Machine Learning - Special issue on learning with probabilistic representations
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Bayesian network model for semi-structured document classification
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Finding Fastest Paths on A Road Network with Speed Patterns
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal and spatial features of single-trial EEG for brain-computer interface
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Finding time-dependent shortest paths over large graphs
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Towards modeling the traffic data on road networks
Proceedings of the Second International Workshop on Computational Transportation Science
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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We maintain a one of a kind, large-scale and high resolution (both spatially and temporally) traffic sensor dataset collected from the entire Los Angeles County road network. Traffic sensors (installed under the road pavement) are used to measure real-time traffic flows through road segments. In this paper, we exploit this dataset to rigorously verify two popular instinctive understandings about traffic flows on road segments: 1) each road segment has a typical traffic flow (known by local travelers) and one can often categorize road segments based on the similarity of their traffic flows, and 2) the road segments within each category not only have similar traffic flows but also are similar in their other characteristics (such as locality, connectivity). Toward this end, we developed a hypothesis analysis framework based on a variety of clustering and correlation evaluation techniques and leveraged this framework to respectively show the following. First, the set of road segments can indeed be partitioned into a set of distinct subpartitions with similar traffic flows, and there is a limited number of signature traffic patterns/labels each of which can accurately represent all traffic flows of a subpartition of the road segments. Second, all segments within each subpartition (represented by one signature) are also highly similar in three other characteristics, namely, direction, connectivity and locality. Our experiments verify our observations with high confidence.