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A Freeway Travel Time Prediction Method Based on an XGBoost Model
| Content Provider | MDPI |
|---|---|
| Author | Chen, Zhen Fan, Wei |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 8577 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su13158577 |
| Journal | Sustainability |
| Issue Number | 15 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-31 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Transportation Science and Technology Transportation Systems Travel Times Machine Learning Forecasting Xgboost Model |
| Content Type | Text |
| Resource Type | Article |
