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Road Travel Time Prediction Method Based on Random Forest Model

  • Wanchao SongEmail author
  • Yinghua Zhou
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)

Abstract

Accurately predicting the travel time of each key road in a certain period of time will help the traffic management department to take measures to prevent and reduce traffic congestion. At the same time, it can help to make an optimal travel plan for the traveler based on the dynamic traffic information. Consequently, the utilization efficiency of the load can be improved. RF-DBSCAN, a prediction model based on the random forest (RF) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), is proposed. After trained using the history traffic datasets, the model can predict the road travel time taking into account the regularity of time series, weather factors, road structures, weekends, and holidays. Experiments are carried out and the results show that the RF-DBSCAN has higher accuracy compared with the traditional random forest and GBDT (Gradient Boosting Decision Tree).

Keywords

Travel time Random forest Density clustering 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Chongqing University of Posts and TelecommunicationsChongqingChina

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