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

被引：1

作者：

Park, Hyun Su ^{[1
]}

Park, Yong Woo ^{[2
]}

Kwon, Oh Hoon ^{[2
]}

Park, Shin Hyoung ^{[3
]}

机构：

[1] Univ Seoul, Inst Urban Sci, Seoul 02504, South Korea

[2] Keimyung Univ, Dept Transportat Engn, Daegu 42601, South Korea

[3] Univ Seoul, Dept Transportat Engn, Seoul 02504, South Korea

来源：

JOURNAL OF ADVANCED TRANSPORTATION | 2022年 / 2022卷

关键词：

TRAFFIC FLOW PREDICTION; TIME PREDICTION; MODEL; NETWORK;

D O I：

10.1155/2022/1107048

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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.

引用

页数：11

共 11 条

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