Traffic delay prediction from historical observations
Name
755720221-MIT.pdf
Description
Full printable version
Size
7.73 MB
Format
Adobe PDF
Checksum (MD5)
e152f9b92e4a183898fe0fa5ad336320
Author(s)
Malalur, Paresh (Paresh G.)
Advisor(s)
Hari Balakrishnan and Samuel Madden.
Alternative Title
Traffic delay prediction from sparse historical observations
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
Road traffic congestion is one of the biggest frustrations for the daily commuter. By improving the currently available travel estimates, one can hope to save time, fuel and the environment by avoiding traffic jams. Before one can predict the best route for a user to take, one must first be able to accurately predict future travel times. In this thesis, we develop a classification-based technique to extract information from historical traffic data to help improve delay estimates for road segments. Our techniques are able to reduce the traffic delay prediction error rate from over 20% to less than 10%. We were hence able to show that by using historical information, one can drastically increase the accuracy of traffic delay prediction. The algorithm is designed to enable delay prediction on a per-segment basis in order to enable the use of simple routing schemes to solve the bigger problem of predicting best future travel paths.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 75-77).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
Persistent DSpace Link
