The Multilayer Temporal Network of Public Transport in Great BritainThe dataset describes the Public Transport Network of Great Britain by use of a Multilayer node-list and edge-list, where each layer is associated to a mode of transport. Each node is geo-referenced, thus defining a spatial network, and associated to a wide list of administrative meta-data. For each edge, the minimal travel-time in minutes is indicated. For the case of inter-layer edges, this is defined as the walking time needed for the inter-modal connection. For each intra-layer edge, we also specify a list of temporal events representing the vehicles' rides along the edges, with origin time and travel duration expressed in minutes with a precision of one minute. In Multilayer networks, each node may have several ‘copies’ in different layers. For that reason, nodes are identified by two numbers, one specifying the node itself, and another the layer where it belongs. As a consequence, the edges are identified by four numbers, two for the origin and two for the destination (node and layer). The ordering of the fields in edge-list has been chosen to conform to the tensorial notation widely used for this kind of networks. There is no established standard format for the temporal event list. Therefore, we decided to use a format, derived from the idea of adjacency lists, which has been specifically adapted to this dataset.ML_PT_GB_v1.11.zip

Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.