136 (2000)

    Google Scholar 

  34. Yuhara, N., Tajima, J.: Multi-driver agent-based traffic simulation systems for evaluating the effects of advanced driver assistance systems on road traffic accidents. Cognition, Technology & Work 8, 283–300 (2006), http://dx.doi.org/10.1007/s10111-006-0045-9 , doi:10.1007/s10111-006-0045-9

    Article  Google Scholar 

  35. Zhu, H.B., Lei, L., Dai, S.Q.: Two-lane traffic simulations with a blockage induced by an accident car. Physica A: Statistical Mechanics and its Applications 388(14), 2903–2910 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Systems and Simulation, Institute of Computer Science, Goethe University Frankfurt, P.O. Box 11 19 32, 60054, Frankfurt, Germany

    Jörg Dallmeyer & Andreas D. Lattner

  2. Business Informatics I, University of Trier, D-54286, Trier, Germany

    Ingo J. Timm

Authors
  1. Jörg Dallmeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas D. Lattner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ingo J. Timm
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fachbereich IV - Wirtschaftsinformatik I, Universität Trier, Universitätsring 15, 54286, Trier, Germany

    Ingo J. Timm

  2. Level 5, IBM R&D Laboratories, 204 Laygon Street, 3051, Carlton, VIC, Australia

    Christian Guttmann

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dallmeyer, J., Lattner, A.D., Timm, I.J. (2012). Selfish Road Users – Case Studies on Rule Breaking Agents for Traffic Simulation. In: Timm, I.J., Guttmann, C. (eds) Multiagent System Technologies. MATES 2012. Lecture Notes in Computer Science(), vol 7598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33690-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33690-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33689-8

  • Online ISBN: 978-3-642-33690-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation