Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones

Autor:	Hyun Su Park, Yong Woo Park, Oh Hoon Kwon, Shin Hyoung Park
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
Zdroj:	Journal of Advanced Transportation, Vol 2022 (2022)
Druh dokumentu:	article
ISSN:	2042-3195
DOI:	10.1155/2022/1107048
Popis:	This study developed and verified a travel speed prediction model based on the travel speed and work zone statistics collected from the advanced traffic management system (ATMS) real-time data in Daegu, South Korea. A clustered K-nearest neighbors (CKNN) algorithm was used to predict travel speed, resulting in a 6.9% average mean absolute percentage error (MAPE) using the data from 1,815 work zones. Furthermore, road network impact due to road work was calculated by comparing the travel speed prediction results obtained from the historical speed data. The predicted travel speed data in a work zone generated from this study is expected to allow drivers to select optimized paths and use them for traffic management strategies to operate in a work zone efficiently.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/99bbb49ac1074465a324197a835ac9b0 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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