图神经网络驱动的交通预测技术：探索与挑战

doi:10.11959/j.issn.2096-3750.2021.00235

Abstract

Abstract:

With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.

Key words: traffic prediction, graph neural networks, spatial-temporal correlation, synchronous convolution, graph at-tention networks

CLC Number:

TP391

Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN. Graph neural network driven traffic prediction technology:review and challenge[J]. Chinese Journal on Internet of Things, 2021, 5(4): 1-16.

Figures/Tables 15

时间	模型	MAE	RMSE	MAPE
15 min	HA	2.88	5.59	6.76%
	VAR	1.74	3.09	3.59%
	ARIMA	1.62	3.30	3.50%
	FC-LSTM	2.05	4.19	4.80%
	DCRNN	1.31	2.76	2.73%
	Graph WaveNet	$1 . 30$	$2 . 74$	$2 . 73 %$
30 min	HA	2.88	5.59	6.76%
	VAR	2.33	4.15	5.02%
	ARIMA	2.33	4.76	5.40%
	FC-LSTM	2.20	4.55	5.20%
	DCRNN	1.65	3.77	3.72%
	Graph WaveNet	$1 . 63$	$3 . 70$	$3 . 67 %$
60 min	HA	2.88	5.59	6.76%
	VAR	2.92	5.11	6.46%
	ARIMA	3.38	6.50	8.30%
	FC-LSTM	2.37	4.96	5.70%
	DCRNN	1.97	4.62	4.71%
	Graph WaveNet	$1 . 95$	$4 . 52$	$4 . 63 %$

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方法		优点	缺点
模型驱动		主要研究流量、速度和密度之间的瞬时和稳态关系，主要依赖先验知识进行系统建模	模型固定，难以准确地模拟变化多端的真实交通状况
数据驱动	统计分析模型	算法和模型简易，实现方便	模型建立在时间序列数据平稳的假设前提下
	机器学习模型	可以提取与交通相关的特征，用于非线性数据的模型构建	依赖于人工提取的交通特征，模型架构浅易、参数有限且计算效率较低
	深度学习模型	能够让计算机自动学习路网特征，减少对人工提取特征的依赖性	只考虑路网空间或时间特征
	图神经网络+深度学习	从时间和空间两个角度提取路网特征	学习时间相关性效率不高，长期预测精度有待进一步提高

文献	空间模型	时间模型	数据集
[5]	GCN	GRU	SZ-taxi；Los-loop
[28]	GCN	LSTM	出租车的GPS数据（上海TIC）
[23]	GCN	标准二维卷积	PeMSD4；PeMSD8
[20]	GCN	门控CNN	BJER4；PeMSD7
[29]	GCN	DCC	PeMSD4；PeMSD7
[30]	GCN	门控TCN	METR-LA；PEMS-BAY
[31]	考虑高阶邻域的GCN	LSTM	LOOP；INRIX
[32]	GCN	GRU+注意力	A-map
[19]	扩散卷积网络	GRU	METR-LA；PEMS-BAY
[21]	基于优化图矩阵的GCN	GRU	D.C.；Philadelphia；PeMSD4
[22]	GAT	GRU	METR-LA
[24]	GCN+注意力	标准二维卷积+注意力	PeMSD4；PeMSD8
[26]	注意力	LSTM+注意力	PeMSD4；PeMSD8
[33]	GAT	LSTM	PeMSD7
[34]	注意力	注意力	Xiamen；PeMS
[35]	时空同步卷积	时空同步卷积	PEMS03；PEMS04；PEMS07；PEMS08
[36]	注意力	注意力	METR-LA；PEMS-BAY

Graph neural network driven traffic prediction technology:review and challenge

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