基于图时空模式学习网络的路网实时交通事件自动检测方法

doi:10.13229/j.cnki.jdxbgxb.20230875

Abstract

Abstract:

In order to improve the detection accuracy of road network traffic incidents and reduce the false alarm rate， a real-time automatic detection method of road network traffic incidents based on Graph Spatial-Temporal Pattern Learning Network （GSTPL） is proposed. Firstly， the traffic incident detection problem in the road network is abstracted into a graph structure anomaly detection task； a traffic spatial-temporal fusion graph representation method is designed to filter the road network graph node with strong spatial-temporal dependence and same pattern regularity as the input. Then， the graph spatial-temporal convolution and graph embedding layer are introduced to extract the spatial-temporal pattern features， and the multi-component input and fusion prediction structure are constructed to fuse traffic pattern rules in different time dimensions， and realize stable forecasts of graph node parameters. An abnormal state evaluation method is designed， and the final incident detection result is given by learning of the prediction error distribution and combining with the current detection data. Two real road networks datasets were used for validation experiments， and the proposed algorithm was compared with several typical traffic incident detection algorithms. The comparison results show that the proposed GSTPL has higher detection accuracy， lower false alarm rate and shorter average detection time. When the acceptable false positive rate is 5% and 10%， the detection rate of traffic incidents can reach more than 91% and 96% respectively.

Key words: engineering of communications and transportation system, automatic incident detection, graph neural network, pattern learning, abnormal evaluation

CLC Number:

U458

Shu-shan CHAI,Zhi-qiang ZHOU,Hai-tao LI,Jiong-yang XU. Real-time road network traffic anomaly incident detection based on graph spatial-temporal pattern learning network[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(7): 2145-2161.

Figures/Tables 13

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Table 1

Major parameters configurations of GSTPL model"

模型参数	设置值或参数类型
最大训练次数	20 000
多组件预测训练阶段学习率	0.001
组件权重联调阶段学习率	0.000 1
网络优化器	Adam
长范围滑动窗口 $L w$	3 000
短范围滑动窗口 $L s$	6
式（11）与式（12）中高斯核带宽 $h$	0.2
式（16）中调整系数 $γ$	0.8
异常状态可能性判定阈值 $ε$	0.3

Table 1

Fig.7

Fig.8

Table 2

Table 3

Table 4

Table 5

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评价指标	美国西雅图数据集
模型	SND	MAD	多元时空动态阈值	贝叶斯分类	支持向量机	随机森林	卡尔曼滤波	STGAN	LSTM+ 异常评估	ASTGAT+ 异常评估	MSASGCN+异常评估	TCA+ 异常评估	GSTPL
检测率（误检率 5%）/%	78.21	80.52	82.79	86.92	84.68	79.25	89.26	89.61	91.34	90.78	91.62	93.15	94.52
检测率（误检率 10%）/%	84.75	87.32	88.43	89.72	86.92	86.25	92.56	94.59	95.32	94.62	95.78	98.61	98.71
误检率（检测率 90%）/%	28.76	25.93	24.81	18.85	20.15	27.74	8.51	7.06	4.40	6.17	34.832	3.52	3.12
误检率（检测率 95%）/%	45.92	43.64	40.25	33.21	39.08	44.52	12.23	10.58	9.12	9.73	9.26	6.72	6.09
平均检测时间/min	3.62	3.58	3.51	8.59	5.36	5.82	4.05	4.52	3.69	3.82	3.72	3.88	3.92
AUC	0.904 3	0.912 4	0.918 2	0.947 6	0.942 5	0.923 5	0.954	0.966 3	0.978 1	0.973 4	0.979 2	0.983 2	0.987 6
评价指标	国内某市数据集
模型	SND	MAD	多元时空动态阈值	贝叶斯分类	支持向量机	随机森林	卡尔曼滤波	STGAN	LSTM+ 异常评估	ASTGAT+ 异常评估	MSASGCN+异常评估	TCA+ 异常评估	GSTPL
检测率（误检率5%）/%	75.63	78.52	79.53	81.57	80.26	77.65	86.12	87.52	89.62	87.96	88.12	89.92	91.43
检测率（误检率10%）/%	81.45	82.46	84.43	87.46	85.17	83.14	89.35	91.92	94.68	93.42	93.62	95.03	96.54
误检率（检测率90%）/%	20.56	19.76	17.52	15.92	17.35	15.93	11.35	8.92	6.15	7.47	68.14	5.03	4.22
误检率（检测率95%）/%	41.62	37.92	38.72	31.54	34.17	29.25	17.62	12.59	10.25	11.38	10.75	9.83	8.23
平均检测时间/min	6.38	5.78	5.12	8.92	7.78	7.52	6.38	6.74	5.53	5.73	5.61	5.42	5.24
AUC	0.853 6	0.861 7	0.8732	0.902 5	0.886 4	0.879 6	0.931 4	0.943 2	0.949 2	0.943 7	0.945 8	0.946 2	0.970 7

评价指标	美国西雅图数据集			国内某市数据集
评价指标	空间图+GSTPL	时间图+GSTPL	时空融合图+GSTPL	空间图+GSTPL	时间图+GSTPL	时空融合图+GSTPL
检测率（误检率5%）/%	92.16	93.72	94.52	89.51	90.17	91.43
检测率（误检率10%）/%	96.43	98.32	98.71	93.54	95.26	96.54
误检率（检测率90%）/%	4.92	4.31	3.12	6.91	4.92	4.22
误检率（检测率95%）/%	8.52	7.16	6.09	10.31	9.73	8.23
平均检测时间/min	3.82	3.96	3.92	5.53	5.42	5.24
AUC	0.965 4	0.980 2	0.987 6	0.941	0.953 6	0.970 7

评价指标	美国西雅图数据集						国内某市数据集
评价指标	近期输入	日周期输入	周周期输入	近期+日周期输入	近期+周周期输入	本文方法	近期输入	日周期输入	周周期输入	近期+日周期输入	近期+周周期输入	本文方法
检测率（误检率5%）/%	92.56	91.28	89.18	93.47	92.35	94.52	89.63	87.12	86.51	90.15	89.94	91.43
检测率（误检率10%）/%	95.76	95.32	94.42	96.54	95.72	98.71	94.18	93.27	92.34	95.13	94.36	96.54
误检率（检测率90%）/%	4.95	4.92	6.62	4.38	4.76	3.12	6.94	8.82	9.32	4.92	6.35	4.22
误检率（检测率95%）/%	9.42	9.87	10.16	8.61	9.63	6.09	10.31	11.25	12.63	9.28	10.18	8.23
平均检测时间/min	3.61	4.37	4.53	4.01	3.84	3.92	5.04	5.73	5.76	5.62	5.13	5.24
AUC	0.961 3	0.951 6	0.943 2	0.976 1	0.963 2	0.987 6	0.950 3	0.948 1	0.942 6	0.952 5	0.962 3	0.970 7

评价指标	美国西雅图数据集			国内某市数据集
评价指标	GSTPL+ 固定阈值	GSTPL+ 动态阈值	本文方法	GSTPL+ 固定阈值	GSTPL+ 动态阈值	本文方法
检测率（误检率5%）/%	84.76	90.31	94.52	85.72	88.49	91.43
检测率（误检率10%）/%	92.15	94.67	98.71	89.67	93.84	96.54
误检率（检测率90%）/%	8.92	4.98	3.12	10.43	8.92	4.22
误检率（检测率95%）/%	14.67	10.37	6.09	13.62	11.36	8.23
平均检测时间/min	3.79	3.83	3.92	5.16 min	5.49 min	5.24 min
AUC	0.951 1	0.965 7	0.987 6	0.941 3	0.962 4	0.970 7

Real-time road network traffic anomaly incident detection based on graph spatial-temporal pattern learning network

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