, Volume 40, Issue 3, pp 729–750 | Cite as

A complex network approach to understand commercial vehicle movement

  • Johan W. JoubertEmail author
  • Kay W. Axhausen


We introduce complex network analysis and use a commercial vehicle’s observed trip as a proxy for a business relation between two facilities in its activity chain. We extract facility locations by applying density-based clustering to GPS data of commercial vehicle activities. The network among the facilities is then extracted by analysing the activity chains of more than 25,000 commercial vehicles. Centrality metrics prove useful and novel in identifying and locating key logistics players. Transport planners and decision makers can benefit from such an approach as it allows them to design more targeted initiatives and policy interventions.


Network analysis Clustering Transport planning Freight 



Thank you to the anonymous reviewers for their valuable feedback and review comments. The first author is grateful to the South African National Research Foundation (NRF) for funding the research under Grant FA-2007051100019; and also to the University of Pretoria’s Research Development Programme. All figures except Fig. 9 were created using R (R Core Team 2012).


  1. Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)CrossRefGoogle Scholar
  2. Andrienko, G., Andrienko, N., Bak, P., Keim, D., Kisilevich, S., Wrobel, S.: A conceptual framework and taxonomy of techniques for analyzing movement. J. Visu. Lang. Comput. 22(3), 213–232 (2011)CrossRefGoogle Scholar
  3. Autry, C.W., Griffis, S.E.: Supply chain capital: The impact of structural and relational linkages on firm execution and innovation. J. Bus. Logist. 29(1), 157–173 (2008)CrossRefGoogle Scholar
  4. Banister, D., Berechman, Y.: Transport investment and the promotion of economic growth. J. Transp. Geogr. 9(3), 209–218 (2001)CrossRefGoogle Scholar
  5. Barthélemy, M.: Spatial networks. Phys. Rep. 499(1–3), 1–101 (2011)CrossRefGoogle Scholar
  6. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Phys. Rep. 424(4–5), 175–308 (2006)CrossRefGoogle Scholar
  7. Borgatti, S.R., Li, X.: On social network analysis in a supply chain context. J. Supply Manag. 45(2), 5–22 (2009)Google Scholar
  8. Fourie, P.: Agent-Based transport simulation versus equilibrium assignment for private vehicle traffic in Gauteng. In: 29th Annual Southern African Transport Conference, 12–23 July (2010)Google Scholar
  9. Gao, W., Balmer, M., Miller, E.J.: Comparison of MATSim and EMME/2 on greater Toronto and Hamilton area network, Canada. Transp. Res. Rec. 2197, 118–128 (2009)CrossRefGoogle Scholar
  10. Giuliani, E., Bell, M.: The micro-determinants of meso-level learning and innovation: evidence from a chilean wine cluster. Res. Policy 34(1), 47–68 (2005)CrossRefGoogle Scholar
  11. Giuliani, E., Pietrobelli, C., Rabellotti, R.: Upgrading in global value chains: Lessons from latin american clusters. World Dev. 33(4), 549–573 (2005)CrossRefGoogle Scholar
  12. Graf, H., Krüger, J.J.: The performance of gatekeepers in innovator networks. Jena Economic Research Papers in Economics 2009-058. Max-Planck-Institute of Economics, Jena (2009)Google Scholar
  13. Hackney, J.K., Marchal, F.: A model for coupling multi-agent social interactions and traffic simulation. In: 88th Annual Meeting of the Transport Research Board (2009)Google Scholar
  14. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107–145 (2001)CrossRefGoogle Scholar
  15. Hensher, D.A.: Models of organizational and agency choices for passenger- and freight-related travel: notions of interactivity and influence. In: Axhausen, K.W. (ed.) Moving Through Nets: The Physical and Social Dimensions of Travel, pp. 107–130. Elsevier, Amsterdam (2007)Google Scholar
  16. Hensher, D., Figliozzi, M.A.: Behavioural insights into the modelling of freight transportation and distribution systems. Transp. Res. B 41(9), 921–923 (2007)CrossRefGoogle Scholar
  17. Hesse, M., Rodrigue, J.: The transport geography of logistics and freight distribution. J. Transp. Geogr. 12(3), 171–184 (2004)CrossRefGoogle Scholar
  18. Holme, P., Kim, B.J., Yoon, C.N., Han, S.K.: Attack vulnerability of complex networks. Phys. Rev. 65(056109):1–14 (2002)Google Scholar
  19. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  20. Joubert, J.W., Axhausen, K.W.: Inferring commercial vehicle activities in Gauteng, South Africa. J. Transp. Geogr. 19(1), 115–124 (2011)CrossRefGoogle Scholar
  21. Joubert, J.W., Fourie, P.J., Axhausen, K.W.: Large-scale agent-based combined traffic simulation of private cars and commercial vehicles. Transp. Res. Rec. 2168, 24–32 (2010)CrossRefGoogle Scholar
  22. Kowald, M., Frei, A., Hackney, J.K., Illenberger, J., Axhausen, K.W.: Collecting data on leisure travel: The link between leisure acquaintances and social interactions. In: Applications of Social Network Analysis. Manchester, UK (2009)Google Scholar
  23. Lazzarini, S., Chaddad, F., Cook, M.: Integrating supply chain and network analyses: The study of netchains. J. Chain Netw. Sci. 1(1), 7–22 (2001)CrossRefGoogle Scholar
  24. Liedtke, G.: Principles of micro-behavior commodity transport modeling. Transp. Res. E 45(5), 795–809 (2009)CrossRefGoogle Scholar
  25. Lima-Mendez, G., van Helden, J.: The powerful law of the power law and other myths in network biology. Mol. BioSyst. 5(12), 1482–1493 (2009)CrossRefGoogle Scholar
  26. Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)CrossRefGoogle Scholar
  27. Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)CrossRefGoogle Scholar
  28. Newman, M.E.J.: Power laws, pareto distributions and zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)CrossRefGoogle Scholar
  29. Pelekis, N., Kopanakis, I., Kotsifakos, E.E., Frentzos, E., Theodoridis, Y.: Clustering uncertain trajectories. Knowl. Inf. Syst. 28(1), 117–147 (2011)CrossRefGoogle Scholar
  30. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012),, ISBN 3-900051-07-0
  31. Roorda, M.J., Cavalcante, R., McCabe, S., Kwan, H.: A conceptual framework for agent-based modelling of logistics services. Transp. Res. E 46(1), 18–31 (2010)CrossRefGoogle Scholar
  32. Schröder, S., Zilske, M., Liedtke, G.T., Nagel, K.: Computational framework for multiagent simulation of freight transport activities. In: 91st Annual Meeting of the Transportation Research Board, Paper 12-4152 (2012)Google Scholar
  33. Spaccapietra, S., Parentb, C., Damiania, M.L., de Macedoa, J.A., Portoa, F., Vangenota, C.: A conceptual view on trajectories. Data Knowl. Eng. 65(1), 126–146 (2008)CrossRefGoogle Scholar
  34. Strogatz, S.H.: Exploring complex networks. Nature 410, 268–276 (2001)CrossRefGoogle Scholar
  35. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, Amsterdam (2006)Google Scholar
  36. Tiakas, E., Papadopoulos, A., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., Djordjevic-Kajan, S.: Searching for similar trajectories in spatial networks. J. Syst. Softw. 82(5), 772–788 (2009)CrossRefGoogle Scholar
  37. Zhou, C., Frankowski, D., Ludford, P., Shekhar, S., Terveen, L.: Discovering personal gazetteers: an interactive clustering approach. In: Proceedings of the 12th annual ACM international workshop on Geographic Information Systems, pp 266–273. ACM, Washington DC(2004)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  1. 1.Centre of Transport Development, Industrial and Systems EngineeringUniversity of PretoriaHatfieldSouth Africa
  2. 2.CSIR Built EnvironmentPretoriaSouth Africa
  3. 3.Institute for Transport Planning and Systems (IVT), ETH ZurichZurichSwitzerland

Personalised recommendations