Prediction of traffic swarm movement situation based on generalized spatio-temporal graph convolution network

QU Xu; GAN Rui; AN Bo-cheng; LI Lin-heng; CHEN Zhi-jun; RAN Bin

doi:10.19818/j.cnki.1671-1637.2022.03.006

Volume 22 Issue 3

Jun. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(3): 79-88

QU Xu, GAN Rui, AN Bo-cheng, LI Lin-heng, CHEN Zhi-jun, RAN Bin. Prediction of traffic swarm movement situation based on generalized spatio-temporal graph convolution network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 79-88. doi: 10.19818/j.cnki.1671-1637.2022.03.006

Citation:

QU Xu, GAN Rui, AN Bo-cheng, LI Lin-heng, CHEN Zhi-jun, RAN Bin. Prediction of traffic swarm movement situation based on generalized spatio-temporal graph convolution network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 79-88. doi: 10.19818/j.cnki.1671-1637.2022.03.006

Citation:

QU Xu, GAN Rui, AN Bo-cheng, LI Lin-heng, CHEN Zhi-jun, RAN Bin. Prediction of traffic swarm movement situation based on generalized spatio-temporal graph convolution network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 79-88. doi: 10.19818/j.cnki.1671-1637.2022.03.006

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Prediction of traffic swarm movement situation based on generalized spatio-temporal graph convolution network

doi: 10.19818/j.cnki.1671-1637.2022.03.006

QU Xu^{1, 2, 3
,},
GAN Rui^{1, 2, 3},
AN Bo-cheng^{1, 2, 3},
LI Lin-heng^{1, 2, 3},
CHEN Zhi-jun⁴,
RAN Bin^{1, 2, 3}

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
Institute on Internet of Mobility of Southeast University and University of Wisconsin-Madison, Southeast University, Nanjing 211189, Jiangsu, China
3.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, Jiangsu, China
4.
Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

Key Research and Development Program of Shandong Province 2020CXGC010118

More Information

Author Bio:
QU Xu(1982-), male, associate professor, PhD, quxu@seu.edu.cn
Received Date: 2021-12-14
Publish Date: 2022-06-25

Abstract

Abstract

To address the problem that traffic congestion on highways and urban expressways is becoming more and more serious and causes great difficulties for traffic management and control, a traffic speed prediction model was proposed based on the generalized spatio-temporal graph convolution network (GSTGCN). According to the complex spatio-temporal characteristics of traffic data, the generalized traffic data graph structure was defined, and the adjacency relationships of the generalized graph were constructed. By the basic theory of graph convolution network, the Chebyshev approximation and the first-order approximation were adopted to simplify the computational cost of the graph convolution operation, and a generalized graph convolution operator was established. With the generalized graph convolution module, standard convolution module, and linear fully-connected layer, a GSTGCN model was presented to extract the spatial and temporal characteristics of complex traffic data. The vehicle speed, flow, and occupancy datum were recorded by 38 detectors at 5-minute intervals for 21 weekdays on the expressway network in Milwaukee, Wisconsin, USA. The short-term traffic speed prediction accuracy and training efficiency of the GSTGCN model were evaluated on this data set. Analysis results show that compared with the results of the traditional auto regressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) model, and the recent spatio-temporal graph convolution network (STGCN) model, the root mean square error, mean absolute error, and mean absolute percentage error of the GSTGCN model in the traffic speed prediction reduces by 22.79%, 22.97%, and 16.73%, respectively. Moreover, the training time of the GSTGCN model is 5.17% and 75.71% shorter than those of the STGCN model and LSTM model, respectively. Therefore, the GSTGCN model is able to effectively deal with the complex spatio-temporal traffic data structure, accurately predict the traffic speed, and provide information on the movement situation of traffic swarm for the traffic control and management. 4 tabs, 6 figs, 31 refs.

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References(31)

References

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Prediction of traffic swarm movement situation based on generalized spatio-temporal graph convolution network

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