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Generating Realistic Road Usage Information and Origin-Destination Data for Traffic Simulations: Augmenting Agent-Based Models with Network Techniques

  • Christian Hofer
  • Georg Jäger
  • Manfred Füllsack
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

We present a novel network approach, supported by an agent-based simulation using empirical survey results, in order to generate origin-destination data and information about the road usage of a large, urban traffic system. Additionally, we investigate congestion and its effects on road usage due to traffic jam avoidance strategies. The investigated city serves as a case study and the presented method can be easily adapted for arbitrary traffic networks. We find that the use of network techniques offers various advantages and can replace aspects that are traditionally performed by computationally more expensive methods. Our method shifts the computational efforts from individual agent interactions to more elegant network techniques, which leads to much lower computation time and better scaling properties. Results are evaluated and show high conformance with measured data, especially if congestion effects are included. Furthermore, the obtained data can be used as an input for car-following models or other types of traffic simulation to gain even more information about the investigated traffic network.

References

  1. 1.
    Wang, H., Fu, L.: Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 1—Modeling fuel consumption and emissions based on speed and vehicle-specific power. J. Air Waste Manag. Assoc. 60(12), 1463–1470 (2010)Google Scholar
  2. 2.
    Caponio, G.: Commuting carbon dioxide (CO2) emissions: A study of ten Italian metropolitan cities. G. Caponio*, G. Mascolo, G. Mummolo, G. Mossa, S. Digiesi (2015)Google Scholar
  3. 3.
    Berkowicz, R., Winther, M., Ketzel, M.: Traffic pollution modelling and emission data. Environ. Model. Softw. 21(4), 454–460 (2006)Google Scholar
  4. 4.
    Steele, C.: A critical review of some traffic noise prediction models. Appl. Acoust. 62(3), 271–287 (2001)Google 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(2), 1035 (1995)Google Scholar
  6. 6.
    Grote, M., Williams, I., Preston, J., Kemp, S.: Including congestion effects in urban road traffic CO 2 emissions modelling: Do Local Government Authorities have the right options?. Transp. Res. Part D: Transp. Environ. 43, 95–106 (2016)Google Scholar
  7. 7.
    Helbing, D., Armbruster, D., Mikhailov, A.S., Lefeber, E.: Information and material flows in complex networks. Phys. A: Stat. Mech. Appl. 363(1), xi–xvi (2006)Google Scholar
  8. 8.
    Barthélemy, M.: Spatial networks. Phys. Rep. 499(1), 1–101 (2011)Google Scholar
  9. 9.
    Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I 2(12), 2221–2229 (1992)Google Scholar
  10. 10.
    Bazzan, A.L., Klügl, F.: A review on agent-based technology for traffic and transportation. Knowl. Eng. Rev. 29(3), 375–403 (2014)Google Scholar
  11. 11.
    Chen, B., Cheng, H.H.: A review of the applications of agent technology in traffic and transportation systems. IEEE Trans. Intell. Trans. Syst. 11(2), 485–497 (2010)Google Scholar
  12. 12.
    Vissim, P.: 5.10 User Manual, PTV Planung Transport Verkehr AG, Stumpfstraße, vol. 1 (2008)Google Scholar
  13. 13.
    Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K.: MATSim-T: Architecture and simulation times. In: Proceeding of the Multi-agent Systems for Traffic and Transportation Engineering, IGI Global, pp. 57–78 (2009)Google Scholar
  14. 14.
    Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P.: SUMO (Simulation of Urban MObility)-an open-source traffic simulation. In: Proceedings of the 4th Middle East Symposium on Simulation and Modelling (MESM20002), pp. 183–187 (2002)Google Scholar
  15. 15.
    Nagurney, A., Dong, J.: A multiclass, multicriteria traffic network equilibrium model with elastic demand. Transp. Res. Part B Methodol. 36(5), 445–469 (2002)Google Scholar
  16. 16.
    Cascetta, E.: Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator. Transp. Res. Part B Methodol. 18(4), 289–299 (1984)Google Scholar
  17. 17.
    Caceres, N., Wideberg, J., Benitez, F.: Deriving origin–destination data from a mobile phone network. IET Intell. Transp. Syst. 1(1), 15–26 (2007)Google Scholar
  18. 18.
    Tomschy, R. et al.: Oesterreich unterwegs 2013/2014: Ergebnisbericht zur oesterreichweiten Mobilitaetserhebung. Oesterreich unterwegs 2013/2014 (2016)Google Scholar
  19. 19.
    Boeing, G.: OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environ. Urban Syst. 65, 126–139 (2017)Google Scholar
  20. 20.
    Map, O.S.: Open street map 18, Retrieved June (2017)Google Scholar
  21. 21.
    Statistik Austria: Registerzählung 2011. Statistik Austria Pendlerstatistik (2011)Google Scholar
  22. 22.
    FGSV: Richtlinien für die Anlage von Straßen (RAS) Teil: Querschnitte (RAS-Q). FGSV-Verlag, Köln (2006)Google Scholar
  23. 23.
    Höfler, F.: Verkehrswesen-Praxis-Band 1: Verkehrsplanung (2004)Google Scholar
  24. 24.
    De Palma, A., Rochat, D.: Understanding individual travel decisions: results from a commuters survey in Geneva. Transportation 26(3), 263–281 (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Christian Hofer
    • 1
  • Georg Jäger
    • 1
  • Manfred Füllsack
    • 1
  1. 1.Institute of Systems Sciences, Innovation and Sustainability ResearchUniversity of GrazGrazAustria

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