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Can I read A Freeway Travel Time Prediction Method Based on an XGBoost Model on EtoBox?
A Freeway Travel Time Prediction Method Based on an XGBoost Model by Chen, Zhen (author);Fan, Wei (author) is a Environmental Science article available to read on EtoBox.
What is A Freeway Travel Time Prediction Method Based on an XGBoost Model about?
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.
Who reads A Freeway Travel Time Prediction Method Based on an XGBoost Model?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Chen, Zhen (author);Fan, Wei (author)
- Publisher
- MDPI AG
- Published
- 2021
- Language
- EN
- Field
- Environmental Science (Social Sciences)