Development of a Data‐Driven Framework for Real‐Time Travel Time Prediction. (25th May 2016)

• Record Type: Journal Article
• Authors: Tak, Sehyun; Kim, Sunghoon; Oh, Simon; Yeo, Hwasoo
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 31:Number 10(2016:Oct.)
• Page Start: 777
• Page End: 793
• Publication Date: 2016-05-25
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12205
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: 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.