Traffic delay prediction from historical observations
Author(s)Malalur, Paresh (Paresh G.)
Traffic delay prediction from sparse historical observations
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Hari Balakrishnan and Samuel Madden.
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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.
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).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.