从使用 GraphResLSTM 模型的道路平均速度数据来预测出发目的地
1Capital University of Economics and Business, Beijing, China.
PeerJ. Computer science
|March 10, 2025
概括
本研究引入了GraphResLSTM,用于智能交通系统中准确的原点-目的地 (OD) 预测. 使用公路平均速度数据,它显著优于传统方法.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 交通管理 交通管理
- 数据科学数据科学数据科学
背景情况:
- 准确的来源-目的地 (OD) 预测对于ITS中的交通管理和资源分配至关重要.
- 传统方法通常依赖于交通流量数据,这些数据可能无法有效地捕捉拥堵细微差别.
研究的目的:
- 介绍一个新的综合框架,GraphResLSTM,用于增强的OD预测.
- 为了评估道路平均速度数据与传统交通流量数据对OD预测的有效性.
主要方法:
- 通过集成图形卷积网络 (GCN),残余神经网络 (ResNet) 和长短期记忆 (LSTM) 开发了GraphResLSTM.
- 利用道路平均速度数据,在现实道路网络上使用城市移动模拟 (SUMO) 模拟.
- 采用混合权方法和以类似于理想解决方案的订单偏好技术 (TOPSIS) 进行关键道路段的选择,以提高培训效率.
主要成果:
- 在OD预测中,GraphResLSTM模型表现出卓越的性能.
- 道路平均速度数据被证明比传统的交通流量数据更有效,用于OD预测.
- 对比实验证实了模型和数据类型在替代方案上的优势.
结论:
- 在ITS.内,GraphResLSTM框架在OD预测准确度方面取得了重大进展.
- 与流量数据相比,道路平均速度数据为交通预测提供了更丰富的洞察力.
- 提出的方法提高了预测准确性和培训效率.
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