Optimizing travel time reliability with XAI: A Virginia interstate network case using machine learning and meta-heuristics

• Published in: Machine learning with applications Vol. 21; p. 100709
• Main Authors: Khorshidi, Navid, Zargari, Shahriar Afandizadeh, Rezashoar, Soheil, Mirzahossein, Hamid
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2025; Elsevier
• Subjects: Hyperparameter optimization; Machine learning; Meta-heuristic; Travel time reliability; Hyperparameter optimization; Meta-heuristic; Travel time reliability; Machine learning
• ISSN: 2666-8270; 2666-8270
• DOI: 10.1016/j.mlwa.2025.100709

•XGBoost-GWO achieved 92 % accuracy in travel time reliability prediction, outperforming the base model and other optimized tree-based models.•Metaheuristic optimization with GWO improved model performance, showing superior results with fewer control parameters and faster convergence.•Feature importance analysis identified Link Length and AADT/Lane.mile as key predictors of travel time reliability.•Machine learning and metaheuristic integration enhance travel time prediction accuracy, supporting data-driven transportation planning. This paper applies machine learning models to predict travel time reliability in transportation networks, using XGBoost, LightGBM, and CatBoost optimized with seven metaheuristic algorithms. The models were fine-tuned with a four-year dataset (2014–2017) covering 59 interstate sections in Virginia. Key features Link Length, AADT/mile/lane, Total Rate, and PRCP/1000 were identified as influential factors for travel time index prediction. Results revealed that XGBoost optimized with Grey Wolf Optimizer (GWO) achieved the highest accuracy at 92 %, surpassing the base model. LightGBM-GWO and CatBoost-GWO also demonstrated improvements, scoring up to 89 %. GWO outperformed other optimization methods, delivering superior accuracy with fewer control parameters. Feature importance analysis highlighted Link Length and AADT/Lane.mile as critical predictors. This research enhances travel time reliability prediction, providing insights for transportation planning and management. Future work includes exploring multi-objective optimization and integrating additional features to refine model accuracy further.