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基于检查点数据的GCN-GRU车辆轨迹和交通流量预测

Deyong Guan¹, Na Ren¹, Ke Wang²

¹College of Transportation, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific reports

|December 5, 2024

概括

本研究引入了一种使用交通检查点数据和图形卷积神经网络门循环单元 (GCN-GRU) 模型进行准确的城市交通流量预测的新方法. GCN-GRU模型显著提高了轨迹预测的准确性,从而实现了更可靠的流量预测.

科学领域:

城市规划和交通管理.
数据科学和人工智能数据科学和人工智能
运输工程运输工程运输工程

背景情况:

大规模的交通数据需要对城市道路网络进行先进的短期交通分析.
准确的车辆轨迹和交通流量预测对于交通管理至关重要,包括路径规划和拥堵警告.
现有的方法通常依赖于GPS数据,其覆盖范围和数据重量有局限性.

研究的目的:

通过使用交通检查站数据,提出一种用于准确预测城市交通流量的新方法.
为了利用检查点数据的广泛覆盖,高可靠性和较轻的重量来进行交通分析.
提高车辆轨迹预测的准确性,以提高交通流量预测.

主要方法:

使用检查点数据驱动的方法,将数据转换为时间序列车辆轨迹.
使用图形卷积神经网络 (GCN) 来从检查点数据和空间关系中学习空间特征.
一个门式循环单位 (GRU) 与GCN集成,以捕捉时空相关性,形成用于轨迹预测的GCN-GRU模型.
使用GCN-GRU轨迹预测模型的输出,预测了流量.

主要成果:

与单个模型 (GCN,GRU,BiGRU,BiLSTM) 相比,GCN-GRU模型在车辆轨迹预测方面表现优越.
GCN-GRU模型实现了平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 的显著降低.
使用GCN-GRU模型预测的流量显示平均绝对百分比误差 (MAPE) 为0.18,证实了其可靠性.

结论:

拟议的GCN-GRU模型有效地从检查点数据中提取时空特征,以准确预测车辆轨迹.
该方法为城市道路网络中短期交通流量预测提供了可靠的方法.
使用高级深度学习模型的交通检查站数据,为基于GPS的交通分析提供了一个有前途的替代方案.

关键词:

检查点数据检查点数据门式经常性单位门式经常性单位图表卷积神经网络的图表预测交通流量预测预测车辆轨迹的预测

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