Improving Dublin's E-Bike Experience
The researchers initiated this project by developing an open dataset, collected in Dublin, specifically designed to model the energy usage of shared e-bikes.
Previous studies have relied on more generalised data, which was less effective. Additionally, Dr. Liu and his team have developed a smart parking recommendation system to help users park safely and ensure ready access to bikes across the city.
Dr. Liu's research found that up to 12.9% of shared e-bike users did not properly park their bikes at designated stands as a result of insufficient planning and distribution. This naturally reduced the overall operational efficiency of the service. In response, Dr Liu's team has created Upark, a smart parking system that aims to enhance the user experience by providing accurate and personalised parking suggestions. Upark combines historical journey data and real-time trip trajectories to accurately and proactively predict both the user's destination and the availability of nearby parking spots. Based on these predictions, Upark provides personalized parking suggestions to enable users to efficiently locate parking spaces.
Read the full paper here.