Understanding behavioral patterns in truck co-driving networks

Understanding behavioral patterns in truck co-driving networks

de Bruin, G.J.
Veenman, C.J.
van den Herik, H.J.
Takes, F.W.

Aiello, L.M. (editor)
Cherifi, H. (editor)
Lio, P. (editor)
Rocha, L.M. (editor)
Cherifi, C. (editor)
Lambiotte, R. (editor)

2019

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. © Springer Nature Switzerland AG 2019.

Assortativity
Co-driving networks
Community detection
Infrastructure networks
Network analysis

http://resolver.tudelft.nl/uuid:0fc3f8f0-8c33-4515-b5c6-4d48dcb8ea2c

https://doi.org/10.1007/978-3-030-05414-4_18

844226

Springer Verlag

9783030054137

1860-949X

Studies in Computational Intelligence, 813, 223-235

conference paper