Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Activity-aware ECG-based patient authentication for remote health monitoring
Proceedings of the 2009 international conference on Multimodal interfaces
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 13th international conference on Ubiquitous computing
Computing with Spatial Trajectories
Computing with Spatial Trajectories
Inferring land use from mobile phone activity
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
From data to knowledge: city-wide traffic flows analysis and prediction using bing maps
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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We introduce our vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, we show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. We explore privacy algorithms based on the virtual trip lines (VTL) concept to regulate where and when the mobile data should be collected. The traffic modeling techniques feature an integration of traffic principles and learning/optimization techniques. The proposed methods are illustrated using two case studies for extracting traffic knowledge for urban signalized intersection.