2026-09-21T02:36:21Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/664442022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hari Balakrishnan and Samuel Madden.
Malalur, Paresh (Paresh G.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-10-17T21:26:43Z
2011-10-17T21:26:43Z
2011
2011
http://hdl.handle.net/1721.1/66444
755720221
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).
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.
by Paresh Malalur.
M.Eng.
71 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Traffic delay prediction from historical observations
Traffic delay prediction from sparse historical observations
Thesis
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Publication
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<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.</Abstract>
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