X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Taxi-aware map: identifying and predicting vacant taxis in the city
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
Urban mobility study using taxi traces
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Sensing urban mobility with taxi flow
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Prediction of urban human mobility using large-scale taxi traces and its applications
Frontiers of Computer Science in China
Urban traffic modelling and prediction using large scale taxi GPS traces
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrated in a web mash-up application to show that context-aware demand prediction can help improve the management of taxi fleets.