Positive connotations of map-matching based on sub-city districts for trajectory data analytics

We propose a new data pre-processing method, sub-district-based map matching (SDBMM), which involves projecting a trajectory onto sub-city districts (SCDs), geographical areas on the map with irregular boundaries. Thus, this method differs significantly from traditional map-matching processes, which match trajectory data points to the nearest or most probable road segments. With SDBMM, a moving object is represented through gradual transitions through a set of SCDs instead of ‘blinking’ at the road segments rapidly. This change in information granularity yields more presentation efficiency. As SCDs are meaningful partitions of a city based on urban planning or travel behaviours, SDBMM also underlies a new ground for post hoc analytics. In the first application, we perform SDBMM for a large taxi-trajectory dataset in a large city. This case verifies SDBMM with sufficient massive data and brings valuable knowledge to several parties (i.e. taxi drivers, service operators, and the government) in managing the taxi transport mode in the city and policy-making. We apply SDBMM in a second application of anti-drug investigation and find that SCDs with pathway entrances/exits across the mountain ranges are usually the hot traces of drug transactions. These practical applications may foster greater confidence in future utilisations of SDBMM.