Abstract
This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the temporal and regional variations of the travel demand by train in Belgium. Travel requests are collected over a two-month period and consist of origin-destination flows between stations operated by the Belgian national railway company in 2016. The Louvain method is applied to detect communities of tightly-connected stations. Results show the influence of both the urban and network structures on the spatial organization of the clusters. We also further discuss the implications of the observed temporal and regional variations of these clusters for transportation travel demand and planning.
Highlights
Travel flows are commonly represented by graphs, wherein nodes are the origins, and destinations and edges are weighted by the intensity of the flows
We study: (1) the general dataset; (2) a subset excluding stations inside the Brussels Capital Region; (3) all requests made during weekdays; (4) all requests made during the weekend; (5) requests made in Dutch; (6) requests made in French; and (7) those made for journeys between stations inside the RER zone
The analysis proposed in this paper would gain from relying on the travel requests made on the official SNCB application, even if the pattern observed here is consistent with the geography of Belgium
Summary
Travel flows are commonly represented by graphs, wherein nodes are the origins, and destinations and edges are weighted by the intensity of the flows. Examples for home-to-work commuting include the Czech Republic [1], Ireland [2], Slovenia [3], the United Kingdom [4] and the city of Brussels [5] (in Belgium) All of these papers, rely on travel flows between administrative units, all transportation modes combined. For instance divided by transport mode or at the public transport stops level, rather than administrative delineation, may provide further insights on travel behavior Despite their limits and critics, information and communication technologies (ICT) are quite important for that purpose (see [6], for a review) and offer many opportunities in human geography [7]. The nodes represent the train stations, while the weight of a link (i, j) is the number of travel requests between stations i and j
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