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.
Highlights
A downside involved in road works is the reduction in the capacity of a road, leading to traffic congestion and inconveniencing drivers
Based on the cited studies emphasizing the efficacy and potential of the K-nearest neighbors (KNN) algorithm, this study aims to develop an algorithm that predicts traffic speeds based on changing traffic conditions caused by work zones on urban roads
It is crucial to prepare an appropriate traffic management strategy for the expected congestion level by predicting the travel speed after road work to prevent congestion caused by road works. is study developed a model that predicts the travel speed of the work zone using the clustered K-nearest neighbors (CKNN) algorithm
Summary
A downside involved in road works is the reduction in the capacity of a road, leading to traffic congestion and inconveniencing drivers. Other essential details, including expected traffic congestion and estimated travel time based on road works, are not disclosed. Erefore, drivers navigating or near the work zone may experience significantly longer travel times than expected due to the restricted notices. Predicting the network impact of road constructions can provide drivers with opportunities to choose detours [1, 2] and allow road managers to use it as data for establishing traffic operation strategies in case of traffic congestion. It is necessary to have a system that predicts the impact of work on traffic flow to reduce congestion caused by frequent road works while providing information to drivers or road managers ahead of time. An algorithm for predicting the speed of neighboring road links after road work and a method for understanding the effect of road construction on the network should be developed
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