Traffic prediction and road space optimization for the integration of dockless bike-sharing and subway

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2025-01-30 DOI:10.1016/j.scs.2025.106162

Ganmin Yin , Chen Fu , Shuliang Ren , Xiaoqin Yan , Junnan Qi , Yi Bao , Zhou Huang

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引用次数: 0

Abstract

The integration of dockless bike-sharing (DBS) and subway is an effective measure to promote sustainable urban transportation. However, inaccurate traffic prediction and unreasonable road space allocation have brought a severe imbalance between supply and demand, significantly restricting its application. To address these issues, this study first employs machine learning to establish a traffic prediction model at the origin–destination level. Then, we propose a road space optimization method based on multi-source geospatial big data, aiming to compress motorized lanes and increase cycling space. Results from the Beijing case indicate: (1) The XGBoost model achieves the best prediction accuracy, with an R² of 0.68 ± 0.04. (2) The optimization method can accurately identify high-priority areas, and compressing each motorized lane only by 0–0.41 m can achieve reasonable allocation and still meet official standards. This study will assist policymakers in identifying demand and adjusting infrastructure within the DBS-subway integration scenario, ultimately achieving sustainable transportation systems.

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;