A survey of traffic prediction: from spatio-temporal data to intelligent transportation
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Dynamic Graph Neural Networks (DGNNs) have become one of the most promising
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …
Spatio-temporal self-supervised learning for traffic flow prediction
Robust prediction of citywide traffic flows at different time periods plays a crucial role in
intelligent transportation systems. While previous work has made great efforts to model …
intelligent transportation systems. While previous work has made great efforts to model …
Gman: A graph multi-attention network for traffic prediction
Long-term traffic prediction is highly challenging due to the complexity of traffic systems and
the constantly changing nature of many impacting factors. In this paper, we focus on the …
the constantly changing nature of many impacting factors. In this paper, we focus on the …
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of
transportation. However, it is very challenging since the traffic flows usually show high …
transportation. However, it is very challenging since the traffic flows usually show high …
Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
G Lin, A Lin, D Gu - Information Sciences, 2022 - Elsevier
The prediction of short-term traffic flow is critical for improving service levels for drivers and
passengers as well as enhancing the efficiency of traffic management in the urban …
passengers as well as enhancing the efficiency of traffic management in the urban …