Bike Sharing Demand Forecasting Based on the Informer Model

Abstract

Advancements in machine learning and artificial intelligence have been instrumental in enhancing demand forecasting for shared transportation modes, significantly benefiting urban traffic management and promoting a balanced supply-demand dynamic. This study harnesses the deep learning Informer model to forecast bike-sharing demand, integrating multiple factors to provide an empirical foundation for transport scheduling and redeployment, thereby contributing to the advancement of the field. Our experimental analysis reveals that the Informer model outperforms LSTM in predictive accuracy, as evidenced by improved R² scores and MSE values, reflecting gains in training efficiency and predictive precision. Despite these advances, predictions for extreme demand values, particularly during peak usage periods, indicate potential areas for refinement. Future research directions are oriented towards optimizing model accuracy by leveraging multimodal data for a comprehensive analysis and enhancement of traffic management strategies. This approach aims not only to better predict demand and alleviate congestion but also to deliver more optimized solutions for businesses and convenience for users. Furthermore, the model's application extends beyond bike-sharing to other forms of shared mobility, such as electric scooters, potentially aiding in reducing carbon emissions and furthering the development of the sharing economy and environmental sustainability.