@techreport{zhu2011inferring,
author = {Zhu, Yin and Zheng, Yu and Zhang, Liuhang and Santani, Darshan and Xie, Xing and Yang, Qiang},
title = {Inferring Taxi Status Using GPS Trajectories},
year = {2011},
month = {November},
abstract = {In this paper, we infer the statuses of a taxi, consisting of occupied, non-occupied and parked, in terms of its GPS trajectory. The status information can enable urban computing for improving a city’s transportation systems and land use planning. In our solution, we first identify and extract a set of effective features incorporating the knowledge of a single trajectory, historical trajectories and geographic data like road network. Second, a parking status detection algorithm is devised to find parking places (from a given trajectory), dividing a trajectory into segments (i.e., sub-trajectories). Third, we propose a two-phase inference model to learn the status (occupied or non-occupied) of each point from a taxi segment. This model first uses the identified features to train a local probabilistic classifier and then carries out a Hidden Semi-Markov Model (HSMM) for globally considering long term travel patterns. We evaluated our method with a large-scale real-world trajectory dataset generated by 600 taxis, showing the advantages of our method over baselines.},
url = {http://approjects.co.za/?big=en-us/research/publication/inferring-taxi-status-using-gps-trajectories/},
number = {MSR-TR-2011-144},
}