Development of a Data-Driven Framework for Real-Time Travel Time Prediction

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Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long-term prediction in a real-time manner have been lacking. Existing methods do not fully utilize the advantages of the state-of-the-art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real-time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long-term (at least 6-hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k-nearest neighbor (Mk-NN) method which is compared with the conventional k-NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long-term travel time with shorter computation time.
Publisher
WILEY-BLACKWELL
Issue Date
2016-10
Language
English
Article Type
Article
Keywords

NEURAL-NETWORK MODEL; FREEWAY INCIDENT DETECTION; VEHICULAR TRAFFIC FLOW; NONPARAMETRIC REGRESSION; MISSING DATA; ALGORITHM; WEATHER; DEMAND; URBAN; DELAY

Citation

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, v.31, no.10, pp.777 - 793

ISSN
1093-9687
DOI
10.1111/mice.12205
URI
http://hdl.handle.net/10203/213771
Appears in Collection
CE-Journal Papers(저널논문)
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