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

Playing with Matches: Vehicular Mobility through Analysis of Trip Similarity and Matching