Port traffic flow prediction based on knowledge graph and spatio-temporal diffusion graph convolutional network

doi:10.11772/j.issn.1001-9081.2023081100

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2952-2957.DOI: 10.11772/j.issn.1001-9081.2023081100

• Frontier and comprehensive applications • Previous Articles Next Articles

Port traffic flow prediction based on knowledge graph and spatio-temporal diffusion graph convolutional network

Guixiang XUE¹(), Hui WANG², Weifeng ZHOU³, Yu LIU³, Yan LI⁴

^1.School of Civil and Transportation Engineering，Hebei University of Technology，Tianjin 300401，China
^2.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^3.Intelligent Transportation Monitoring Center of Tianjin，Tianjin 300250，China
^4.Tianjin Port Information Technology Development Company Limited，Tianjin 300461，China

Received:2023-08-15 Revised:2023-11-01 Accepted:2023-11-16 Online:2023-12-18 Published:2024-09-10
Contact: Guixiang XUE
About author:WANG Hui， born in 1999， M. S. candidate. His research interests include intelligent transport system.
ZHOU Weifeng， born in 1969， Ph. D.， professor of engineering. His research interests include intelligent transport system.
LIU Yu， born in 1975， M. S.， senior engineer. His research interests include intelligent transport system.
LI Yan， born in 1980， M. S.， senior engineer. His research interests include intelligent port.
Supported by:
National Natural Science Foundation of China(52208240);Tianjin Science and Technology Plan Project(23ZGCXQY00030)

基于知识图谱和时空扩散图卷积网络的港口交通流量预测

薛桂香¹(), 王辉², 周卫峰³, 刘瑜³, 李岩⁴

^1.河北工业大学土木与交通学院，天津 300401
^2.河北工业大学人工智能与数据科学学院，天津 300401
^3.天津市智能交通运行监测中心，天津 300250
^4.天津港信息技术发展有限公司，天津 300461

通讯作者: 薛桂香
作者简介:王辉（1999—），男，河北邯郸人，硕士研究生，主要研究方向：智能交通系统
周卫峰（1969—），男，陕西韩城人，教授级高级工程师，博士，主要研究方向：智能交通系统
刘瑜（1975—），男，浙江舟山人，高级工程师，硕士，主要研究方向：智能交通系统
李岩（1980—），男，天津人，高级工程师，硕士，主要研究方向：智慧港口。
基金资助:
国家自然科学基金资助项目(52208240);天津市科技计划项目(23ZGCXQY00030)

Abstract

Abstract:

Accurate prediction of port traffic flow is a challenging task due to its stochastic uncertainty and time-unsteady characteristics. In order to improve the accuracy of port traffic flow prediction， a port traffic flow prediction model based on knowledge graph and spatio-temporal diffusion graph convolution network， named KG-DGCN-GRU， was proposed， taking into account the external disturbances such as meteorological conditions and the opening and closing status of the port-adjacent highway. The factors related to the port traffic network were represented by the knowledge graph， and the semantic information of various external factors were learned from the port knowledge graph by using the knowledge representation method， and Diffusion Graph Convolutional Network （DGCN） and Gated Recurrent Unit （GRU） were used to effectively extract the spatio-temporal dependency features of the port traffic flow. The experimental results based on the Tianjin Port traffic dataset show that KG-DGCN-GRU can effectively improve the prediction accuracy through knowledge graph and diffusion graph convolutional network， the Root Mean Squared Error （RMSE） is reduced by 4.85% and 7.04% and the Mean Absolute Error （MAE） is reduced by 5.80% and 8.17%， compared with Temporal Graph Convolutional Network （T-GCN） and Diffusion Convolutional Recurrent Neural Network （DCRNN） under single step prediction （15 min）.

Key words: port traffic flow prediction, knowledge graph, spatio-temporal dependency, Gated Recurrent Unit (GRU), Graph Convolutional Network (GCN)

摘要：

由于港口交通流量具有随机不确定性、时间不平稳特征，因此港口交通流量的精准预测是一项具有挑战性的任务。为了提高港口交通流量预测精度，考虑气象条件和港口相邻高速公路开闭状态等外部干扰因素，提出了一种基于知识图谱和时空扩散图卷积网络的港口交通流量预测算法KG-DGCN-GRU。知识图谱表示港口交通网络相关因素，知识表示方法从港口知识图谱中学习各外部因素的语义信息，扩散图卷积网络（DGCN）和门控循环单元（GRU）能有效挖掘港口交通流量的时空依赖特征。基于天津港交通数据集的实验结果表明，KG-DGCN-GRU能通过知识图谱和扩散图卷积有效提高预测精度，在单步预测（15 min）中与时间图卷积网络（T-GCN）和扩散卷积递归神经网络（DCRNN）相比，均方根误差（RMSE）分别降低了4.85%和7.04%，平均绝对误差（MAE）分别降低了5.80%和8.17%。

关键词: 港口交通流量预测, 知识图谱, 时空依赖, 门控循环单元, 图卷积网络

CLC Number:

TP181

Guixiang XUE, Hui WANG, Weifeng ZHOU, Yu LIU, Yan LI. Port traffic flow prediction based on knowledge graph and spatio-temporal diffusion graph convolutional network[J]. Journal of Computer Applications, 2024, 44(9): 2952-2957.

薛桂香, 王辉, 周卫峰, 刘瑜, 李岩. 基于知识图谱和时空扩散图卷积网络的港口交通流量预测[J]. 《计算机应用》唯一官方网站, 2024, 44(9): 2952-2957.

Figures/Tables 9

References 25

1	KALAMARAS I， ZAMICHOS A， SALAMANIS A， et al. An interactive visual analytics platform for smart intelligent transportation systems management ［J］. IEEE Transactions on Intelligent Transportation Systems， 2018， 19（2）： 487-496.
2	GASTEIGER J， WEIβENBERGER S， GÜNNEMANN S. Diffusion improves graph learning ［C］// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2019： 133666-13378.
3	ZHANG S， SONG Y， JIANG D， et al. Noise-identified Kalman filter for short-term traffic flow forecasting ［C］// Proceedings of the 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks. Piscataway： IEEE， 2019： 462-466.
4	YE Q， SZETO W Y， WONG S C. Short-term traffic speed forecasting based on data recorded at irregular intervals ［J］. IEEE Transactions on Intelligent Transportation Systems， 2012， 13（4）： 1727-1737.
5	CHANG H， LEE Y， YOON B， et al. Dynamic near-term traffic flow prediction： system-oriented approach based on past experiences ［J］. IET Intelligent Transport Systems， 2012， 6（3）： 292-305.
6	HU W， YAN L， LIU K， et al. A short-term traffic flow forecasting method based on the hybrid PSO-SVR ［J］. Neural Processing Letters， 2016， 43（1）： 155-172.
7	ZOU Z， PENG H， LIU L， et al. Deep convolutional mesh RNN for urban traffic passenger flows prediction ［C］// Proceedings of the 2018 IEEE Smart World， Ubiquitous Intelligence & Computing， Advanced & Trusted Computing， Scalable Computing & Communications， Cloud & Big Data Computing， Internet of People and Smart City Innovation. Piscataway： IEEE， 2018： 1305-1310.
8	MA X， TAO Z， WANG Y， et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data ［J］. Transportation Research Part C： Emerging Technologies， 2015， 54： 187-197.
9	FU R， ZHANG Z， LI L. Using LSTM and GRU neural network methods for traffic flow prediction ［C］// Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation. Piscataway： IEEE， 2016： 324-328.
10	YUAN H， LI G. A survey of traffic prediction： from spatio-temporal data to intelligent transportation ［J］. Data Science and Engineering， 2021， 6（1）： 63-85.
11	罗文慧，董宝田，王泽胜.基于CNN-SVR混合深度学习模型的短时交通流预测［J］.交通运输系统工程与信息，2017，17（5）：68-74.
	LUO W H， DONG B T， WANG Z S. Short-term traffic flow prediction based on CNN-SVR hybrid deep learning model ［J］. Journal of Transportation Systems Engineering and Information Technology， 2017， 17（5）： 68-74.
12	MA D， SHENG B， JIN S， et al. Short-term traffic flow forecasting by selecting appropriate predictions based on pattern matching ［J］. IEEE Access， 2018， 6： 75629-75638.
13	YU B， LEE Y， SOHN K. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a Graph Convolutional Neural network （GCN）［J］. Transportation Research Part C： Emerging Technologies， 2020， 114： 189-204.
14	冯宁，郭晟楠，宋超，等.面向交通流量预测的多组件时空图卷积网络［J］.软件学报，2019，30（3）：759-769.
	FENG N， GUO S N， SONG C， et al. Multi-component spatial-temporal graph convolution networks for traffic flow forecasting ［J］. Journal of Software， 2019， 30（3）： 759-769.
15	张永凯，武志昊，林友芳，等.面向交通流量预测的时空超关系图卷积网络［J］.计算机应用，2021，41（12）：3578-3584.
	ZHANG Y K， WU Z H， LIN Y F， et al. Spatio-temporal hyper-relationship graph convolutional network for traffic flow forecasting ［J］. Journal of Computer Applications， 2021， 41（12）： 3578-3584.
16	CAO Y， LIU D， YIN Q， et al. MSASGCN： multi-head self-attention spatiotemporal graph convolutional network for traffic flow forecasting ［J］. Journal of Advanced Transportation， 2022， 2022： 2811961.
17	KHALED A， ELSIR A M T， SHEN Y. TFGAN： traffic forecasting using generative adversarial network with multi-graph convolutional network ［J］. Knowledge-Based Systems， 2022， 249： 108990.
18	ZHANG D， KABUKA M R. Combining weather condition data to predict traffic flow： a GRU-based deep learning approach ［J］. IET Intelligent Transport Systems， 2018， 12（7）： 578-585.
19	ZHU J， WANG Q， TAO C. AST-GCN： attribute-augmented spatiotemporal graph convolutional network for traffic forecasting ［J］. IEEE Access， 2021， 9： 35973-35983.
20	BORDES A， USUNIER A， GARCIA-DURÁN A， et al. Translating embeddings for modeling multi-relational data ［C］// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2013： 2787-2795.
21	CASTRO-NETO M， Y-S JEONG， M-K JEONG， et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions ［J］. Expert Systems with Applications， 2009， 36（3）： 6164-6173.
22	CHUNG J， GULCEHRE C， CHO K H， et al. Empirical evaluation of gated recurrent neural networks on sequence modeling ［EB/OL］. （2014-12-11）［2023-08-14］. .
23	LI Y， YU R， SHAHABI C， et al. Diffusion convolutional recurrent neural network： data-driven traffic forecasting ［EB/OL］. ［2023-08-01］. .
24	ZHAO L， SONG Y， ZHANG C， et al. T-GCN： a temporal graph convolutional network for traffic prediction ［J］. IEEE Transactions on Intelligent Transportation Systems， 2020， 21（9）： 3848-3858.
25	BAI J， ZHU J， SONG Y， et al. A3T-GCN： attention temporal graph convolutional network for traffic forecasting ［J］. ISPRS International Journal of Geo-Information， 2021， 10（7）： 485.

模型	15 min预测			30 min预测			45 min预测			60 min预测
模型	RMSE	MAE	MAPE/%	RMSE	MAE	MAPE/%	RMSE	MAE	MAPE/%	RMSE	MAE	MAPE/%
HA	29.310 0	17.946 9	0.241 2	30.610 5	18.925 4	0.259 7	31.716 3	19.811 9	0.264 9	32.680 3	20.615 1	0.281 1
SVR	28.838 4	17.307 6	0.221 6	29.654 5	17.635 3	0.239 8	31.045 6	19.735 0	0.257 8	32.239 3	19.706 8	0.279 6
GRU	27.515 4	16.360 0	0.198 2	28.225 4	17.289 5	0.205 4	29.885 4	19.145 9	0.221 1	30.248 4	19.355 8	0.251 9
DCRNN	26.064 7	16.879 5	0.192 1	27.985 9	16.981 3	0.210 4	29.015 7	18.879 1	0.219 7	30.024 9	19.016 7	0.232 1
T-GCN	25.464 5	16.455 5	0.190 3	27.828 7	16.773 2	0.201 2	28.846 0	18.401 3	0.215 3	29.604 7	18.902 4	0.240 4
A3T-GCN	25.383 3	16.904 4	0.189 8	27.788 4	16.389 3	0.197 6	28.450 7	18.039 3	0.208 7	28.969 8	18.590 9	0.221 7
KG-DGCN-GRU	24.229 8	15.500 3	0.173 9	25.607 4	16.511 4	0.186 7	27.646 0	17.480 7	0.188 7	28.821 7	17.975 7	0.203 4

模型	15 min预测			30 min预测			45 min预测			60 min预测
模型	RMSE	MAE	MAPE/%	RMSE	MAE	MAPE/%	RMSE	MAE	MAPE/%	RMSE	MAE	MAPE/%
HA	29.310 0	17.946 9	0.241 2	30.610 5	18.925 4	0.259 7	31.716 3	19.811 9	0.264 9	32.680 3	20.615 1	0.281 1
SVR	28.838 4	17.307 6	0.221 6	29.654 5	17.635 3	0.239 8	31.045 6	19.735 0	0.257 8	32.239 3	19.706 8	0.279 6
GRU	27.515 4	16.360 0	0.198 2	28.225 4	17.289 5	0.205 4	29.885 4	19.145 9	0.221 1	30.248 4	19.355 8	0.251 9
DCRNN	26.064 7	16.879 5	0.192 1	27.985 9	16.981 3	0.210 4	29.015 7	18.879 1	0.219 7	30.024 9	19.016 7	0.232 1
T-GCN	25.464 5	16.455 5	0.190 3	27.828 7	16.773 2	0.201 2	28.846 0	18.401 3	0.215 3	29.604 7	18.902 4	0.240 4
A3T-GCN	25.383 3	16.904 4	0.189 8	27.788 4	16.389 3	0.197 6	28.450 7	18.039 3	0.208 7	28.969 8	18.590 9	0.221 7
KG-DGCN-GRU	24.229 8	15.500 3	0.173 9	25.607 4	16.511 4	0.186 7	27.646 0	17.480 7	0.188 7	28.821 7	17.975 7	0.203 4

模型	RMSE	MAE	MAPE/%
KG-DGCN-GRU（weather）	24.300 7	15.590 0	0.191 0
KG-DGCN-GRU（highway）	25.048 9	16.603 8	0.209 3
KG-DGCN-GRU（weather+highway）	24.229 8	15.500 3	0.176 7

模型	RMSE	MAE	MAPE/%
KG-DGCN-GRU（weather）	24.300 7	15.590 0	0.191 0
KG-DGCN-GRU（highway）	25.048 9	16.603 8	0.209 3
KG-DGCN-GRU（weather+highway）	24.229 8	15.500 3	0.176 7

模型	RMSE	MAE	MAPE/%
KG-GCN-GRU	26.842 8	17.658 6	0.204 6
KG-DGCN-GRU	24.229 8	15.500 3	0.176 7

Port traffic flow prediction based on knowledge graph and spatio-temporal diffusion graph convolutional network

基于知识图谱和时空扩散图卷积网络的港口交通流量预测

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