Crowd flow forecasting with multi-graph neural networks
X Zhang, R Cao, Z Zhang, Y Xia - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Crowd flow forecasting is of great significance for urban traffic management and personal
travel planning. Due to the complexity of the urban geographic structure and the highly …
travel planning. Due to the complexity of the urban geographic structure and the highly …
St-expertnet: A deep expert framework for traffic prediction
Recently, forecasting the crowd flows has become an important research topic, and plentiful
technologies have achieved good performances. As we all know, the flow at a citywide level …
technologies have achieved good performances. As we all know, the flow at a citywide level …
[HTML][HTML] Predicting citywide crowd flows using deep spatio-temporal residual networks
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, including spatial …
safety, and very challenging as it is affected by many complex factors, including spatial …
Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism
C Tian, X Zhu, Z Hu, J Ma - Applied Intelligence, 2020 - Springer
Flow prediction at a citywide level is of great significance to traffic management and public
safety. Since deep learning has achieved success to deal with complex nonlinear problems …
safety. Since deep learning has achieved success to deal with complex nonlinear problems …
Attentive crowd flow machines
Traffic flow prediction is crucial for urban traffic management and public safety. Its key
challenges lie in how to adaptively integrate the various factors that affect the flow changes …
challenges lie in how to adaptively integrate the various factors that affect the flow changes …
Multi-perspective convolutional neural networks for citywide crowd flow prediction
G Dai, W Kong, Y Liu, Y Ge, S Zhang - Applied Intelligence, 2023 - Springer
Crowd flow prediction is an important problem of urban computing with many applications,
such as public security. Inspired by the success of deep learning, various deep learning …
such as public security. Inspired by the success of deep learning, various deep learning …
Revisiting convolutional neural networks for citywide crowd flow analytics
Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the
crowd flow (eg, inflow and outflow) of each region in a city based on historical observations …
crowd flow (eg, inflow and outflow) of each region in a city based on historical observations …
Urban crowd flow forecasting based on cellular network
Y Zhao, J Li, X Miao, X Ding - … of the ACM Turing Celebration Conference …, 2019 - dl.acm.org
Forecasting the crowd flows in a city is crucial for public safety, traffic management and
urban planning. Researchers proposed several methods to forecast the crowd flows …
urban planning. Researchers proposed several methods to forecast the crowd flows …
Forecasting the crowd: An effective and efficient neural network for citywide crowd information prediction at a fine spatio-temporal scale
Modelling and forecasting citywide crowd information (eg, crowd volume of a region, the
inflow of crowds into a region, outflow of crowds from a region) at a fine spatio-temporal …
inflow of crowds into a region, outflow of crowds from a region) at a fine spatio-temporal …
Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks
Predicting the accurate traffic crowd flows is of practical importance for intelligent
transportation systems (ITS). However, it is challenging because traffic flows are affected by …
transportation systems (ITS). However, it is challenging because traffic flows are affected by …