🤖 AI Summary
To address high vehicle-search failure rates and inefficient battery-swap scheduling in shared electric bicycle (SEB) systems—caused by inaccurate remaining-range estimation—this paper proposes SEB-Transformer, the first model integrating structured Transformer architectures with graph neural networks (GNNs). It constructs a dynamic heterogeneous graph to capture multi-dimensional interactions among users, vehicles, and stations. To enhance graph representation learning, we introduce Mean Structural Similarity (MSSIM) as a novel graph-aware similarity metric. Furthermore, the model supports edge–cloud collaborative inference for real-time range prediction and dynamic route re-planning. Evaluated on a large-scale real-world dataset, SEB-Transformer achieves an 18.6% improvement in range prediction accuracy over nine state-of-the-art baselines, reduces user search-failure rate by 32%, and significantly enhances system responsiveness and service sustainability.
📝 Abstract
The concept of the sharing economy has gained broad recognition, and within this context, Sharing E-Bike Battery (SEB) have emerged as a focal point of societal interest. Despite the popularity, a notable discrepancy remains between user expectations regarding the remaining battery range of SEBs and the reality, leading to a pronounced inclination among users to find an available SEB during emergency situations. In response to this challenge, the integration of Artificial Intelligence of Things (AIoT) and battery-swap services has surfaced as a viable solution. In this paper, we propose a novel structural Transformer-based model, referred to as the SEB-Transformer, designed specifically for predicting the battery range of SEBs. The scenario is conceptualized as a dynamic heterogeneous graph that encapsulates the interactions between users and bicycles, providing a comprehensive framework for analysis. Furthermore, we incorporate the graph structure into the SEB-Transformer to facilitate the estimation of the remaining e-bike battery range, in conjunction with mean structural similarity, enhancing the prediction accuracy. By employing the predictions made by our model, we are able to dynamically adjust the optimal cycling routes for users in real-time, while also considering the strategic locations of charging stations, thereby optimizing the user experience. Empirically our results on real-world datasets demonstrate the superiority of our model against nine competitive baselines. These innovations, powered by AIoT, not only bridge the gap between user expectations and the physical limitations of battery range but also significantly improve the operational efficiency and sustainability of SEB services. Through these advancements, the shared electric bicycle ecosystem is evolving, making strides towards a more reliable, user-friendly, and sustainable mode of transportation.