Learning the evolutionary and multi-scale graph structure for multivariate time series forecasting

J Ye, Z Liu, B Du, L Sun, W Li, Y Fu… - Proceedings of the 28th …, 2022 - dl.acm.org
Recent studies have shown great promise in applying graph neural networks for multivariate
time series forecasting, where the interactions of time series are described as a graph …

Edge content caching with deep spatiotemporal residual network for IoV in smart city

X Xu, Z Fang, J Zhang, Q He, D Yu, L Qi… - ACM Transactions on …, 2021 - dl.acm.org
Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving
increasing service demands for processing various contents (eg, videos). Generally, for …

[HTML][HTML] DeepTSP: Deep traffic state prediction model based on large-scale empirical data

Y Liu, C Lyu, Y Zhang, Z Liu, W Yu, X Qu - … in transportation research, 2021 - Elsevier
Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …

Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction

B Du, H Peng, S Wang, MZA Bhuiyan… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Urban traffic passenger flows prediction is practically important to facilitate many real
applications including transportation management and public safety. Recently, deep …

Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis

Z Lin, J Feng, Z Lu, Y Li, D Jin - Proceedings of the AAAI conference on …, 2019 - aaai.org
Crowd flow prediction is of great importance in a wide range of applications from urban
planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds …

A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction

C Diao, D Zhang, W Liang, KC Li… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Traffic flow forecasting is indispensable in today's society and regarded as a key problem for
Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious …

ESTNet: embedded spatial-temporal network for modeling traffic flow dynamics

G Luo, H Zhang, Q Yuan, J Li… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Accurate spatial-temporal prediction is a fundamental building block of many real-world
applications such as traffic scheduling and management, environment policy making, and …

Understanding private car aggregation effect via spatio-temporal analysis of trajectory data

Z Xiao, H Fang, H Jiang, J Bai… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Understanding the private car aggregation effect is conducive to a broad range of
applications, from intelligent transportation management to urban planning. However, this …

Coupled layer-wise graph convolution for transportation demand prediction

J Ye, L Sun, B Du, Y Fu, H Xiong - … of the AAAI conference on artificial …, 2021 - ojs.aaai.org
Abstract Graph Convolutional Network (GCN) has been widely applied in transportation
demand prediction due to its excellent ability to capture non-Euclidean spatial dependence …

Adaptive multi-kernel SVM with spatial–temporal correlation for short-term traffic flow prediction

X Feng, X Ling, H Zheng, Z Chen… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Accurate estimation of the traffic state can help to address the issue of urban traffic
congestion, providing guiding advices for people's travel and traffic regulation. In this paper …