Abstract
Multimodal transportation systems, with several coexisting services like bus, tram and metro, can be represented as time-resolved multilayer networks where the different transportation modes connecting the same set of nodes are associated with distinct network layers. Their quantitative description became possible recently due to openly accessible datasets describing the geo-localized transportation dynamics of large urban areas. Advancements call for novel analytics, which combines earlier established methods and exploits the inherent complexity of the data. Here, we provide a novel user-based representation of public transportation systems, which combines representations, accounting for the presence of multiple lines and reducing the effect of spatial embeddedness, while considering the total travel time, its variability across the schedule, and taking into account the number of transfers necessary. After the adjustment of earlier techniques to the novel representation framework, we analyse the public transportation systems of several French municipal areas and identify hidden patterns of privileged connections. Furthermore, we study their efficiency as compared to the commuting flow. The proposed representation could help to enhance resilience of local transportation systems to provide better design policies for future developments.
Highlights
Urban transportation systems interweave our everyday life and their construction is based on conscious design they appear with complex structural and dynamical features [1]
Efficient analysis of public transportation networks is possible via abstract representations, which in turn help us to reveal hidden characteristics of such systems
As our main scientific contribution we provided a solution for this challenge by introducing a novel description, which combines multiedge and P-space representations of multilayer transportation networks
Summary
Urban transportation systems interweave our everyday life and their construction is based on conscious design they appear with complex structural and dynamical features [1]. GTFS data are used for trip planning, ride-sharing, timetable creation, mobile data, visualization, accessibility and to provide real-time service informations These recent developments in data collection practices and in the corresponding fields of complex networks and human dynamics provided the opportunity to quantitatively study transportation systems using a data-driven approach. The emerging field of multilayer networks provided the methodology to consider their multimodal character [28,29] In this representation, each layer corresponds to the network of a single means of transportation (bus, tram, train, etc.), which are defined on the same set of nodes (stations). Note that the implementation of the proposed methodology is openly accessible online (https://github. com/lalessan/user_basedPT)
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