时空信息增强图形卷积网络:用于乘车需求预测的深度学习框架
Zhenglong Tang1, Chao Chen1,2
1College of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
这项研究引入了一个时空图形卷积网络,用于乘车需求预测. 该模型准确地纳入了外部因素,提高了预测准确度,并证明了城市移动服务的稳定性.
科学领域:
- 城市规划和交通科学.
- 人工智能和机器学习.
- 地理空间数据分析.
背景情况:
- 准确的乘车需求预测对于优化城市交通系统至关重要,包括车辆调度和交通管理.
- 现有的方法难以有效地整合外部时空因素,导致预测不准确.
- 诸如天气和事件等外部因素对叫车需求的影响是显著的,但往往被低估.
研究的目的:
- 开发一种先进的需求预测模型,通过有效地结合外部时空影响来克服以前方法的局限性.
- 为了提高乘车需求预测的准确性和及时性.
- 提高模型捕捉复杂的时空依赖性的能力.
主要方法:
- 提出了一个新的时空信息增强图形卷积网络 (STE-GCN) 用于乘车需求预测.
- 利用相关性分析来提取和编码关键的外部时空因素到区域特定的特征单元.
- 采用封闭的循环单位 (GRU) 和图形卷积网络 (GCN) 来建模需求与外部因素之间的时空依赖.
主要成果:
- 拟议的STE-GCN模型在整合外部时空因素时,与基线模型相比,显示出更高的预测性能.
- 该模型在不同的实验领域表现出一致的准确性,表明了强度.
- 结合外部因素显著提高了模型的预测准确性和对现实世界的影响的感知能力.
结论:
- 外部时空因素对于提高乘车需求预测模型的性能至关重要.
- 开发的STE-GCN模型强大,性能优异,突出了其在智能运输系统中的广泛应用潜力.
- 这项研究为优化乘车服务和缓解城市交通拥堵提供了有价值的工具.
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