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

Hyun Su Park, Yong Woo Park, Oh Hoon Kwon, Shin Hyoung Park

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number1107048
JournalJournal of Advanced Transportation
Volume2022
DOIs
StatePublished - 2022

Fingerprint

Dive into the research topics of 'Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones'. Together they form a unique fingerprint.

Cite this