[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 …
State-of-art review of traffic signal control methods: challenges and opportunities
Introduction Due to the menacing increase in the number of vehicles on a daily basis,
abating road congestion is becoming a key challenge these years. To cope-up with the …
abating road congestion is becoming a key challenge these years. To cope-up with the …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Multi-agent deep reinforcement learning for large-scale traffic signal control
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …
Scheduling eight-phase urban traffic light problems via ensemble meta-heuristics and Q-learning based local search
This paper addresses urban traffic light scheduling problems (UTLSP) with eight phases.
The objective is to minimize the total vehicle delay time by assigning traffic phases and …
The objective is to minimize the total vehicle delay time by assigning traffic phases and …
Applications of deep learning in intelligent transportation systems
AK Haghighat, V Ravichandra-Mouli… - Journal of Big Data …, 2020 - Springer
Abstract In recent years, Intelligent Transportation Systems (ITS) have seen efficient and
faster development by implementing deep learning techniques in problem domains which …
faster development by implementing deep learning techniques in problem domains which …
Multi-objective deep reinforcement learning approach for adaptive traffic signal control system with concurrent optimization of safety, efficiency, and decarbonization at …
This study introduces a novel approach to adaptive traffic signal control (ATSC) by
leveraging multi-objective deep reinforcement learning (DRL) techniques. The proposed …
leveraging multi-objective deep reinforcement learning (DRL) techniques. The proposed …
A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control
Abstract Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
Radar jamming decision-making in cognitive electronic warfare: A review
C Zhang, L Wang, R Jiang, J Hu, S Xu - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
With the increasingly complex electromagnetic environment and the intelligent development
of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize …
of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize …
A survey on deep reinforcement learning approaches for traffic signal control
H Zhao, C Dong, J Cao, Q Chen - Engineering Applications of Artificial …, 2024 - Elsevier
In the domain of complex urban traffic networks, real-time Traffic Signal Control (TSC) serves
as a pivotal strategy for mitigating congestion. Coordinating signal control across multiple …
as a pivotal strategy for mitigating congestion. Coordinating signal control across multiple …