Abstract
This paper examines the co-driving behavior of truck drivers using network analysis. From a unique spatiotemporal dataset encompassing more than 10 million measurements of trucks passing 17 different highway locations in the Netherlands, we extract a so-called co-driving network. In this network, nodes are truck drivers and edges represent pairs of trucks that are systematically driving together. The obtained co-driving network structure has various properties common to real-world networks, such as a dominant giant component and a power law degree distribution. Moreover, network distance metrics and community detection reveal that the network has a highly modular structure. We furthermore propose a method for understanding the network community structure through attribute assortativity. Results indicate that co-driving links are mostly established based on geographical aspects: truck drivers from the same country or the same region in the Netherlands are more inclined to drive together. The resulting improved understanding of co-driving behavior has important implications for society and the environment, as trucks coordinating their driving behavior together help reduce traffic congestion and optimize fuel usage.
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References
Barabási, A.L.: Network Science. Cambridge University Press, Cambridge (2016)
Barrat, A., Cattuto, C.: Temporal networks of face-to-face human interactions. In: Temporal Networks, pp. 191–216. Springer (2013)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), 1–12 (2008)
da Cunha, B.R., Gonçalves, S.: Topology, robustness, and structural controllability of the brazilian federal police criminal intelligence network. Appl. Netw. Sci. 3(1), 36 (2018)
Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)
Good, B.H., de Montjoye, Y.A., Clauset, A.: Performance of modularity maximization in practical contexts. Phys. Rev. E 81(4), 046,106 (2010)
Kassarnig, V., Mones, E., Bjerre-Nielsen, A., Sapiezynski, P., Dreyer Lassen, D., Lehmann, S.: Academic performance and behavioral patterns. EPJ Data Sci. 7, 1–16 (2018)
Kleinberg, J.: The small-world phenomenon: an algorithmic perspective. In: Proceedings of 31st Annual ACM Symposium on Theory of Computing, pp. 163–170. ACM (2000)
Lambiotte, R., Delvenne, J.C., Barahona, M.: Laplacian dynamics and multiscale modular structure in networks. IEEE Trans. Netw. Sci. Eng. 1(2), 76–90 (2014)
Newman, M.E.J.: Assortative mixing in networks. Phys. Rev. Lett. 89(20), 208,701 (2002)
Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67(2), 026,126 (2003)
Newman, M.E.J.: Networks: An Introduction. OUP Oxford, Oxford (2009)
Tsugawa, S., Kato, S.: Energy its: another application of vehicular communications. IEEE Commun. Mag. 48(11) (2010)
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Bruin, G.J.d., Veenman, C.J., van den Herik, H.J., Takes, F.W. (2019). Understanding Behavioral Patterns in Truck Co-driving Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_18
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DOI: https://doi.org/10.1007/978-3-030-05414-4_18
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