Map-matching for low-sampling-rate GPS trajectories
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Contraction hierarchies: faster and simpler hierarchical routing in road networks
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Urban mobility study using taxi traces
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
PHAST: Hardware-Accelerated Shortest Path Trees
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
A Taxi Driving Fraud Detection System
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Reducing Uncertainty of Low-Sampling-Rate Trajectories
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Crowd sensing of traffic anomalies based on human mobility and social media
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Outlier detection in large-scale taxi trip records has imposed significant technical challenges due to huge data volumes and complex semantics. In this paper, we report our preliminary work on detecting outliers from 166 millions taxi trips in the New York City (NYC) in 2009 through efficient spatial analysis and network analysis using a NAVTEQ street network with half a million edges. As a byproduct of large-scale shortest path computation in outlier detection, betweenness centralities of street network edges are computed and mapped. The techniques can be used to help better understand the connection strengths among different parts of NYC using the large-scale taxi trip records.