Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Adaptive fastest path computation on a road network: a traffic mining approach
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach
IEEE Transactions on Knowledge and Data Engineering
Clustering of time series data-a survey
Pattern Recognition
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A new metric for probability distributions
IEEE Transactions on Information Theory
On Discovery of Traveling Companions from Streaming Trajectories
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Multidimensional Analysis of Atypical Events in Cyber-Physical Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Mining event periodicity from incomplete observations
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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The widespread use of road sensors has generated huge amount of traffic data, which can be mined and put to various different uses. Finding frequent trajectories from the road network of a big city helps in summarizing the way the traffic behaves in the city. It can be very useful in city planning and traffic routing mechanisms, and may be used to suggest the best routes given the region, road, time of day, day of week, season, weather, and events etc. Other than the frequent patterns, even the events that are not so frequent, such as those observed when there is heavy snowfall, other extreme weather conditions, long traffic jams, accidents, etc. might actually follow a periodic occurrence, and hence might be useful to mine. This problem of mining the frequent patterns from road traffic data has been addressed in previous works using the context knowledge of the road network of the city. In this paper, we have developed a method to mine spatiotemporal periodic patterns in the traffic data and use these periodic behaviors to summarize the huge road network. The first step is to find periodic patterns from the speed data of individual road sensor stations, and use their periods to represent the station's periodic behavior using probability distribution matrices. Then, we use density-based clustering to cluster the sensors on the road network based on the similarities between their periodic behavior as well as their geographical distance, thus combining similar nodes to form a road network with larger but fewer nodes.