A complex network approach to understand commercial vehicle movement
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We introduce complex network analysis and use a commercial vehicle’s observed trip as a proxy for a business relation between two facilities in its activity chain. We extract facility locations by applying density-based clustering to GPS data of commercial vehicle activities. The network among the facilities is then extracted by analysing the activity chains of more than 25,000 commercial vehicles. Centrality metrics prove useful and novel in identifying and locating key logistics players. Transport planners and decision makers can benefit from such an approach as it allows them to design more targeted initiatives and policy interventions.
KeywordsNetwork analysis Clustering Transport planning Freight
Thank you to the anonymous reviewers for their valuable feedback and review comments. The first author is grateful to the South African National Research Foundation (NRF) for funding the research under Grant FA-2007051100019; and also to the University of Pretoria’s Research Development Programme. All figures except Fig. 9 were created using R (R Core Team 2012).
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