Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing of network constrained moving objects
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
On-line discovery of hot motion paths
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Trajectories Mining for Traffic Condition Renewing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Taxonomy-driven lumping for sequence mining
Data Mining and Knowledge Discovery
Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Proceedings of the 5th French-Speaking Conference on Mobility and Ubiquity Computing
Discovering private trajectories using background information
Data & Knowledge Engineering
Towards mobility-based clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern-based moving object tracking
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Computing turn delay in city road network with GPS collected trajectories
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Proceedings of the 13th international conference on Ubiquitous computing
Synthesizing routes for low sampling trajectories with absorbing Markov chains
WAIM'11 Proceedings of the 12th international conference on Web-age information management
LOCAR: local compression of alternative routes
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Road traffic congestion in the developing world
Proceedings of the 2nd ACM Symposium on Computing for Development
Summarizing trajectories into k-primary corridors: a summary of results
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Privacy-preserving trajectory data publishing by local suppression
Information Sciences: an International Journal
Finding time period-based most frequent path in big trajectory data
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Calibrating trajectory data for similarity-based analysis
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A data-driven approach for convergence prediction on road network
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
Adaptive collective routing using gaussian process dynamic congestion models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatiotemporal periodical pattern mining in traffic data
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Capturing hotspots for constrained indoor movement
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Deploying a network of smart cameras for traffic monitoring on a "city kernel"
Expert Systems with Applications: An International Journal
A framework of traveling companion discovery on trajectory data streams
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Effective co-betweenness centrality computation
Proceedings of the 7th ACM international conference on Web search and data mining
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Finding hot routes (traffic flow patterns) in a road network is an important problem. They are beneficial to city planners, police departments, real estate developers, and many others. Knowing the hot routes allows the city to better direct traffic or analyze congestion causes. In the past, this problem has largely been addressed with domain knowledge of city. But in recent years, detailed information about vehicles in the road network have become available. With the development and adoption of RFID and other location sensors, an enormous amount of moving object trajectories are being collected and can be used towards finding hot routes. This is a challenging problem due to the complex nature of the data. If objects traveled in organized clusters, it would be straightforward to use a clustering algorithm to find the hot routes. But, in the real world, objects move in unpredictable ways. Variations in speed, time, route, and other factors cause them to travel in rather fleeting "clusters." These properties make the problem difficult for a naive approach. To this end, we propose a new density-based algorithm named FlowScan. Instead of clustering the moving objects, road segments are clustered based on the density of common traffic they share. We implemented FlowScan and tested it under various conditions. Our experiments show that the system is both efficient and effective at discovering hot routes.