Machine Learning
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
Adaptive Routing of Cruising Taxis by Mutual Exchange of Pathways
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
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
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
Towards reducing taxicab cruising time using spatio-temporal profitability maps
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Sensing places' life to make city smarter
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
U2SOD-DB: a database system to manage large-scale ubiquitous urban sensing origin-destination data
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Some help on the way: opportunistic routing under uncertainty
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Detecting regions of disequilibrium in taxi services under uncertainty
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Exploring social properties in vehicular ad hoc networks
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
Finding time period-based most frequent path in big trajectory data
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Inferring human mobility patterns from taxicab location traces
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Exploring relationship between taxi volume and flue gases' concentrations
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Real Time Anomalous Trajectory Detection and Analysis
Mobile Networks and Applications
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
coRide: carpool service with a win-win fare model for large-scale taxicab networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations and the routes to these locations, towards which they are more likely to pick up passengers quickly (during the routes or at these locations) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model which estimates the profit of the candidate locations for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using historical trajectories generated by over 12,000 taxis during 110 days.