城市交通路网动态短时推理与精准预测研究

doi:10.11959/j.issn.2096-6652.202233

Abstract

Abstract:

The structure of urban traffic network has a significant impact on the formation and spatio-temporal pattern propagation of traffic congestions.However, in studies based on traditional traffic models or deep learning models, the generation of traffic mode can only be described indirectly by traffic indicators, without considering the traffic network feature.This makes it very difficult to accurately describe the propagation dynamics both in temporal and spatial dimensions and lacks specificity.To tackle the above-mentioned problems, a novel traffic state prediction approach based on traffic pattern reasoning (TP²) framework was proposed.The framework modeled congestion propagation as a dynamically evolving temporal knowledge graph (TKG), and applied an inferencing framework (TPP-TKG) that was based on a novel aggregator called RGraAN.TPP-TKG captured the spatial-temporal propagation pattern of traffic congestion, and combined related road links to a given link, and constructed correlated sub region of the traffic network.Then a traffic state predicting based on graph neural network was employed to predict short-term speed evolution of road links in this sub region.Comparing to the state-of-the-art benchmark models, TP² achieves 1% ~ 2% higher accuracy.

Key words: traffic network structure, propagation mode, traffic mode reasoning and prediction, temporal knowledge graph, graph neural network.

CLC Number:

U495

Yuanqi QIN, Qingyuan JI, Jun GE, et al. Short-term traffic state reasoning and precise prediction in urban networks[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 380-395.

Figures/Tables 18

区域	类别	模型	早高峰				晚高峰
区域	类别	模型	Hit@1	Hit@3	Hit@10	MRR	Hit@1	Hit@3	Hit@10	MRR
区域1	非知识图	HA	13.2				11.6
	谱模型	ARIMA	18.6				16.4
		ST-GCN	19.6				17.5
		AST-GCN	19.4				20.3
	知识图谱	T-TransE	28.0	55.3	69.3	35.1	25.1	39.7	58.2	32.2
	模型	TA-TransE	35.6	52.7	76.7	48.9	23.9	62.1	75	38.6
		TA-DistMult	57.1	61.8	83.1	57.5	41.0	65.1	72.8	52.8
		RE-NET	64.3	71.5	85.8	72.1	51.2	75.6	81.9	58.7
		TP²	$66 . 1$	$72 . 9$	$87 . 2$	$74 . 3$	$52 . 9$	$77 . 3$	$83 . 6$	$60 . 2$
区域2	非知识图	HA	11.2				14.2
	谱模型	ARIMA	14.5				16.6
		ST-GCN	16.7				18.5
		AST-GCN	22.7				21.1
	知识图谱	T-TransE	32.5	32.5	47.8	24.3	27.2	45.7	66.2	33.4
	模型	TA-TransE	29.6	47.7	51.3	41.3	28.8	53.3	69.1	48.9
		TA-DistMult	42.5	58.4	73.5	52.8	43.0	57.9	76.3	57.9
		RE-NET	57.1	68.7	78.4	62.4	56.5	70.3	84.8	69.6
		TP²	$58 . 9$	$68 . 5$	$80 . 1$	$65 . 3$	$57 . 7$	$74 . 4$	$86 . 7$	$71 . 8$

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路段ID	时间戳	速度/（km·h^-1）	信号周期/s
R0001	2020-01-01 08:00:00	35	180
R0001	2020-01-01 08:05:00	32	180
R0001	2020-01-01 08:10:00	28	135

拥堵率	0＜v＜10	10＜v＜20	20＜v＜40
0＜cr＜0.2	1	6	11
0.2＜cr＜0.4	2	7	12
0.4＜cr＜0.6	3	8	13
0.6＜cr＜0.8	4	9	14
0.8＜cr＜1.0	5	10	15

交通传播模式关系	具体解释
p₀	cr相同，v相同
p₁	cr相同，v更高
p₂	cr更低，v更高
p₃	cr更低，v相同
p₄	cr更低，v更低
p₅	cr相同，v更低
p₆	cr更高，v更低
p₇	cr更高，v相同
p₈	cr更高，v更高

基准模型	基准模型最佳超参
ST-GCN	ST-Conv的通道数分别为64、16、64。图卷积核数量K与时间卷积核数量K_t均设为3
AST-GCN	所有的图卷积层和时间卷积层使用了64个卷积核
T-TransE	负采样率为1，间隔为1
TA-TransE	负采样率为1，间隔为1
TA-DistMult	使用交叉熵损失，负采样率为1，间隔为1
RE-NET	RGCN聚合函数层数为2，使用两个块对角矩阵，隐藏向量维度为200

基准模型	基准模型最佳超参
SVR	使用线性核函数，惩罚因子为0.001
GRU	使用单层GRU网络，隐藏向量维度为64
GCN	使用双层GCN，隐藏向量维度为64
T-GCN	隐藏向量维度为64
ST-GCN	ST-Conv的通道数分别为64、16、64；图卷积核数量K与时间卷积核数量K_t均设为3
A3T-GCN	隐藏向量维度为64

Short-term traffic state reasoning and precise prediction in urban networks

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拥堵频率	时间窗口5 min	时间窗口10 min	时间窗口20 min
60%	67.3/55.4/58.2/66.5	70.3/58.5/64.1/69.2	70.9/57.2/61.5/68.5
80%	71.7/59.2/62.1/69.8	74.3/60.2/65.3/71.8	72.1/59.3/63.5/70.2
90%	70.4/57.2/59.3/67.5	72.1/59.8/63.7/69.7	71.2/58.1/62.5/67.7

模型	MAE	RMSE	MAPE
HA	7.61/9.14/12.0	8.87/11.4/16.5	21.8%/25.7%/33.9%
SVR	7.32/8.72/11.6	8.63/10.5/14.3	16.5%/19.8%/23.5%
ARIMA	5.18/6.79/9.27	6.02/7.11/9.74	14.8%/17.5%/20.1%
GCN	6.46/7.78/9.19	6.94/8.54/9.81	11.6%/13.2%/15.4%
GRU	5.34/5.87/7.27	6.48/6.88/8.53	11.2%/12.4%/14.0%
T-GCN	4.42/4.86/5.62	5.60/5.91/6.52	10.7%/11.5%/12.8%
ST-GCN	3.88/4.41/4.99	5.27/5.45/5.86	9.5%/9.8%/10.9%
A3T-GCN	3.65/3.89/4.24	4.62/4.93/5.34	9.2%/9.6%/10.5%
TP²	2.99/3.21/3.39	4.05/4.40/4.59	8.6%/8.9%/9.3%

模型	训练时间/s	推理时间/s
A3T-GCN	8 532	115
TP²	2 480	26