2015/09/27 by Laura Alessandretti, Márton Karsai, Alessandretti, Laura +4 · 1 citation
Computer Science · Engineering · Physics and Astronomy · Social Sciences · #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #Traffic Prediction and Management Techniques #Transportation Planning and Optimization #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1509.08095
24 pages, 8 figures
arxiv created 2015/09/27 · openalex publication_date 2015/09/27 · arxiv updated 2015/09/29 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Multimodal transportation systems can be represented as time-resolved multilayer networks where different transportation modes connecting the same set of nodes are associated to distinct network layers. Their quantitative description became possible recently due to openly accessible datasets describing the geolocalised 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, our aim is to provide a novel user-based methodological framework to represent public transportation systems considering the total travel time, its variability across the schedule, and taking into account the number of transfers necessary. Using this framework we analyse public transportation systems in several French municipal areas. We incorporate travel routes and times over multiple transportation modes to identify efficient transportation connections and non-trivial connectivity patterns. The proposed method enables us to quantify the network's overall efficiency as compared to the specific demand and to the car alternative.