停车场交通预测基于多面空间-时间特征的融合
Lechuan Zhang1, Bin Wang1, Qian Zhang1
1College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201400, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
准确的停车场交通预测对于管理城市拥堵至关重要. 本研究引入了一种新型模型,该模型有效地将复杂的空间和时间数据与外部因素集成在一起,以改进流量预测.
科学领域:
- 城市规划和智能交通系统.
- 数据科学和机器学习应用.
- 时间空间数据分析.
背景情况:
- 快速的城市化和车辆数量的增加加剧了交通拥堵.
- 有效的停车指导和信息系统 (PGI) 对城市流动至关重要.
- 准确的停车交通预测对于提高效率和减少拥堵至关重要.
研究的目的:
- 解决模拟复杂的时空停车数据和外部影响的挑战.
- 提出一种用于预测停车场交通流量的新型模型.
- 提高停车交通预测的准确性和有效性.
主要方法:
- 开发了一个多面的时空特征融合图形卷积网络 (MFF-STGCN) 模型.
- 集成的外部功能,如假日,景点 (POI) 和天气使用功能嵌入模块.
- 采用时空注意力机制和图形卷积与封闭的递归单元用于特征提取.
主要成果:
- 多年框架-STGCN模型有效地捕捉了复杂的空间关系和时间依赖.
- 该模型准确地纳入了影响停车交通的外部因素.
- 在真实世界数据集上的实验结果证实了增强的预测性能.
结论:
- 拟议的MFF-STGCN模型为停车场交通预测提供了一个强大的解决方案.
- 准确的交通流量预测可以显著缓解城市交通拥堵.
- 这种方法为智能运输系统的开发提供了宝贵的工具.
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