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Electric shared micromobility services (ESMSs) has become a vital element within the mobility as a service framework, contributing to sustainable transportation systems. However, existing ESMS face notable design challenges such as shortcomings in integration, transparency, and user-centered approaches, resulting in increased operational costs and decreased service quality. A key operational issue for ESMS revolves around parking, particularly ensuring the availability of parking spaces as users approach their destinations. For instance, a recent study illustrated that nearly 13% of shared e-bike users in Dublin, Ireland, encounter difficulties parking their e-bikes due to inadequate planning and guidance. In response, we introduce U-Park, a user-centric smart parking recommendation system designed for ESMS, providing tailored recommendations to users by analyzing their historical mobility data, trip trajectory, and parking space availability. We present the system architecture, implement it, and evaluate its performance using real-world data from an Irish-based shared e-bike provider, MOBY Bikes. Our results illustrate U-Park's ability to predict a user's destination within a shared e-bike system, achieving an approximate accuracy rate of over 97.60%, all without requiring direct user input. Experiments have proven that this predictive capability empowers U-Park to suggest the optimal parking station to users based on the availability of predicted parking spaces, improving the probability of obtaining a parking spot by 24.91% on average and 29.66% on maximum when parking availability is limited.
Yan et al. (Tue,) studied this question.