Understanding Behavioral Patterns in Truck Co-driving Networks.

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 codriving 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 codriving behavior has important implications for society and the environment, as trucks coordinating their driving behavior together help reduce traffic congestion and optimize fuel usage.