Evaluating High-Demand Periods in Bicycle Sharing Through Machine Learning: A Case Study in London

Bike-sharing systems have become an integral part of urban mobility, offering sustainable and accessible transportation solutions. However, optimizing their usage requires a comprehensive understanding of factors influencing demand. This study investigates the determinants of shared bicycle usage in London, focusing on the effects of weather, temperature, and temporal factors, including peak demand periods. Leveraging advanced machine learning techniques such as heatmaps, decision trees (DT), support vector machines (SVM), and K-nearest neighbors (KNN), the analysis identifies key variables influencing hourly and daily demand. The findings reveal the impact of weather conditions, with humidity and temperature being prominent predictors, while temporal patterns, such as high-demand periods during specific times of the day and weekdays, provide different perspectives for system optimization. This research emphasizes the distinct influence of time-of-day variations and peak demand periods on shared bicycle usage. The findings offer practical recommendations for optimizing operational strategies, enhancing user experience, and supporting data-driven decision-making in bike-sharing systems.