Understanding Dynamics of Truck Co-Driving Networks

Bruin, G.J. de; Veenman, C.J.; Herik, H.J. van den; Takes, F.W.; Cherifi, H.; Gaito, S.; Fernendo, Mendes J.; Moro, E.; Mateus, Rocha L.

doi:doi:10.1007/978-3-030-36683-4_12

Persistent URL of this record https://hdl.handle.net/1887/85502

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Leiden Institute of Advanced Computer Science (LIACS)

Bruin, G.J. de; Veenman, C.J.; Herik, H.J. van den; Takes, F.W. (2019)

Understanding Dynamics of Truck Co-Driving Networks

Article in monograph or in proceedings

The goal of this paper is to learn the dynamics of truck co-driving behaviour. Understanding this behaviour is important because co-driving has a potential positive impact on the environment. In the so-called co-driving network, trucks are nodes while links indicate that two trucks frequently drive together. To understand the network’s dynamics, we use a link prediction approach employing a machine learning classifier. The features of the classifier can be categorized into spatio-temporal features, neighbourhood features, path features, and node features. The very different types of features allow us to understand the social processes underlying the co-driving behaviour. Our work is based on a spatio-temporal data not studied before. Data is collected from 18 million truck movements in the Netherlands. We find that co-driving behaviour is best described by using neighbourhood features, and to lesser extent by path and spatio-temporal features. Node features are deemed...Show moreThe goal of this paper is to learn the dynamics of truck co-driving behaviour. Understanding this behaviour is important because co-driving has a potential positive impact on the environment. In the so-called co-driving network, trucks are nodes while links indicate that two trucks frequently drive together. To understand the network’s dynamics, we use a link prediction approach employing a machine learning classifier. The features of the classifier can be categorized into spatio-temporal features, neighbourhood features, path features, and node features. The very different types of features allow us to understand the social processes underlying the co-driving behaviour. Our work is based on a spatio-temporal data not studied before. Data is collected from 18 million truck movements in the Netherlands. We find that co-driving behaviour is best described by using neighbourhood features, and to lesser extent by path and spatio-temporal features. Node features are deemed unimportant. Findings suggest that the dynamics of a truck co-driving network has clear social network effects.Show less

Spatio-Temporal Networks

Link Prediction