Artificial Intelligence Applications for Demand Forecasting and Optimization

The rise of bike sharing systems in the continually evolving landscape of city mobility has emerged as an effective and sustainable remedy for addressing the challenges caused by the traffic environment and congestion concerns. With the increasing popularity of bike sharing systems, intelligent data-driven methods for optimizing their functioning are in demand. This chapter discovers the relevance of artificial intelligence (AI) in the specific spheres of demand forecasting and optimization within the context of predicting bike sharing usage. The collaboration between bike sharing and AI systems holds the potential for significant transformations in enhancing operational efficiency, user experience, and overall sustainability. Through the employment of sophisticated algorithms, machine learning models, and predictive analytics, this chapter investigates how AI methodologies can offer appreciated insights into forecast demand patterns, user behavior, and the allocation of resources within bike sharing networks. The exploration investigates the complexities of demand forecasting by utilizing external factors and historical usage data to develop robust models capable of precisely predicting future demand. The incorporation of AI in this domain allows operators to predict the peak usage periods and facilitate upbeat planning to meet fluctuating demand in real time.