| description abstract | With the advent of the Internet of Things, bike-sharing systems have seen widespread adoption globally, whereas they often grapple with an uneven spatiotemporal distribution of vehicles. This issue is particularly acute in the wake of electronic fences, with some areas often faced with the predicament of inadequate supply. To tackle this challenge, accurate prediction of borrowing and returning demands at different parking spots and varying times is necessary. In this study, we used a comprehensive data set from Yancheng, Jiangsu, China, covering shared bicycle usage across 394 parking spots. These data enabled us to delve deep into urban travel patterns and discern the various factors influencing these behaviors. To enhance the prediction accuracy, we propose the time-series weighted regression (TSWR) model, a long-term multistep forecasting method, which adeptly addresses issues associated with sparse statistical data and long-term prediction inaccuracies, outperforming other machine learning models in our experiments. Further recognizing the considerable impact of geographical location and weather conditions on shared bicycle demand, we incorporated the rule-based adjustment optimization (RAO) method into our approach, which refines nonlinear components by accounting for various factors. The implementation of RAO resulted in a 10.34% increase in accuracy compared to TSWR alone and an improvement of over 35% in comparison to other approaches. Overall, this study illuminates the underlying influences on urban travel patterns and offers valuable suggestions for bike dispatching to those enterprises, contributing significantly to the research in this field. | |
