A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations

Zhuorui Wang (), Dexin Yu (), Xiaoyu Zheng, Fanyun Meng and Xincheng Wu
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Zhuorui Wang: School of Transportation, Jilin University, Changchun 130022, China
Dexin Yu: Navigation College, Jimei University, Xiamen 361021, China
Xiaoyu Zheng: BIT—Barcelona Innovative Transportation Research, Civil Engineering School, UPC–Barcelona Tech, 08034 Barcelona, Spain
Fanyun Meng: School of Transportation, Jilin University, Changchun 130022, China
Xincheng Wu: Navigation College, Jimei University, Xiamen 361021, China

Sustainability, 2025, vol. 17, issue 3, 1-21

Abstract: Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R 2 compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R 2 values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability.

Keywords: shared bike; demand prediction; sustainable transportation; model–data dual-driven (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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