[HTML][HTML] Comprehensive systematic review of information fusion methods in smart cities and urban environments
Smart cities result from integrating advanced technologies and intelligent sensors into
modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in …
modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in …
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Future smart cities are the key to fulfilling the ever-growing demands of citizens. Information
and communication advancements will empower better administration of accessible …
and communication advancements will empower better administration of accessible …
AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change
Climate change is one of the most pressing global challenges we face today. The impacts of
rising temperatures, sea levels, and extreme weather events are already being felt around …
rising temperatures, sea levels, and extreme weather events are already being felt around …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
Spatial–temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism
Z Chen, Z Lu, Q Chen, H Zhong, Y Zhang, J Xue… - Information Sciences, 2022 - Elsevier
Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays
an important role in traffic management. The graph convolution network (GCN) is widely …
an important role in traffic management. The graph convolution network (GCN) is widely …
A spatio-temporal sequence-to-sequence network for traffic flow prediction
Spatio-temporal prediction has drawn much attention given its wide application, of which
traffic flow prediction is a typical task. Within the vision of smart cities, traffic flow prediction …
traffic flow prediction is a typical task. Within the vision of smart cities, traffic flow prediction …
Research trends, themes, and insights on artificial neural networks for smart cities towards SDG-11
Smart Cities can promote economic growth, sustainable transport, environmental
sustainability, and good governance among cities. These benefits can support cities in …
sustainability, and good governance among cities. These benefits can support cities in …
Deep spatio-temporal 3D densenet with multiscale ConvLSTM-Resnet network for citywide traffic flow forecasting
R He, Y Liu, Y Xiao, X Lu, S Zhang - Knowledge-Based Systems, 2022 - Elsevier
Reliable traffic flow forecasting is paramount in Intelligent Transportation Systems (ITS) as it
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …
can effectively improve traffic efficiency and social security. Its vital challenge is to effectively …
[HTML][HTML] Particle swarm optimization and RBF neural networks for public transport arrival time prediction using GTFS data
Abstract Accurate prediction of Public Transport (PT) mobility is important for intelligent
transportation. Nowadays, mobility data have become increasingly available with the …
transportation. Nowadays, mobility data have become increasingly available with the …