High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS
Joint Authors
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Despite the achievements of academic research on data-driven k-nearest neighbour nonparametric regression (KNN-NPR), the low-speed computational capability of the KNN-NPR method, which can occur during searches involving enormous amounts of historical data, remains a major obstacle to improvements of real-system applications.
To overcome this critical issue successfully, a high-speed KNN-NPR framework, capable of generating short-term traffic volume predictions, is proposed in this study.
The proposed method is based on a two-step search algorithm, which has the two roles of building promising candidates for input data during nonprediction times and identifying decision-making input data for instantaneous predictions at the prediction point.
To prove the efficacy of the proposed model, an experimental test was conducted with large-size traffic volume data.
It was found that the performance of the model not only at least equals that of linear-search-based KNN-NPR in terms of prediction accuracy, but also shows a substantially reduced execution time in approximating real-time applications.
This result suggests that the proposed algorithm can be also effectively employed as a preprocess to select useful past cases for advanced learning-based forecasting models.
American Psychological Association (APA)
Chang, Hyun-ho& Yoon, Byoung-jo. 2018. High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181434
Modern Language Association (MLA)
Chang, Hyun-ho& Yoon, Byoung-jo. High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS. Journal of Advanced Transportation No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1181434
American Medical Association (AMA)
Chang, Hyun-ho& Yoon, Byoung-jo. High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181434
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181434