[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
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 …
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 …
EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as
medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch …
medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch …
Max-pressure traffic signal timing: A summary of methodological and experimental results
MW Levin - Journal of Transportation Engineering, Part A: Systems, 2023 - ascelibrary.org
Max-pressure control is a new approach to signal timing with mathematically proven network
throughput properties. Over the past decade, max-pressure control has emerged from a …
throughput properties. Over the past decade, max-pressure control has emerged from a …
Delay-throughput tradeoffs for signalized networks with finite queue capacity
Network-level adaptive signal control is an effective way to reduce delay and increase
network throughput. However, in the face of asymmetric exogenous demand, the increase of …
network throughput. However, in the face of asymmetric exogenous demand, the increase of …
A max pressure algorithm for traffic signals considering pedestrian queues
This paper proposes a novel max-pressure (MP) algorithm that incorporates pedestrian
traffic into the MP control architecture. Pedestrians are modeled as being included in one of …
traffic into the MP control architecture. Pedestrians are modeled as being included in one of …
[HTML][HTML] Backpressure or no backpressure? Two simple examples
MJ Smith, R Mounce - Transportation research part C: emerging …, 2024 - Elsevier
Many responsive traffic signal control strategies are “pressure-driven”. These strategies
move green-time from stages with a lower pressure to stages with a higher pressure, at each …
move green-time from stages with a lower pressure to stages with a higher pressure, at each …
Traffic signal control under stochastic traffic demand and vehicle turning via decentralized decomposition approaches
Traffic congestion is a global pressing issue but can be mitigated via effective traffic signal
control schemes. In this paper, based on a cell transmission model we coordinate the control …
control schemes. In this paper, based on a cell transmission model we coordinate the control …
Adaptive network traffic control with approximate dynamic programming based on a non-homogeneous Poisson demand model
S Chen, X Lü - Transportmetrica B: Transport Dynamics, 2024 - Taylor & Francis
In this study, we develop a stochastic dynamic traffic-flow model subject to practical
restrictions under the non-homogeneous Poisson vehicle arrival process. Using the cell …
restrictions under the non-homogeneous Poisson vehicle arrival process. Using the cell …