%0 Conference Paper %1 Aslam:2012:CTE %A Aslam, Javed %A Lim, Sejoon %A Pan, Xinghao %A Rus, Daniela %B Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems %C New York, NY, USA %D 2012 %I ACM %K BigData Data Monitoring Traffic %P 141--154 %R 10.1145/2426656.2426671 %T City-Scale Traffic Estimation from a Roving Sensor Network %X Traffic congestion, volumes, origins, destinations, routes, and other road-network performance metrics are typically collected through survey data or via static sensors such as traffic cameras and loop detectors. This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above. In this paper we conduct a case study that demonstrates that it is possible to accurately infer traffic volume through data collected from a roving sensor network of taxi probes that log their locations and speeds at regular intervals. Our model and inference procedures can be used to analyze traffic patterns and conditions from historical data, as well as to infer current patterns and conditions from data collected in real-time. As such, our techniques provide a powerful new sensor network approach for traffic visualization, analysis, and urban planning. %@ 978-1-4503-1169-4

@inproceedings{Aslam:2012:CTE, abstract = {Traffic congestion, volumes, origins, destinations, routes, and other road-network performance metrics are typically collected through survey data or via static sensors such as traffic cameras and loop detectors. This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above. In this paper we conduct a case study that demonstrates that it is possible to accurately infer traffic volume through data collected from a roving sensor network of taxi probes that log their locations and speeds at regular intervals. Our model and inference procedures can be used to analyze traffic patterns and conditions from historical data, as well as to infer current patterns and conditions from data collected in real-time. As such, our techniques provide a powerful new sensor network approach for traffic visualization, analysis, and urban planning.}, acmid = {2426671}, added-at = {2013-10-22T15:00:20.000+0200}, address = {New York, NY, USA}, author = {Aslam, Javed and Lim, Sejoon and Pan, Xinghao and Rus, Daniela}, biburl = {https://www.bibsonomy.org/bibtex/2867816f38bd47f3019877cd9a8955bb2/gron}, booktitle = {Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems}, description = {City-scale traffic estimation from a roving sensor network}, doi = {10.1145/2426656.2426671}, interhash = {85a99df4d3187266791e450e3283f521}, intrahash = {867816f38bd47f3019877cd9a8955bb2}, isbn = {978-1-4503-1169-4}, keywords = {BigData Data Monitoring Traffic}, location = {Toronto, Ontario, Canada}, numpages = {14}, pages = {141--154}, publisher = {ACM}, series = {SenSys '12}, timestamp = {2013-10-22T15:00:20.000+0200}, title = {City-Scale Traffic Estimation from a Roving Sensor Network }, year = 2012 }