整合空间时间网络因素，提升电动滑板车需求预测准确率27%至49%
A Comprehensive Machine Learning Framework for Micromobility Demand Prediction
- 融合空间、时间与网络依赖关系构建预测框架
- 相比基线模型准确率提升27%到49%
- 适合城市交通规划与共享出行企业参考
无桩电动滑板车作为关键的微出行服务，已成为环保且灵活的城市交通选择，改善首末公里连接，减少拥堵与排放，补充短途公共交通。然而，有效管理依赖于精准的需求预测，对车队调度与基础设施规划至关重要。以往研究多孤立分析空间或时间因素，本文提出整合空间、时间与网络依赖性的机器学习框架，显著提升微出行需求预测精度。实验表明，该框架相比基线模型准确率提升27%至49%，更好地捕捉城市微出行使用模式。研究结果支持数据驱动的微出行管理，助力优化车队分布、降低成本并推动可持续城市规划。
原文摘要 · Abstract (English)
Dockless e-scooters, a key micromobility service, have emerged as eco-friendly and flexible urban transport alternatives. These services improve first and last-mile connectivity, reduce congestion and emissions, and complement public transport for short-distance travel. However, effective management of these services depends on accurate demand prediction, which is crucial for optimal fleet distribution and infrastructure planning. While previous studies have focused on analyzing spatial or temporal factors in isolation, this study introduces a framework that integrates spatial, temporal, and network dependencies for improved micromobility demand forecasting. This integration enhances accuracy while providing deeper insights into urban micromobility usage patterns. Our framework improves demand prediction accuracy by 27 to 49% over baseline models, demonstrating its effectiveness in capturing micromobility demand patterns. These findings support data-driven micromobility management, enabling optimized fleet distribution, cost reduction, and sustainable urban planning.
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