Strategic Deployment of Electric Buses Through Replacement Factor Prediction: A Machine Learning Framework for Cost‐Effective Electrification

ABSTRACT The transition to electric buses (e‐buses) is essential for reducing greenhouse gas emissions in urban transit systems. However, successful e‐bus deployment requires careful planning to ensure service reliability while minimising costs. A key challenge in this transition is determining the replacement factor, the ratio of e‐buses needed to replace the current diesel‐engine bus fleet for a certain route. This factor is essential for transit agencies as it directly influences fleet size, capital investment, and operational efficiency. Accurately estimating replacement factors allows agencies, to prioritise routes where electrification achieves the highest economic and environmental benefits while preventing unnecessary fleet expansion and idle capacity by selecting routes with low replacement factors. This study develops a framework for estimating e‐bus replacement factors based on route characteristics, vehicle attributes, and external conditions. Multiple machine learning models are evaluated, with XGBoost achieving the highest accuracy (R 2 = 0.93). Model interpretability using SHapley Additive exPlanations (SHAP) analysis identifies the average bus speed and ambient temperature as the main variables affecting the replacement factor. The proposed framework enables transit agencies to optimise fleet deployment by prioritising routes with lower replacement factors, maximising e‐bus utilisation, and achieving cost efficiencies while aligning with environmental objectives.