动态多图形时空同步聚合框架用于智能交通系统中的交通预测
Xian Yu1,2, Yinxin Bao1, Quan Shi1,3
1School of Information Science and Technology, Nantong University, Nantong, Jiangsu, China.
PeerJ. Computer science
|March 4, 2024
概括
准确的交通预测对于智能交通系统 (ITS) 至关重要. 一个新的动态多图框架 (DMSTSAF) 通过结合外部因素和多图视角来改善交通预测.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 交通预测 交通预测
- 图形神经网络 图形神经网络
背景情况:
- 准确的交通预测对于优化智能交通系统 (ITS) 和道路网络效率至关重要.
- 现有的方法难以建模复杂的时空相关性,尤其是在结合外部因素和多样化的图形结构时.
研究的目的:
- 提出一个新的框架,即动态多图形时空同步聚合框架 (DMSTSAF),用于增强交通预测.
- 解决现有模型在外部因素整合和多视角图形构建方面的局限性.
主要方法:
- DMSTSAF使用功能增强模块 (FAM) 来将流量数据与外部因素合并.
- 该框架使用多种空间和时间图形,并设计同步聚合模块,同时从多个角度提取特征.
主要成果:
- DMSTSAF在交通预测准确度方面取得了显著的改进.
- 该模型在四个现实数据集上实现了3.68-8.54%的性能增长,高于最先进的基线.
结论:
- 拟议的DMSTSAF有效地模拟了交通数据中的时空相关性.
- 该框架能够纳入外部因素并利用多个图形视角,从而带来优越的交通预测性能.
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