Traffic volume prediction for scenic spots based on multi‐source and heterogeneous data
Y Gao, YY Chiang, X Zhang, M Zhang - Transactions in GIS, 2022 - Wiley Online Library
Traffic prediction for scenic spots is an important topic in modeling an urban traffic system.
Existing traffic prediction approaches typically use raw traffic data and road networks without …
Existing traffic prediction approaches typically use raw traffic data and road networks without …
A Novel Graph Convolutional Gated Recurrent Unit Framework for Network-Based Traffic Prediction
A Smart City is characterized mainly as an efficient, technologically advanced, green, and
socially informed city. An intelligent transportation system (ITS) is a subset area of smart …
socially informed city. An intelligent transportation system (ITS) is a subset area of smart …
MmgFra: A multiscale multigraph learning framework for traffic prediction in smart cities
W Yu, S Wu, M Huang - Earth Science Informatics, 2023 - Springer
Traffic prediction is an important part of smart city projects. Due to the complex topology of
urban road network and the dynamic change of traffic data, establishing a spatio-temporal …
urban road network and the dynamic change of traffic data, establishing a spatio-temporal …
Modeling multi-regional temporal correlation with gated recurrent unit and multiple linear regression for urban traffic flow prediction
Urban traffic flow prediction has received much attention in the past few years, especially
after the availability of huge traffic data. In addition, the efficacy of some existing traffic flow …
after the availability of huge traffic data. In addition, the efficacy of some existing traffic flow …
Region-level traffic prediction based on temporal multi-spatial dependence graph convolutional network from GPS data
H Yang, X Zhang, Z Li, J Cui - Remote Sensing, 2022 - mdpi.com
Region-level traffic information can characterize dynamic changes of urban traffic at the
macro level. Real-time region-level traffic prediction help city traffic managers with traffic …
macro level. Real-time region-level traffic prediction help city traffic managers with traffic …
A Dynamic Spatio‐Temporal Deep Learning Model for Lane‐Level Traffic Prediction
Traffic prediction aims to predict the future traffic state by mining features from history traffic
information, and it is a crucial component for the intelligent transportation system. However …
information, and it is a crucial component for the intelligent transportation system. However …
RL-GCN: Traffic flow prediction based on graph convolution and reinforcement learning for smart cities
H Xing, A Chen, X Zhang - Displays, 2023 - Elsevier
The traffic flow problem has become essential in urban planning and management in today's
increasingly urbanized world. Traditional traffic flow prediction models cannot fully consider …
increasingly urbanized world. Traditional traffic flow prediction models cannot fully consider …
SHGCN: a hypergraph-based deep learning model for spatiotemporal traffic flow prediction
Traffic flow prediction, as one of the prominent tasks in intelligent transportation systems, is
challenging due to underlying complex spatiotemporal characteristics. Consideration of …
challenging due to underlying complex spatiotemporal characteristics. Consideration of …
Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit
The prediction of regional traffic flows is important for traffic control and management in an
intelligent traffic system. With the help of deep neural networks, the convolutional neural …
intelligent traffic system. With the help of deep neural networks, the convolutional neural …
A macro–micro spatio-temporal neural network for traffic prediction
Accurate traffic prediction is crucial for planning, management and control of intelligent
transportation systems. Most state-of-the-art methods for traffic prediction effectively capture …
transportation systems. Most state-of-the-art methods for traffic prediction effectively capture …