Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work ZonesAuthor/Presenter: Park, Hyun Su; Park, Yong Woo; Kwon, Oh Hoon; Park, Shin Hyoung
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.
Publication Date: 2022
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Advanced Traffic Management Systems; Data Collection; Mathematical Models; Temporary Traffic Control; Traffic Speed; Urban Highways; Work Zones