A complex network approach to understand commercial vehicle movement

Abstract

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 proves 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.Keywords: Network analysis, Clustering, Transport planning, Freight IntroductionIn this paper we link two bodies of knowledge that both focus on the mobility of vehicles, people, goods and services. On the one side there is a supply chain management body of knowledge concerned with the management of a network of interconnected businesses providing products and services to one another and to end customers. We may not see the abstract supply chains in our daily lives, yet its manifestation is multitude: we experience it through services rendered; products being available at our local food courts; and the seemingly obstructive heavy vehicles during our daily commute. On the other side, the body of transport planning deals with the design, operation and evaluation of transport infrastructure. While supply chain researchers and practitioners are dealing with the challenge of "how can we as a firm better compete?", the transport planners are trying to answer an aggregate question: "how can we provide better supporting infrastructure so firms and individuals can participate in the economy?" amidst the uncertainty caused by the various competing objectives of the firms and other road users. Our objective in this paper is to link these two domains using complex networks and network analysis.To account for commercial vehicles in transport planning models, passenger and private vehicle models are often just inflated by some fraction to reflect commercial tra c as background noise. In a recent special issue on the behavioural insights into the modelling of freight transportation, Hensher Although commercial vehicles account for a small proportion of all the road users, each vehicle contributes disproportionately to tra c congestion and emissions. Commercial vehicle movement, however, can be considered as the manifestation of complex inter-dependent relationships between enterprises: the delivery of goods across geographically dispersed locations and the provision of services is the result of supply meeting demand for commodities and services. Borgatti and Li [7] make a strong case to analyse and express the complex supply chain structures of firms as social networks. Following such a path through literature often highlights knowledge exchange as the focus of social networks amongst firms. Establishing clear networks of knowledge exchange is arguably leading to innovation systems, ...

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