City-scale traffic estimation from a roving sensor network
Name
Rus_City-scale traffic.pdf
Size
4.55 MB
Format
Adobe PDF
Checksum (MD5)
be6f1147909b842e152454c0e00b22d0
Author(s)
Aslam, Javed
Lim, Sejoon
Pan, Xinghao
Rus, Daniela L.
Date Issued
November 2012
Journal
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (SenSys '12)
Publisher
Association for Computing Machinery (ACM)
Citation
Javed Aslam, Sejoon Lim, Xinghao Pan, and Daniela Rus. 2012. City-scale traffic estimation from a roving sensor network. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (SenSys '12). ACM, New York, NY, USA, 141-154.
Version
Author's final manuscript
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 This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above.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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. School of Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2426656.2426671
