在包含STAGCN的地铁站周围共享自行车的需求预测
Xue Xing1, Le Wan1, Fahui Luo1
1School of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.
PloS one
|July 15, 2025
概括
在地铁站附近准确的共享自行车需求预测对于环保的城市旅行至关重要. 一个新的模型整合了时空注意力图卷积网络 (STAGCN) 和长短期记忆 (LSTM) 显著提高了预测准确性.
科学领域:
- 城市规划和交通科学 城市规划和交通科学
- 数据科学和机器学习
- 环境可持续性 环境可持续性
背景情况:
- 共享自行车系统对于可持续的城市流动性至关重要,但在地铁站附近面临供需失衡的挑战.
- 准确预测共享自行车需求,特别是与地铁使用相关的旅行,对于高效的资源配置至关重要.
- 现有的方法在共享自行车使用数据中难以捕捉复杂的时空依赖.
研究的目的:
- 开发和评估一种新的共享自行车需求预测模型,专门用于地铁连接的旅行.
- 提高在地铁站附近地区共享自行车需求预测的准确性.
- 增强共享自行车资源分配的优化,并改善用户体验.
主要方法:
- 开发了一种方法来识别与地铁使用相关的共享自行车旅行,过非连接记录.
- 设计了一个预测模型,集成了时空注意力图卷积网络 (STAGCN),长短期记忆 (LSTM) 和Informer.
- STAGCN捕获了空间相关性,LSTM学习了时间依赖性,多层感知器生成了最终的预测.
主要成果:
- 拟议的模型在深的共享自行车和地铁数据集上实现了0.893的确定系数 (R2).
- 预测准确度超过了基线模型的6.7%.
- 与传统的时间序列方法相比,该模型显示了较低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
结论:
- 开发的需求预测模型有效地预测了地铁连接的共享自行车需求.
- 该模型的卓越性能有助于运营商优化共享自行车资源配置.
- 这对改善用户体验和促进绿色城市旅行做出了重大贡献.
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