Enhancing transferability of deep reinforcement learning-based variable speed limit control using transfer learning
The study aims to evaluate the performance of the transfer learning algorithm to enhance the
transferability of a deep reinforcement learning-based variable speed limits (VSL) control …
transferability of a deep reinforcement learning-based variable speed limits (VSL) control …
The state-of-the-art of coordinated ramp control with mixed traffic conditions
Ramp metering, a traditional traffic control strategy for conventional vehicles, has been
widely deployed around the world since the 1960s. On the other hand, the last decade has …
widely deployed around the world since the 1960s. On the other hand, the last decade has …
[HTML][HTML] Ramp metering control under stochastic capacity in a connected environment: A dynamic bargaining game theory approach
This paper presents a dynamic predictive and cooperative ramp metering approach that
considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is …
considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is …
Deep reinforcement learning for dynamic incident-responsive traffic information dissemination
This study is concerned with the optimal dynamical information dissemination (DID) problem
in a transportation network interrupted by traffic incidents. Optimizing system performance …
in a transportation network interrupted by traffic incidents. Optimizing system performance …
Vision-based lane-changing behavior detection using deep residual neural network
Accurate lane localization and lane change detection are crucial in advanced driver
assistance systems and autonomous driving systems for safer and more efficient trajectory …
assistance systems and autonomous driving systems for safer and more efficient trajectory …
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 …
guided deep reinforcement learning for coordinated ramp metering and perimeter control in large scale networks
Z Hu, W Ma - Transportation research part C: emerging technologies, 2024 - Elsevier
Effective traffic control methods have great potential in alleviating network congestion.
Particularly, in an urban network consisting of heterogeneous roads (eg, freeways and …
Particularly, in an urban network consisting of heterogeneous roads (eg, freeways and …
Optimal control-based eco-ramp merging system for connected and automated vehicles
Our current transportation system suffers from a number of problems in terms of safety,
mobility, and environmental sustainability. The emergence of innovative intelligent …
mobility, and environmental sustainability. The emergence of innovative intelligent …
A ramp metering method based on congestion status in the urban freeway
Z Liu, Y Wu, S Cao, L Zhu, G Shen - IEEE Access, 2020 - ieeexplore.ieee.org
In ramp metering methods, the ALINEA algorithm is a very effective way and has been
applied widely. But the critical occupancy in ALINEA algorithm is often difficult to obtain and …
applied widely. But the critical occupancy in ALINEA algorithm is often difficult to obtain and …
Framework of active obstacle avoidance for autonomous vehicle based on hybrid soft actor-critic algorithm
Y Chen, S Wu - Journal of transportation engineering, Part A …, 2023 - ascelibrary.org
In this paper, a framework of active obstacle avoidance for autonomous vehicles based on
the hybrid soft actor-critic (SAC) algorithm is proposed. In the stage of local path planning, a …
the hybrid soft actor-critic (SAC) algorithm is proposed. In the stage of local path planning, a …