A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
[HTML][HTML] CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor
Tackling traffic signal control through multi-agent reinforcement learning is a widely-
employed approach. However, current state-of-the-art models have drawbacks: intersections …
employed approach. However, current state-of-the-art models have drawbacks: intersections …
Static-dynamic collaborative graph convolutional network with meta-learning for node-level traffic flow prediction
X Yin, W Zhang, X Jing - Expert Systems with Applications, 2023 - Elsevier
Accurate traffic flow prediction relies on the comprehensive extraction of complex
spatiotemporal features from the traffic data. However, existing spatiotemporal models still …
spatiotemporal features from the traffic data. However, existing spatiotemporal models still …
A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning
Due to its capability in handling complex urban intersection environments, deep
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …
Spatiotemporal dynamic graph convolutional network for traffic speed forecasting
X Yin, W Zhang, S Zhang - Information Sciences, 2023 - Elsevier
Accurate traffic speed forecasting is challenging because of complex spatiotemporal
correlations of traffic data. Some studies have recognized that correlations among sensors …
correlations of traffic data. Some studies have recognized that correlations among sensors …
Cyber-physical models for distributed CAV data intelligence in support of self-organized adaptive traffic signal coordination control
While more studies have been focused on adaptive traffic signal control (ATSC) algorithms
to learn the control policy from interactions with the traffic environment by using connected …
to learn the control policy from interactions with the traffic environment by using connected …
New machine learning application platform for spatial–temporal thermal error prediction and control with STFGCN for ball screw system
The big data platform, which has a high control accuracy and efficiency, is expected to
realize the high-accuracy prediction and real-time control of the thermal error (TE) for the …
realize the high-accuracy prediction and real-time control of the thermal error (TE) for the …
Towards explainable traffic signal control for urban networks through genetic programming
The increasing number of vehicles in urban areas draws significant attention to traffic signal
control (TSC), which can enhance the efficiency of the entire network by properly switching …
control (TSC), which can enhance the efficiency of the entire network by properly switching …
Adaptability and sustainability of machine learning approaches to traffic signal control
M Korecki - Scientific Reports, 2022 - nature.com
This study investigates how adaptable Machine Learning Traffic Signal control methods are
to topological variability. We ask how well can these methods generalize to non-Manhattan …
to topological variability. We ask how well can these methods generalize to non-Manhattan …