Preliminary Results from an Agent-Based Model of the Daily Commute in Aberdeen and Aberdeenshire, UK

  • Jiaqi Ge
  • Gary Polhill
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 528)


Rapid economic and population growth have posed challenges to Aberdeen City and Shire in UK. Some social policies can potentially be helpful to alleviate traffic congestion and help people maintain a healthy work–life balance. In this initial model, we study the impact of flexi-time work arrangement and the construction of a new bypass on average daily commute time and CO2 emissions. We find that both flexi-time scheme and the new bypass will effectively reduce average daily commute time. Introducing a 30-min flexi-time range will reduce daily commute time by 6.5 min on average. However, further increasing flexi-time range will produce smaller saving in commute time. The new bypass will also reduce daily commute time, but only by one minute on average. As for environmental impact, introducing a 30-min flexi-time range will decrease CO2 emissions by 7 %. Not only that, it also flattens the peak emission at rush hour. The bypass, on the other hand, will increase CO2 emissions by roughly 2 %.


Daily commute Flexible work scheme Road infrastructure 


  1. 1.
    Aberdeen City Council.: Behind the Granite Aberdeen key facts 2014. (2014)
  2. 2.
    Lárraga, M., del Rıo, J., Mehta, A.: Two effective temperatures in traffic flow models: analogies with granular flow. Phys. A Stat. Mech. Appl. 307(3), 527–547 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Nagatani, T.: Self-organization and phase transition in traffic-flow model of a two-lane roadway. J. Phys. A Math. Gen. 26(17), L781 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Lárraga, M.E., Río, J.A.D., Alvarez-lcaza, L.: Cellular automata for one-lane traffic flow modeling. Transport. Res. C Emerg. Technol. 13(1), 63–74 (2005)CrossRefGoogle Scholar
  5. 5.
    Esser, J., Schreckenberg, M.: Microscopic simulation of urban traffic based on cellular automata. Int. J. Modern Phys. C 8(05), 1025–1036 (1997)CrossRefGoogle Scholar
  6. 6.
    Maerivoet, S., De Moor, B.: Cellular automata models of road traffic. Phys. Rep. 419(1), 1–64 (2005)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wardrop, J.G.: Road Paper. Some Theoretical Aspects of Road Traffic Research. ICE Proceedings: Engineering Divisions, Thomas Telford (1952)Google Scholar
  8. 8.
    Nakayama, S., Kitamura, R., Fujii, S.: Drivers’ route choice rules and network behavior: do drivers become rational and homogeneous through learning? Transport. Res. Record J. Transport. Res. Board 1752(1), 62–68 (2001)CrossRefGoogle Scholar
  9. 9.
    Ramming, M.S.: Network knowledge and route choice. (2001). Massachusetts Institute of TechnologyGoogle Scholar
  10. 10.
    Arslan, T., Khisty, J.: A rational approach to handling fuzzy perceptions in route choice. Eur. J. Oper. Res. 168(2), 571–583 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Mahmassani, H.S., Liu, Y.-H.: Dynamics of commuting decision behaviour under advanced traveller information systems. Transport. Res. C Emerg. Technol. 7(2), 91–107 (1999)CrossRefGoogle Scholar
  12. 12.
    Abdel-Aty, M.A., Abdalla, M.F.: Examination of multiple mode/route-choice paradigms under ATIS. Intell. Transport. Syst. IEEE Trans. 7(3), 332–348 (2006)CrossRefGoogle Scholar
  13. 13.
    Adler, J.L.: Investigating the learning effects of route guidance and traffic advisories on route choice behavior. Transport. Res. C Emerg. Technol. 9(1), 1–14 (2001)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bogers, E.A., Viti, F., Hoogendoorn, S.P.: Joint modeling of advanced travel information service, habit, and learning impacts on route choice by laboratory simulator experiments. Transport. Res. Record J. Transport. Res. Board 1926(1), 189–197 (2005)CrossRefGoogle Scholar
  15. 15.
    Selten, R., Chmura, T., Pitz, T., Kube, S., Schreckenberg, M.: Commuters route choice behaviour. Games Econ. Behav. 58(2), 394–406 (2007)CrossRefzbMATHGoogle Scholar
  16. 16.
    Zhang, L., Levinson, D.: Determinants of route choice and value of traveler information: a field experiment. Transport. Res. Record J. Transport. Res. Board 2086(1), 81–92 (2008)Google Scholar
  17. 17.
    Dia, H.: An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transport. Res. C Emerg. Technol. 10(5–6), 331–349 (2002)CrossRefGoogle Scholar
  18. 18.
    Adler, J.L., Satapathy, G., Manikonda, V., Bowles, B., Blue, V.J.: A multi-agent approach to cooperative traffic management and route guidance. Transport. Res. B Methodological 39(4), 297–318 (2005)CrossRefGoogle Scholar
  19. 19.
    Bazzan, A.L.C., Wahle, J., Klügl, F.: Agents in traffic modelling—from reactive to social behaviour. KI-99: Advances in Artificial Intelligence, pp. 303–306. Springer (1999)Google Scholar
  20. 20.
    Klügl, F., Bazzan, A.L.: Route decision behaviour in a commuting scenario: simple heuristics adaptation and effect of traffic forecast. J. Artif. Soc. Soc. Simulat. 7(1) (2004)Google Scholar
  21. 21.
    Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S.K., Huse, G.: A standard protocol for describing individual-based and agent-based models. Ecological modelling 198, 115–126 (2006)Google Scholar
  22. 22.
    Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F.: The ODD protocol: a review and first update. Ecological modelling 221, 2760–2768 (2010)Google Scholar
  23. 23.
    Skiena, S.: Dijkstra’s algorithm. Implementing Discrete Mathematics: Combinatorics and Graph Theory with Mathematica, pp. 225–227 Addison-Wesley, Reading, MA (1990)Google Scholar
  24. 24.
    Cappiello, A., Chabini, I., Nam, E.K., Lue, A., Abou Zeid, M.: A statistical model of vehicle emissions and fuel consumption. Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on, IEEE (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The James Hutton InstituteAberdeenUK

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