Latest revision as of 00:39, 29 January 2021

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 This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above.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.

National Science Foundation (U.S.) (Grant CPS-0931550)

National Science Foundation (U.S.) (Grant 0735953)

United States. Office of Naval Research (Grant N00014-09-1-105)

United States. Office of Naval Research (Grant N00014-09-1-1031)

Document type: Conference object

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Original document

The different versions of the original document can be found in:

• https://dspace.mit.edu/bitstream/1721.1/90617/1/Rus_City-scale%20traffic.pdf under the license https://creativecommons.org/licenses/by-nc-sa

• http://hdl.handle.net/1721.1/90617,

https://orcid.org/0000-0001-5473-3566 under the license cc-by-nc-sa

• https://dspace.mit.edu/handle/1721.1/90617,

https://core.ac.uk/display/78055879,

https://dblp.uni-trier.de/db/conf/sensys/sensys2012.html#AslamLPR12,

https://dl.acm.org/citation.cfm?id=2426671,

https://doi.org/10.1145/2426656.2426671,

https://academic.microsoft.com/#/detail/2050134713 under the license http://creativecommons.org/licenses/by-nc-sa/4.0/

• http://dl.acm.org/ft_gateway.cfm?id=2426671&ftid=1332975&dwn=1,

http://dx.doi.org/10.1145/2426656.2426671