Stacked Graphs – Geometry & Aesthetics
IEEE Transactions on Visualization and Computer Graphics
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
An Interactive-Voting Based Map Matching Algorithm
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
PET: a statistical model for popular events tracking in social communities
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 13th international conference on Ubiquitous computing
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
Flow Map Layout via Spiral Trees
IEEE Transactions on Visualization and Computer Graphics
A Taxi Driving Fraud Detection System
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Transforming GIS Data into Functional Road Models for Large-Scale Traffic Simulation
IEEE Transactions on Visualization and Computer Graphics
Constructing popular routes from uncertain trajectories
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Inferring the Root Cause in Road Traffic Anomalies
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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The advances in mobile computing and social networking services enable people to probe the dynamics of a city. In this paper, we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media. Traffic anomalies are caused by accidents, control, protests, sport events, celebrations, disasters and other events. Unlike existing traffic-anomaly-detection methods, we identify anomalies according to drivers' routing behavior on an urban road network. Here, a detected anomaly is represented by a sub-graph of a road network where drivers' routing behaviors significantly differ from their original patterns. We then try to describe the detected anomaly by mining representative terms from the social media that people posted when the anomaly happened. The system for detecting such traffic anomalies can benefit both drivers and transportation authorities, e.g., by notifying drivers approaching an anomaly and suggesting alternative routes, as well as supporting traffic jam diagnosis and dispersal. We evaluate our system with a GPS trajectory dataset generated by over 30,000 taxicabs over a period of 3 months in Beijing, and a dataset of tweets collected from WeiBo, a Twitter-like social site in China. The results demonstrate the effectiveness and efficiency of our system.