E Bike

Electric bikes (e-bikes) are gaining popularity as a sustainable transportation solution, prompting research into optimizing their usage and impact. Current research focuses on improving battery range prediction using machine learning models, such as transformer-based graph neural networks, to enhance user experience and operational efficiency in shared e-bike systems. Studies also employ machine learning algorithms like ARIMA and Random Forest to model e-bike sales growth and assess their environmental and health benefits, providing valuable data for policymakers. This research contributes to a better understanding of e-bike adoption, enabling informed decisions regarding urban planning, transportation strategies, and public health initiatives.