Towards Sustainable Urban Transportation: A Data-Infused Approach to Bike-Sharing Systems Optimization in Smart Cities

It has explored the integration of machine learning techniques to enhance the efficiency of bike-sharing systems within the context of smart cities, focusing on predicting demand patterns, optimizing bike deployment strategies, and implementing data-driven solutions for real-time adjustments. The research aims to contribute to the refinement of urban transportation systems, aligning with the principles of smart city development. By leveraging machine learning algorithms, the study investigates how predictive modeling can be employed to optimize the allocation of bikes, ultimately improving the overall effectiveness of bike-sharing systems. The research methodology involves analyzing historical data to develop robust predictive models. The findings are expected to provide valuable insights into the dynamic nature of urban mobility, offering practical recommendations for the optimization of bike-sharing networks in smart cities.