A novel passenger hotspots searching algorithm for taxis in urban area

Passenger hotspots searching is essential to increase profits for taxis drivers in urban area. In this paper, we propose a two-step approach for pick-up hotspots searching. In the first step, a traveling similarity model is built to quantify the similarity of traveling behaviors. In the second step, we utilize affinity propagation and simulated annealing to identify the daily passenger hotspots in a selected period. Numerical results based on GPS data of Manhattan taxis suggest that the proposed approach outperforms the traditional spatio-temporal clustering regardless of buffer radius.