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] Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control
Traffic-responsive signal control is a cost-effective, easy-to-implement, network management
strategy, bearing high potential to improve performance in heavily congested networks with …
strategy, bearing high potential to improve performance in heavily congested networks with …
N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control
Abstract The Max Pressure (MP) framework has been shown to be an effective real-time
decentralized traffic signal control algorithm. However, despite its superior performance and …
decentralized traffic signal control algorithm. However, despite its superior performance and …
Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network
Max-pressure signal control has been analytically proven to maximize the network
throughput and stabilize queue lengths whenever possible. Since there are many transit …
throughput and stabilize queue lengths whenever possible. Since there are many transit …
OCC-MP: A Max-Pressure framework to prioritize transit and high occupancy vehicles
Max-pressure (MP) is a decentralized adaptive traffic signal control approach that has been
shown to maximize throughput for private vehicles. However, MP-based signal control …
shown to maximize throughput for private vehicles. However, MP-based signal control …
[HTML][HTML] Multi-objective optimization of traffic signals based on vehicle trajectory data at isolated intersections
Existing fixed-time traffic signal optimization methods mainly use traffic volumes collected by
infrastructure-based detectors (eg, loop detectors). These infrastructure-based detectors …
infrastructure-based detectors (eg, loop detectors). These infrastructure-based detectors …
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control
Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-
intersection traffic signal control. The MDRL algorithms produce the decentralized …
intersection traffic signal control. The MDRL algorithms produce the decentralized …
Learning the max pressure control for urban traffic networks considering the phase switching loss
Previous studies have shown that the max pressure control is a throughput-optimal policy
that can stabilize the store-and-forward traffic network when the demand is within the …
that can stabilize the store-and-forward traffic network when the demand is within the …
Smoothing-MP: A novel max-pressure signal control considering signal coordination to smooth traffic in urban networks
Decentralized traffic signal control methods, such as max-pressure (MP) control or back-
pressure (BP) control, have gained increasing attention in recent years. MP control, in …
pressure (BP) control, have gained increasing attention in recent years. MP control, in …
Deep reinforcement learning-based traffic light scheduling framework for SDN-enabled smart transportation system
N Kumar, S Mittal, V Garg… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This work proposes a traffic-light scheduling framework using the deep reinforcement
learning technique to balance the traffic flow and to prevent congestion in the dense regions …
learning technique to balance the traffic flow and to prevent congestion in the dense regions …