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 …
Real-time system optimal traffic routing under uncertainties--Can physics models boost reinforcement learning?
System optimal traffic routing can mitigate congestion by assigning routes for a portion of
vehicles so that the total travel time of all vehicles in the transportation system can be …
vehicles so that the total travel time of all vehicles in the transportation system can be …
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 …
Big-data driven framework to estimate vehicle volume based on mobile device location data
Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control,
transportation project prioritization, road maintenance planning, and more. Traditional …
transportation project prioritization, road maintenance planning, and more. Traditional …
FMS-dispatch: a fast maximum stability dispatch policy for shared autonomous vehicles including exiting passengers under stochastic travel demand
Shared autonomous vehicles (SAVs) are a fleet of autonomous taxis that provide point-to-
point transportation services for travellers, and have the potential to reshape the nature of …
point transportation services for travellers, and have the potential to reshape the nature of …
Deep reinforcement learning for intersection signal control considering pedestrian behavior
G Han, Q Zheng, L Liao, P Tang, Z Li, Y Zhu - Electronics, 2022 - mdpi.com
Using deep reinforcement learning to solve traffic signal control problems is a research
hotspot in the intelligent transportation field. Researchers have recently proposed various …
hotspot in the intelligent transportation field. Researchers have recently proposed various …
Understanding driver-pedestrian interactions to predict driver yielding: Naturalistic open-source dataset collected in Minnesota
Many factors influence the yielding result of a driver-pedestrian interaction, including traffic
volume, vehicle speed, roadway characteristics, etc. While individual aspects of these …
volume, vehicle speed, roadway characteristics, etc. While individual aspects of these …
Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
Coordination in traffic signal control is crucial for managing congestion in urban networks.
Existing pressure-based control methods focus only on immediate upstream links, leading to …
Existing pressure-based control methods focus only on immediate upstream links, leading to …
Boosting max-pressure signal control into practical implementation: Methodologies and simulation studies in city networks
T Xu - 2023 - search.proquest.com
This dissertation presents innovative modifications to the Max-Pressure (MP) control policy,
an adaptive traffic signal control strategy tailored to various urban traffic conditions. The max …
an adaptive traffic signal control strategy tailored to various urban traffic conditions. The max …
Enabling the next generation of transportation systems by accounting for heterogeneity in traffic flow: Modeling and control of mixed autonomy traffic
M Shang - 2024 - search.proquest.com
Traffic engineering is a field characterized by heterogeneity, reflecting the diverse behaviors
of individual agents using the infrastructure. While traffic heterogeneity has been discussed …
of individual agents using the infrastructure. While traffic heterogeneity has been discussed …