Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
An Interactive-Voting Based Map Matching Algorithm
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
An energy-efficient mobile recommender system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards mobility-based clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Taxi-aware map: identifying and predicting vacant taxis in the city
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
Where to find my next passenger
Proceedings of the 13th international conference on Ubiquitous computing
Locating lucrative passengers for taxicab drivers
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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People often have the demand to decide where to wait for a taxi in order to save their time. In this paper, to address this problem, we employ the non-homogeneous Poisson process (NHPP) to model the behavior of vacant taxis. According to the statistics of the parking time of vacant taxis on the roads and the number of the vacant taxis leaving the roads in history, we can estimate the waiting time at different times on road segments. We also propose an approach to make recommendations for potential passengers on where to wait for a taxi based on our estimated waiting time. Then we evaluate our approach through the experiments on simulated passengers and actual trajectories of 12,000 taxis in Beijing. The results show that our estimation is relatively accurate and could be regarded as a reliable upper bound of the waiting time in probability. And our recommendation is a tradeoff between the waiting time and walking distance, which would bring practical assistance to potential passengers. In addition, we develop a mobile application TaxiWaiter on Android OS to help the users wait for taxis based on our approach and historical data.