scholarly journals Research on Travel Time Prediction Model of Freeway Based on Gradient Boosting Decision Tree

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 7466-7480 ◽  
Author(s):  
Juan Cheng ◽  
Gen Li ◽  
Xianhua Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Travel Time ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Time Prediction

scholarly journals A Freeway Travel Time Prediction Method Based on An XGBoost Model

2021 ◽  
Vol 13 (15) ◽  
pp. 8577
Author(s):  
Zhen Chen ◽  
Wei Fan
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Model Performance ◽  
Route Planning ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Vehicle Data ◽  
Comparison Results

Travel time prediction plays a significant role in the traffic data analysis field as it helps in route planning and reducing traffic congestion. In this study, an XGBoost model is employed to predict freeway travel time using probe vehicle data. The effects of different parameters on model performance are investigated and discussed. The optimized model outputs are then compared with another well-known model (i.e., Gradient Boosting model). The comparison results indicate that the XGBoost model has considerable advantages in terms of both prediction accuracy and efficiency. The developed model and analysis results can greatly help the decision makers plan, operate, and manage a more efficient highway system.

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