Improving the service of E-bike sharing by demand pattern analysis: A data-driven approach

Abstract

In recent years, there has been a surge in the popularity of free-floating e-bike sharing. However, the shared mobility sector is fiercely competitive demanding, efficient operations and high-quality service to cater to user expectations.We propose several data-driven methods that apply demand pattern analysis. We suggest the use of a new spatial unit (i.e., overlapping circles) to enhance the cost-efficiency and user-friendliness of e-bike sharing. Moreover, temporal clustering is employed to develop operational strategies that counter the imbalance in supply and demand in recurrent clusters.To evaluate the impact of these strategies, we introduce a framework and apply it in a case study of an e-bike sharing project in The Hague, The Netherlands. We identify 5 hourly clusters which enable reallocation strategies to alleviate the imbalance among spatial units in these clusters.The results demonstrate that the derived operational strategies improve the service significantly, with almost 1.5 times increased ridership, an approximately 20% decrease in vehicle idle time, and a decent monthly net retention rate of around 60%.