Demand prediction for bike-sharing systems: A spatial and semantic modeling approach for enhanced accuracy and operational efficiency

2025-12-20

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Juntao Wu *

Jiahui Feng

Jie Fang

Hefu Liu

DOI：10.1016/j.cie.2025.111775

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• Proposed a novel model, SSGAN, for accurate bike-sharing demand forecasting. • Enhanced prediction by capturing long-range semantic dependencies between stations. • Outperformed benchmark models on real-world datasets. • Significantly reduced operational costs, offering practical value to bike-sharing operators.

Demand prediction for bike-sharing systems: A spatial and semantic modeling approach for enhanced accuracy and operational efficiency

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