An emergent strategy for characterizing urban hotspot dynamics via GPS data (original) (raw)

The increasing volume of urban human mobility data arises unprecedented opportunities to monitor and understand city dynamics. Identifying events which do not conform to the expected patterns can enhance the awareness of decision makers for a variety of purposes, such as the management of social events or extreme weather situations [1]. For this purpose GPS-equipped vehicles provide huge amount of reliable data about urban dynamics, exhibiting correlation with human activities, events and city structure [2]. For example, in [3] the impact of a social event is evaluated by analyzing taxi traces data. Here, the authors model typical passenger flow in an area, in order to compute the probability that an event happens. Then, the event impact is measured by analyzing abnormal traffic flows in the area via Discrete Fourier Transform. In [4] GPS trajectories are mapped through an Interactive Voting-based Map Matching Algorithm. This mapping is used for off-line characterization of normal d...