Mingming Liu, DCU

Recently, Electric Shared Micromobility Services (ESMS), such as shared e-bikes, have been widely accepted around the world as an eco-friendly alternative to traditional transportation methods. However, finding a suitable parking space is a pressing problem for every ESMS user, especially in crowded urban areas, which will lead to wasted time and the potential for improper parking. Therefore, an effective and convenient assistance system is essential for users to help them find parking spaces, especially to meet the changing needs of users during their trips.

In Dublin, the inconsiderate parking issue is also presented. Our recent study analysed the parking behaviour of shared e-bike users in Dublin using real-world data from MOBY Bikes. Our research found that up to 12.9% of shared e-bike users did not park their shared e-bikes properly at designated stands, which inevitably reduced the overall operational efficacy of these tools.

To address these challenges, our research team has developed U-Park, a user-centric smart parking recommendation system for ESMS. Utilising advanced AI technologies and real-world datasets from MOBY Bikes and Dublin Bikes, U-Park aims to enhance the user experience by providing accurate and personalised parking suggestions based on historical journey data and real-time trip trajectories. Specifically, U-Park combines historical journey data and real-time trip trajectories to predict both the user’s destination and the availability of nearby parking spots accurately and proactively. Based on the prediction results, personalised parking suggestions are provided to enable users to efficiently locate parking spaces.

Image: Getty Images