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).
Road Travel Time Prediction Method Based on Random Forest Model
Smart Innovation, Systems and Technologies
2019-12-04
9 pages
Article/Chapter (Book)
Electronic Resource
English
Travel Time Reliability Prediction Using Quantile Random Forest Regression
Springer Verlag | 2025
|Travel Time Reliability Prediction Using Quantile Random Forest Regression
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|