Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction

H Yao, X Tang, H Wei, G Zheng, Z Li - Proceedings of the AAAI …, 2019 - ojs.aaai.org
Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …

A hybrid visualization model for knowledge mapping: Scientometrics, SAOM, and SAO

G Xiao, L Chen, X Chen, C Jiang, A Ni… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Predicting the crowd flow in various areas of the city is of strategic importance for traffic
control and public safety. In recent years, crowd flow prediction based on spatio-temporal …

Traffic flow forecasting with spatial-temporal graph diffusion network

X Zhang, C Huang, Y Xu, L Xia, P Dai, L Bo… - Proceedings of the …, 2021 - ojs.aaai.org
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …

Deep learning on traffic prediction: Methods, analysis, and future directions

X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

H Peng, H Wang, B Du, MZA Bhuiyan, H Ma, J Liu… - Information …, 2020 - Elsevier
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …

Short-term load forecasting with deep residual networks

K Chen, K Chen, Q Wang, Z He, J Hu… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
We present in this paper a model for forecasting short-term electric load based on deep
residual networks. The proposed model is able to integrate domain knowledge and …

A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing

A Ali, Y Zhu, M Zakarya - Multimedia Tools and Applications, 2021 - Springer
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …

Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

L Cai, K Janowicz, G Mai, B Yan, R Zhu - Transactions in GIS, 2020 - Wiley Online Library
Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐
temporal dependencies at different scales. Recently, several hybrid deep learning models …

Deep multi-view spatial-temporal network for taxi demand prediction

H Yao, F Wu, J Ke, X Tang, Y Jia, S Lu… - Proceedings of the …, 2018 - ojs.aaai.org
Taxi demand prediction is an important building block to enabling intelligent transportation
systems in a smart city. An accurate prediction model can help the city pre-allocate …

A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

T Bogaerts, AD Masegosa, JS Angarita-Zapata… - … Research Part C …, 2020 - Elsevier
Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …