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Understanding city-scale vehicular mobility and trip patterns is essential to addressing many problems, from transportation and pollution to public safety. Using spatio-temporal analysis of vehicular mobility, promising solutions can be proposed to alleviate these major challenges, utilizing shared mobility. 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 systematically evaluate its applicability and strengths. First, we use the score to find clusters of trips that were …