Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Machine Learning with Applications

DOI: 10.1016/j.mlwa.2025.100709

Authors: Navid Khorshidi, Shahriar Afandizadeh Zargari, Soheil Rezashoar, Hamid Mirzahossein

Institutions: Iran University of Science and Technology, Imam Khomeini International University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,340