Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides...
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3836 |
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| author | Ariyo Oluwasanmi Muhammad Umar Aftab Zhiguang Qin Muhammad Shahzad Sarfraz Yang Yu Hafiz Tayyab Rauf |
| author_facet | Ariyo Oluwasanmi Muhammad Umar Aftab Zhiguang Qin Muhammad Shahzad Sarfraz Yang Yu Hafiz Tayyab Rauf |
| author_sort | Ariyo Oluwasanmi |
| collection | DOAJ |
| description | Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets. |
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| format | Article |
| id | doaj.art-dec4ed5cf43445869d80825d93d02370 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2025-02-16T21:45:45Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dec4ed5cf43445869d80825d93d023702025-01-17T02:23:45ZengMDPI AGSensors1424-82202023-04-01238383610.3390/s23083836Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic PredictionAriyo Oluwasanmi0Muhammad Umar Aftab1Zhiguang Qin2Muhammad Shahzad Sarfraz3Yang Yu4Hafiz Tayyab Rauf5School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanCentre for Infrastructure Engineering and Safey, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, AustraliaIndependent Researcher, Bradford BD8 0HS, UKIntelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.https://www.mdpi.com/1424-8220/23/8/3836traffic forecastinggraph convolutional networkgated recurrent unitmulti-head attention |
| spellingShingle | Ariyo Oluwasanmi Muhammad Umar Aftab Zhiguang Qin Muhammad Shahzad Sarfraz Yang Yu Hafiz Tayyab Rauf Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction Sensors traffic forecasting graph convolutional network gated recurrent unit multi-head attention |
| title | Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction |
| title_full | Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction |
| title_fullStr | Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction |
| title_full_unstemmed | Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction |
| title_short | Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction |
| title_sort | multi head spatiotemporal attention graph convolutional network for traffic prediction |
| topic | traffic forecasting graph convolutional network gated recurrent unit multi-head attention |
| url | https://www.mdpi.com/1424-8220/23/8/3836 |
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