Playing with Matches: Vehicular Mobility through Analysis of Trip Similarity and Matching (1809.02298v2)

Abstract: Understanding city-scale vehicular mobility and trip patterns is essential to addressing many problems, from transportation and pollution to public safety, among others. Using spatio-temporal analysis of vehicular mobility, promising solutions can be proposed to alleviate these major challenges, utilizing shared mobility and crowd-sourcing. The rise of transportation networks (e.g. Uber, Lyft), is a mere beginning to shared mobility. In this paper, we address problems of trip representation and matching. Particularly, we study a real-world dataset of trips (from Cologne, Germany), from spatial and temporal perspectives. Comparison of trajectories is desired for applications relying on spatio-temporal phenomena. For that purpose, we present a novel combined spatio-temporal similarity score, based on the weighted geometric mean (WGM) and conduct experiments on its applicability and strengths. First, we use the score to find clusters of trips that were spatially and/or temporally separable using spectral clustering. The score is then used in a real-time matching of trips for Catch-a-Ride (CaR) and CarPooling (CP) scenarios. CaR and CP achieve $\approx40\%$ and $\approx25\%$ decrease in traveled distances respectively, at the cost of moving to pick-up and from drop-off locations (i.e. drivers going on average $<700m$ out of their way on pick-up and drop-off for CP). Additionally, a comparison with the metrics available in the literature is presented on CaR scenario. We find that main advantages of WGM include the flexibility to favor time or space components, and linearity of runtime complexity. Finally, we formulate an optimal free float Car-Sharing scenario (e.g. scheduling a system of automated vehicles or taxis) resulting in an average of $\approx3.88$ trips serviced by a car in one hour.