Tool Based Assessment of Electromobility in Urban Logistics

  • Tim HoerstebrockEmail author
  • Axel Hahn
  • Jürgen Sauer
Part of the Studies in Computational Intelligence book series (SCI, volume 537)


Compared to conventional vehicles with combustion engines, electric vehicles have several advantages concerning sustainability and efficiency. Unfortunately, these advantages are bound to low ranges of the vehicles and long charging times due to the battery as energy source. In addition, the expensive battery increase the investment cost of the vehicle. In case of private users, these costs cannot be amortized by the relatively low electricity price due to the low utilizations of the vehicle. Car sharing could be a possible answer to deploy electric cars in urban regions nevertheless. The objective of our research is to assess the feasibility of exchanging conventional vehicles through electric powered ones within a car sharing fleet. The goals of this analysis are to determine possible exchange rates of the vehicles, to specify the required charging infrastructure and to evaluate the effect on the quality of service in terms of availability of the vehicles. In order to achieve these goals, we developed a multi-agent framework that simulates vehicles with new drive systems in existing transportations systems in general and the potential of electromobility in existing road networks in particular. In this chapter, we explain our approach and evaluate the feasibility of electric vehicles in a particular car sharing fleet operating in the city of Oldenburg, Germany. We evaluate two customer patterns: working day and weekend. The results show that the weekend scenario leads to several fuel shortages – in contrast to the working day scenario. The findings indicate that a more intelligent booking system or a quantitative expansion of charging stations would lead to a higher reliability and user acceptance.


Electric Vehicle Multiagent System Transportation System Route Choice Traffic Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adler, J.L., Blue, V.J.: A cooperative multi-agent transportation manage-ment and route guidance system. Transportation Research Part C: Emerging Technologies (2002), doi:10.1016/S0968-090X(02)00030-X.Google Scholar
  2. 2.
    AIMSUN, (accessed December 15, 2011)
  3. 3.
    Balmer, M., Axhausen, K., Nagel, K.: Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations. Transportation Research Record (2006), doi:10.3141/1985-14.Google Scholar
  4. 4.
    Barceló, J.: Parallelization of microscopic traffic simulation for ATT systems. In: Marcotte, P., Nguyen, S. (eds.) Equilibrium and advanced transportation modelling. Centre for Research on Transportation 25th Anniversary Series, 1971-1996, pp. 1–26. Kluwer Academic Publishers, Boston (1998)CrossRefGoogle Scholar
  5. 5.
    Charypar, D., Axhausen, K., Nagel, K.: An event-driven queue-based mi-crosimulation of traffic flow. ETH, Eidgenössische Technische Hochschule Zürich (2006)Google Scholar
  6. 6.
    Choy, M.C., Srinivasan, D., Cheu, R.L.: Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans (2003), doi:10.1109/TSMCA.2003.817394Google Scholar
  7. 7.
    Dia, H.: An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transportation Research Part C: Emerging Technologies (2002), doi:10.1016/S0968-090X(02)00025-6Google Scholar
  8. 8.
    France, J., Ghorbani, A.: A multiagent system for optimizing urban traffic. In: IEEE/WIC International Conference on Intelligent Agent Technology, IAT 2003, pp. 411–414 (2003)Google Scholar
  9. 9.
    Frost, Sullivan: Strategic Market and Technology Assessment of Telematics Applications for Electric Vehicles: Summary of Frost & Sullivan Study (2010)Google Scholar
  10. 10.
    Gringmuth, C., Liedtke, G., Geweke, S., Rothengatter, W.: Impacts of intelli-gent information systems on transport and the economy - the micro based modelling system OVID. In: Advances in Modeling, Optimization and Management of Transportation Processes and Systems: Theory and Practice - 10th Meeting of the EURO Working Group Transportation, EWGT (2000)Google Scholar
  11. 11.
    Hernándes, J.Z., Ossowski, S., García-Serrano, A.: Multiagent architectures for intelligent traffic management systems. Transportation Research Part C: Emerging Technologies (2002), doi:10.1016/S0968-090X(02)00032-3Google Scholar
  12. 12.
    Hunecke, M., Schubert, S., Zinn, F.: Mobilitätsbedürfnisse und Verkehrsmit-telwahl im Nahverkehr. Ein einstellungsbasierter Zielgruppenansatz. Internationales Verkehrswesen 57(1/2), 26–33 (2004)Google Scholar
  13. 13.
    Institute for Transport Studies - Karlsruhe Institute of Technology (KIT). Deutsches Mobilitätspanel, (accessed December 15, 2011)
  14. 14.
    Jochem, P.: A CO2 emission trading scheme for German road transport. Diss. Univ., Karlsruhe (2009)Google Scholar
  15. 15.
    van Katwijk, R., van Koningsbruggen, P.: Coordination of traffic manage-ment instruments using agent technology. Transportation Research Part C: Emerging Technologies (2002), doi:10.1016/S0968-090X(02)00034-7Google Scholar
  16. 16.
    Klügl, F., Bazzan, A.L.C.: Route Decision Behaviour in a Commuting Sce-nario: Simple Heuristics Adaptation and Effect of Traffic Forecast. Journal of Artificial Societies and Social Simulation 7, 1 (2004)Google Scholar
  17. 17.
    Kosonen, I.: Multi-agent fuzzy signal control based on real-time simulation. Transportation Research Part C: Emerging Technologies (2003), doi:10.1016/S0968-090X(03)00032-9.Google Scholar
  18. 18.
    Massachusetts Institute of Technology. MITSIMLab Intelligent Transportation Systems, (accessed December 15, 2011)
  19. 19.
    MATSim. Multi-Agent Transport Simulation, (accessed December 15, 2011)
  20. 20.
    Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. Journal de Physique I (1992), doi:10.1051/jp1:1992277Google Scholar
  21. 21.
    de Oliveira, D., Ferreira Jr., P.R., Bazzan, A.L.C., Klügl, F.: A Swarm-Based Approach for Selection of Signal Plans in Urban Scenarios. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 416–417. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Organisation for Economic Co-operation and Development. Impact of Transport Infrastructure Investment on Regional Development. OECD Publishing, Paris (2002)Google Scholar
  23. 23.
    Parunak, H.V.D.: Industrial and Practical Applications of DAI. In: Weiss, G. (ed.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 377–421. MIT Press, Cambridge (1999)Google Scholar
  24. 24.
    PTV AG. VISSIM - Multi-Modal Traffic Flow Modeling, (accessed December 15, 2011)
  25. 25.
    Rochner, F., Prothmann, H., Branke, J., Müller-Schloer, C., Schmeck, H.: An Organic Architecture for Traffic Light Controllers. In: Hochberger, C., Liskowsky, R. (eds.) Informatik 2006 ‐ Informatik für Menschen. Lecture Notes in Informatics (LNI), vol. 93, pp. 120–127. Köllen Verlag (2006)Google Scholar
  26. 26.
    Rossetti, R.J.F., Bordini, R.H., Bazzan, A.L.C., Bampi, S., Liu, R., van Vliet, D.: Using BDI agents to improve driver modelling in a commuter scenario. Transportation Research Part C: Emerging Technologies (2002), 10.1016/S0968-090X(02)00027-XGoogle Scholar
  27. 27.
    Steierwald, G.: Stadtverkehrsplanung: Grundlagen, Methoden, Ziele, 2nd edn. Springer, Berlin (2005)CrossRefGoogle Scholar
  28. 28.
    Vasirani, M.: Vehicle-centric coordination for urban road traffic management: A market-based multiagent approach. Diss. Universidad Rey Juan Carlos, Madrid (2009), Google Scholar
  29. 29.
    Vasirani, M., Ossowski, S.: An artificial market for efficient allocation of road transport networks. In: Klügl, F., Ossowski, S. (eds.) MATES 2011. LNCS, vol. 6973, pp. 189–196. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  30. 30.
    Waddell, P., Borning, A., Sevcíková, Socha, D.: Opus (the Open Platform for Urban Simulation) and UrbanSim 4. In: Proceedings of the 2006 International Conference on Digital Government Research, pp. 360–361. ACM, San Diego (2006)CrossRefGoogle Scholar
  31. 31.
    Wahle, J.: The impact of real-time information in a two-route scenario using agent-based simulation. Transportation Research Part C: Emerging Technologies (2002), doi:10.1016/S0968-090X(02)00031-1.Google Scholar
  32. 32.
    Wahle, J., Bazzan, A.L.C., Klügl, F., Schreckenberg, M.: Decision dynamics in a traffic scenario. Physica A: Statistical Mechanics and its Applications (2000), doi:10.1016/S0378-4371(00)00510-0.Google Scholar
  33. 33.
    Weiss, G.: Multiagent systems: A modern approach to distributed artificial intelligence. MIT Press, Cambridge (1999)Google Scholar
  34. 34.
    Yang, Q.: A Simulation Laboratory for Evaluation of Dynamic Traffic Manage-ment Systems. PhD thesis. Massachusetts Institute of Technology (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.OFFIS OldenburgOldenburgGermany
  2. 2.Carl von Ossietzky University of OldenburgOldenburgGermany

Personalised recommendations