Passenger Searching from Taxi Traces Using HITS-Based Inference Model

Passenger-searching strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical GPS traces. Mining traces to understand the efficient passenger searching strategies can benefit drivers themselves. Traditionally, the research on passenger search strategies from taxi GPS traces is mainly focused on statistical techniques. Although this can improve the ability of drivers to find potential passengers through hotspots recommendation, most of these research still directly use raw GPS data and failed to take drivers' experience into account. Moreover, because driver's experience is behind of raw data, can't obtain directly, so the traditional model is unable to make good use of it during hot spots mining. In this paper, we proposed an inference model based on HITS (Hypertext Induced Topic Search), which perfectly describes the relationship between hot spots and drivers' experience and thus effectively handle the above problem. We first extract hotspots by an innovative PDBSCAN algorithm based on fuzzy grid partition and match them with corresponding landmarks by a landmark matching algorithm which based on kernel density estimation. Then the HITS-based inference model is used to mine popular hotspots and the most experienced drivers. Finally, we plan an optimal path for drivers. Experimental results demonstrate the efficiency and the ability of this method to provide drivers with better hotspots and hunting sequences recommendation.