This paper copes with the issue of extracting mobility patterns in a urban computing scenario. The computation is parallelized by partitioning the territory into a number of regions. In each region a computing node collects data from a set of local sensors, analyzes the data and coordinates with neighbor regions to extract the mobility patterns. We propose and analyze a “local” synchronization approach, where computation regarding a specific region is performed using the information received from a subset of neighbor regions. When opposed to the usual approach, where the computation proceeds after collecting the results from all the regions, our approach offers notable benefits: reduction of computation time, real-time model extraction, better support to local decisions. The paper describes the model of local synchronization by means of a Petri net and analyzes the performance in terms of the ability of the system of keeping the pace with the data collected by sensors. The analysis is based on a real world dataset tracing the movements of taxis in the urban area of Beijing.
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