How Long a Passenger Waits for a Vacant Taxi -- Large-Scale Taxi Trace Mining for Smart Cities

• Published in: 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing pp. 1029 - 1036
• Main Authors: Guande Qi, Gang Pan, Shijian Li, Zhaohui Wu, Daqing Zhang, Lin Sun, Yang, Laurence Tianruo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2013
• Subjects: arriving model; Cities and towns; Data models; Global Positioning System; passenger's waiting time; Predictive models; Roads; Schedules; Smart city; taxi trace data
• DOI: 10.1109/GreenCom-iThings-CPSCom.2013.175

To achieve smart cities, real-world trace data sensed from the GPS-enabled taxi system, which conveys underlying dynamics of people movements, could be used to make urban transportation services smarter. As an example, it will be very helpful for passengers to know how long it will take to find a taxi at a spot, since they can plan their schedule and choose the best spot to wait. In this paper, we present a method to predict the waiting time for a passenger at a given time and spot from historical taxi trajectories. The arrival model of passengers and that of vacant taxis are built from the events that taxis arrive at and leave a spot. With the models, we could simulate the passenger waiting queue for a spot and infer the waiting time. The experiment with a large-scale real taxi GPS trace dataset is carried out to verify the proposed method.