Real-Time E-bike Route Planning with Battery Range Prediction

Abstract

Electric bicycles (EBs) have gained immense popularity as an environmentally friendly and convenient transportation mode. However, range anxiety remains a major concern for EB users. This paper presents a real-Time route planning model focused on predicting the remaining range of EBs. First, we represent the user's interaction data and the real-Time battery state as a dynamic graph. Then we propose a novel approach called the Real-Time Electric Bicycle Remaining Range (RtRR) prediction model, which leverages the graph structure and jointly optimizes temporal edge convolution, LSTM, and Transformer models to estimate the remaining EB battery range. Based on the prediction, we can update the optimal cycling routes for users in real-Time, considering charging station locations. Extensive evaluations demonstrate that our proposed RtRR model outperforms 9 baseline methods on real-world datasets. The route planning based on RtRR prediction effectively alleviates range anxiety and enhances the user experience. It can be accessed at https://github.com/gu-yongchun/Real-Time-E-bike-Route-Planning-with-Battery-Range-Prediction.