A Freeway Travel Time Prediction Method Based on an XGBoost Model

Zhen Chen,Wei Fan

doi:10.3390/su13158577

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.

Highlights

Travel time prediction plays a significant role in the traffic data analysis field as it helps in route planning and reducing traffic congestion
Different machine learning approaches such as neural networks [1,2,3,4,5,6], ensemble learning [7,8,9,10,11,12], and support vector machine (SVM) [13] are employed by researchers. Their results indicate that such approaches for travel time prediction are adaptable and can give better performances than traditional models
This study aims to develop a methodology to apply the XGBoost model in travel time prediction

Summary

Introduction

Travel time prediction plays a significant role in the traffic data analysis field as it helps in route planning and reducing traffic congestion. Different machine learning approaches such as neural networks [1,2,3,4,5,6], ensemble learning [7,8,9,10,11,12], and support vector machine (SVM) [13] are employed by researchers Their results indicate that such approaches for travel time prediction are adaptable and can give better performances than traditional models.

Objectives

Methods

Results

Conclusion