Never drive alone: Boosting carpooling with network analysis |
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| Affiliation: | 1. KDDLab, Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, Pisa, Italy;2. KDDLab, ISTI-CNR, Via G. Moruzzi, 1, Pisa, Italy;1. CNRS, LAAS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France;2. Univ de Toulouse, LAAS, F-31400 Toulouse, France;3. Univ de Toulouse, INSA, LAAS, F-31400 Toulouse, France;4. Université de Rennes 1, Avenue du Général Leclerc 35042 Rennes Cedex, France;1. Transportation Research Institute (IMOB), Hasselt University, Wetenschapspark 5 bus 6, 3590 Diepenbeek, Belgium;2. Multiagent Group, Université Bourgogne Franche-Comté, UTBM, LE2I UMR CNRS 6306, 90010 Belfort cedex, France;1. Urban Planning Group, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands;2. Movares Consultancy & Engineering, PO Box 2855, 3500 GW Utrecht, The Netherlands;3. Construction, Management & Engineering Group, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands;1. IFSTTAR, AME, LPC, 25 allée des Marronniers – Satory, F-78000 Versailles, France;2. Université Jules Vernes Picardie, Research Center in Psychology: Cognitions, Psyche and Organizations (CRP-CPO EA 7273), Chemin du Thil, F-80000 Amiens Cedex, France |
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| Abstract: | Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling. |
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| Keywords: | Carpooling Complex networks Rank analysis Community discovery Big data analytics Mobility data mining |
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