Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
[HTML][HTML] Explainable artificial intelligence (xai) for intrusion detection and mitigation in intelligent connected vehicles: A review
The potential for an intelligent transportation system (ITS) has been made possible by the
growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration …
growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration …
[HTML][HTML] Artificial intelligence evolution in smart buildings for energy efficiency
The emerging concept of smart buildings, which requires the incorporation of sensors and
big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban …
big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban …
Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning
Abstract According to United Nations (UN) 2030 agenda, the transportation system needs to
be enhanced for the establishment of access to safe, affordable, accessible, and sustainable …
be enhanced for the establishment of access to safe, affordable, accessible, and sustainable …
Traffic flow prediction models–A review of deep learning techniques
AA Kashyap, S Raviraj, A Devarakonda… - Cogent …, 2022 - Taylor & Francis
Traffic flow prediction is an essential part of the intelligent transport system. This is the
accurate estimation of traffic flow in a given region at a particular interval of time in the future …
accurate estimation of traffic flow in a given region at a particular interval of time in the future …
[HTML][HTML] Traffic flow prediction for smart traffic lights using machine learning algorithms
A Navarro-Espinoza, OR López-Bonilla… - Technologies, 2022 - mdpi.com
Nowadays, many cities have problems with traffic congestion at certain peak hours, which
produces more pollution, noise and stress for citizens. Neural networks (NN) and machine …
produces more pollution, noise and stress for citizens. Neural networks (NN) and machine …
[HTML][HTML] Graph neural network for traffic forecasting: The research progress
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
A Long Short-Term Memory-based correlated traffic data prediction framework
Correlated traffic data refers to a collection of time series recorded simultaneously in
different regions throughout the same transportation network route. Due to the presence of …
different regions throughout the same transportation network route. Due to the presence of …
A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network
Spatio-temporal traffic volume forecasting technologies can effectively improve freeway
traffic efficiency and the travel comfort of humans. To construct a high-precision traffic …
traffic efficiency and the travel comfort of humans. To construct a high-precision traffic …