Search

Objectives: Ridesharing is an essential part of shared mobility for improving passengers' travel efficiency in cities. The existing studies usually determine shareable trips based on whether the arrival sequence of vehicles in more than one pick-up and drop-off points can meet the predefined spatiotemporal constraints. Such a simple approach cannot quickly and comprehensively find all the potential shareable trips under scenarios involving large-scale car-sharing requests. Methods: Based on the modeling method of space-time prism and the spatial-temporal expression of passengers' sharing willing, we first propose a potential spatiotemporal path area model of travel. Then, we apply the topological relation between the spacetime prisms of trips for ridesharing identification, and quantify the strength of ridesharing of trips. Finally, two ridesharing matching strategies are proposed to simulate the ridesharing matching process in real-world transport environment. Results: The proposed ridesharing identification model can accurately delineate the potential space-time accessibility of vehicular travel, which makes it easier to discover all potential shareable trips and to realize the accurate and effective ridesharing identification. Conclusions: This study can be helpful for vehicle dispatching and passengers' travel planning in shared urban mobility system. [ABSTRACT FROM AUTHOR]

Human mobility patterns have been intensively investigated by scientists from computational social science, statistical physics and complex science. In last decade, mobile phone data provide an unprecedented tool for capturing individuals' travel activities in space and time. However, its nature of sparsity in time and imprecision in space imposes significant bias upon the derived mobility patterns. This research proposes two efficient techniques to cope with this issue. First, we implement an activity-location and travel-OD identification method to reconstruct reliable trajectories from call detailed records for mobile users. Second, we introduce the approximate entropy, which is superior to conditional entropy, for quantifying the regularity of individuals' consecutively visited locations. With a case study in Harbin, the proposed approaches enable us to uncover meaningful patterns of urban mobility in terms of frequently and consecutively visited locations. [ABSTRACT FROM AUTHOR]