A Freeway Travel Time Prediction Method Based on an XGBoost Model

Zhen Chen and Wei Fan
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Zhen Chen: USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Wei Fan: USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Sustainability, 2021, vol. 13, issue 15, 1-15

Abstract: 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.

Keywords: transportation systems; travel times; machine learning; forecasting; XGBoost model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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