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

delete2025-12-20
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PRE
AI
J
Juntao Wu *
J
Jiahui Feng
J
Jie Fang
H
Hefu Liu
DOI：10.1016/j.cie.2025.111775delete
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Abstract

Abstract

En 中文
• 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.

Journal

Computers and Industrial Engineering cover
Computers and Industrial Engineering
IF:
6.5
Papers:
1.0W
Citations:
3.8W

Organization

U
University of Nottingham
Scholars:
3.4W
Papers: 3.2W
Citations: 5.5W
U
university of science and technology of china
Scholars:
1.0W
Papers: 3.9K
Citations: 3
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