Publications (new)
2020
Bruin, G. J.; Veenman, C. J.; Herik, H. J.; Takes, F. W.
Understanding Dynamics of Truck Co-Driving Networks Proceedings Article
In: Cherifi, H.; Gaito, S.; Mendes, J. F.; Moro, E.; Rocha, L. M. (Ed.): Complex Networks and Their Applications VIII, pp. 140–151, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-36683-4.
Abstract | Links | BibTeX | Tags: Co-driving behaviour, Link Prediction, Mobility, Spatio-temporal networks, Transport networks
@inproceedings{de_bruin_understanding_2020,
title = {Understanding Dynamics of Truck Co-Driving Networks},
author = {G. J. Bruin and C. J. Veenman and H. J. Herik and F. W. Takes},
editor = {H. Cherifi and S. Gaito and J. F. Mendes and E. Moro and L. M. Rocha},
doi = {10.1007/978-3-030-36683-4_12},
isbn = {978-3-030-36683-4},
year = {2020},
date = {2020-01-01},
booktitle = {Complex Networks and Their Applications VIII},
pages = {140–151},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {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 unimportant. Findings suggest that the dynamics of a truck co-driving network has clear social network effects.},
keywords = {Co-driving behaviour, Link Prediction, Mobility, Spatio-temporal networks, Transport networks},
pubstate = {published},
tppubtype = {inproceedings}
}
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 unimportant. Findings suggest that the dynamics of a truck co-driving network has clear social network effects.