Research graph
References from Beyond static snapshots: gated recurrent units and graph attention networks for smarter multi-agent traffic control. Local targets link to admitted publications; unresolved targets remain external evidence.
Reinforcement learning for true adaptive traffic signal control
10.1061/(asce)0733-947x(2003)129:3(278) · 2003 · External reference
Reinforcement learning-based multi-agent system for network traffic signal control
10.1049/iet-its.2009.0070 · 2010 · External reference
“Reinforcement learning benchmarks for traffic signal control,”
2021 · External reference
“A comprehensive survey of multiagent reinforcement learning.”
10.1109/tsmcc.2007.913919 · 2008 · External reference
Balancing efficiency and fairness in traffic light control through deep reinforcement learning
10.48550/arxiv.2605.10170 · 2026 · External reference
“PressLight: learning max pressure control to coordinate traffic signals in arterial network,”
2019 · External reference
“Toward a thousand lights: decentralized deep reinforcement learning for large-scale traffic signal control,”
10.1609/aaai.v34i04.5744 · 2020 · External reference
The real deal: a review of challenges and opportunities in moving reinforcement learning-based traffic signal control systems towards reality
10.48550/arxiv.2206.11996 · 2022 · External reference
Exploring the synergy of blockchain, IoT, and edge computing in smart traffic management across urban landscapes
10.1007/s10723-024-09762-6 · 2024 · External reference
“Learning phrase representations using rnn encoder-decoder for statistical machine translation,”
10.3115/v1/d14-1179 · 2014 · External reference
“Multi-agent deep reinforcement learning for large-scale traffic signal control,”
10.1109/tits.2019.2901791 · 2020 · External reference
Empirical evaluation of gated recurrent neural networks on sequence modeling
10.48550/arxiv.1412.3555 · 2014 · External reference
Multi-agent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC)
10.1109/tits.2013.2255286 · 2013 · External reference
“Sumo's lane-changing model,”
10.1007/978-3-319-15024-6_7 · 2015 · External reference
Unresolved reference
2009 · External reference
Vanishing gradient problem
10.1142/s0218488598000094 · 1998 · External reference
Explainable reinforcement learning for improved traffic signal control
10.1111/mice.70037 · 2025 · External reference
Unresolved reference
1981 · External reference
“X-light: cross-city traffic signal control using transformer on transformer as meta multi-agent reinforcement learner,”
10.24963/ijcai.2024/11 · 2024 · External reference
“Adam: a method for stochastic optimization,”
2015 · External reference
Unresolved reference
1998 · External reference
“Fairness control of traffic light via deep reinforcement learning,”
10.1109/case48305.2020.9216899 · 2020 · External reference
Unresolved reference
2000 · External reference
“On kinematic waves. II. A theory of traffic flow on long crowded roads,”
10.1098/rspa.1955.0089 · 1955 · External reference
“Microscopic traffic simulation using SUMO,”
10.1109/itsc.2018.8569938 · 2018 · External reference
Scats, Sydney coordinated adaptive traffic system: A traffic responsive method of controlling urban traffic
1990 · External reference
Human-level control through deep reinforcement learning
10.1038/nature14236 · 2015 · External reference
“Integrated diagnosis and prognosis of dynamic systems using deep learning: case study on a welding robot,”
10.1109/access.2026.3671697 · 2026 · External reference
Meta-modeling of maintenance assets using an evolutionary algorithm approach: the case of a corn sheller
10.1016/j.cie.2025.111485 · 2025 · External reference
“Review of road traffic control strategies,”
10.1109/jproc.2003.819610 · 2003 · External reference
Unresolved reference
2021 · External reference
Robust deep reinforcement learning for traffic signal control
10.1007/s42421-020-00029-6 · 2020 · External reference
“Generation and analysis of a large-scale urban vehicular mobility dataset,”
10.1109/tmc.2013.27 · 2014 · External reference
“Deep reinforcement learning with double q-learning,”
10.1609/aaai.v30i1.10295 · 2016 · External reference
The max-pressure controller for arbitrary networks of signalized intersections
10.1007/978-1-4614-6243-9_2 · 2013 · External reference
“Graph attention networks,”
2018 · External reference
Towards multi-agent reinforcement learning based traffic signal control through spatio-temporal hypergraphs
10.48550/arxiv.2404.11014 · 2024 · External reference
“Dueling network architectures for deep reinforcement learning,”
2016 · External reference
Unresolved reference
1958 · External reference
“CoLight: learning network-level cooperation for traffic signal control,”
10.1145/3357384.3357902 · 2019 · External reference
“IntelliLight: a reinforcement learning approach for intelligent traffic light control,”
10.1145/3219819.3220096 · 2018 · External reference
“Transformerlight: a novel sequence modeling based traffic signaling mechanism via gated transformer,”
10.1145/3580305.3599530 · 2023 · External reference
Advances in reinforcement learning for traffic signal control: a review of recent progress
10.1093/iti/liaf009 · 2025 · External reference
Intelligent traffic signal control based on reinforcement learning: a survey
10.1007/s10462-026-11530-9 · 2026 · External reference
Adaptive spatio-temporal self-supervised traffic flow prediction method based on contrastive learning
10.3390/electronics15112238 · 2026 · External reference
Multi-agent deep reinforcement learning with graph attention network for traffic signal control in multiple-intersection urban areas
10.1177/03611981241297979 · 2025 · External reference
“Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,”
10.24963/ijcai.2018/505 · 2018 · External reference
“MetaLight: value-based meta-reinforcement learning for traffic signal control,”
10.1609/aaai.v34i01.5467 · 2020 · External reference
Impact analysis of the market penetration rate of connected vehicles and the failure rate of roadside equipment on data accuracy
10.3390/s26020686 · 2026 · External reference
“Cityflow: a multi-agent reinforcement learning environment for large scale city traffic scenario,”
10.1145/3308558.3314139 · 2019 · External reference
The max-pressure controller for arbitrary networks of signalized intersections
10.1007/978-1-4614-6243-9_2 · ExternalCitation · doi-reference
“Sumo's lane-changing model,”
10.1007/978-3-319-15024-6_7 · ExternalCitation · doi-reference
Intelligent traffic signal control based on reinforcement learning: a survey
10.1007/s10462-026-11530-9 · ExternalCitation · doi-reference
Exploring the synergy of blockchain, IoT, and edge computing in smart traffic management across urban landscapes
10.1007/s10723-024-09762-6 · ExternalCitation · doi-reference
Robust deep reinforcement learning for traffic signal control
10.1007/s42421-020-00029-6 · ExternalCitation · doi-reference
Meta-modeling of maintenance assets using an evolutionary algorithm approach: the case of a corn sheller
10.1016/j.cie.2025.111485 · ExternalCitation · doi-reference
Human-level control through deep reinforcement learning
10.1038/nature14236 · ExternalCitation · doi-reference
Reinforcement learning-based multi-agent system for network traffic signal control
10.1049/iet-its.2009.0070 · ExternalCitation · doi-reference
Reinforcement learning for true adaptive traffic signal control
10.1061/(asce)0733-947x(2003)129:3(278) · ExternalCitation · doi-reference
Advances in reinforcement learning for traffic signal control: a review of recent progress
10.1093/iti/liaf009 · ExternalCitation · doi-reference
“On kinematic waves. II. A theory of traffic flow on long crowded roads,”
10.1098/rspa.1955.0089 · ExternalCitation · doi-reference
“Integrated diagnosis and prognosis of dynamic systems using deep learning: case study on a welding robot,”
10.1109/access.2026.3671697 · ExternalCitation · doi-reference
“Fairness control of traffic light via deep reinforcement learning,”
10.1109/case48305.2020.9216899 · ExternalCitation · doi-reference
“Microscopic traffic simulation using SUMO,”
10.1109/itsc.2018.8569938 · ExternalCitation · doi-reference
“Review of road traffic control strategies,”
10.1109/jproc.2003.819610 · ExternalCitation · doi-reference
Multi-agent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC)
10.1109/tits.2013.2255286 · ExternalCitation · doi-reference
“Multi-agent deep reinforcement learning for large-scale traffic signal control,”
10.1109/tits.2019.2901791 · ExternalCitation · doi-reference
“Generation and analysis of a large-scale urban vehicular mobility dataset,”
10.1109/tmc.2013.27 · ExternalCitation · doi-reference
“A comprehensive survey of multiagent reinforcement learning.”
10.1109/tsmcc.2007.913919 · ExternalCitation · doi-reference
Explainable reinforcement learning for improved traffic signal control
10.1111/mice.70037 · ExternalCitation · doi-reference
Vanishing gradient problem
10.1142/s0218488598000094 · ExternalCitation · doi-reference
“IntelliLight: a reinforcement learning approach for intelligent traffic light control,”
10.1145/3219819.3220096 · ExternalCitation · doi-reference
“Cityflow: a multi-agent reinforcement learning environment for large scale city traffic scenario,”
10.1145/3308558.3314139 · ExternalCitation · doi-reference
“CoLight: learning network-level cooperation for traffic signal control,”
10.1145/3357384.3357902 · ExternalCitation · doi-reference
“Transformerlight: a novel sequence modeling based traffic signaling mechanism via gated transformer,”
10.1145/3580305.3599530 · ExternalCitation · doi-reference
Multi-agent deep reinforcement learning with graph attention network for traffic signal control in multiple-intersection urban areas
10.1177/03611981241297979 · ExternalCitation · doi-reference
“Deep reinforcement learning with double q-learning,”
10.1609/aaai.v30i1.10295 · ExternalCitation · doi-reference
“MetaLight: value-based meta-reinforcement learning for traffic signal control,”
10.1609/aaai.v34i01.5467 · ExternalCitation · doi-reference
“Toward a thousand lights: decentralized deep reinforcement learning for large-scale traffic signal control,”
10.1609/aaai.v34i04.5744 · ExternalCitation · doi-reference
“Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,”
10.24963/ijcai.2018/505 · ExternalCitation · doi-reference
“X-light: cross-city traffic signal control using transformer on transformer as meta multi-agent reinforcement learner,”
10.24963/ijcai.2024/11 · ExternalCitation · doi-reference
“Learning phrase representations using rnn encoder-decoder for statistical machine translation,”
10.3115/v1/d14-1179 · ExternalCitation · doi-reference
Adaptive spatio-temporal self-supervised traffic flow prediction method based on contrastive learning
10.3390/electronics15112238 · ExternalCitation · doi-reference
Impact analysis of the market penetration rate of connected vehicles and the failure rate of roadside equipment on data accuracy
10.3390/s26020686 · ExternalCitation · doi-reference
Empirical evaluation of gated recurrent neural networks on sequence modeling
10.48550/arxiv.1412.3555 · ExternalCitation · doi-reference
The real deal: a review of challenges and opportunities in moving reinforcement learning-based traffic signal control systems towards reality
10.48550/arxiv.2206.11996 · ExternalCitation · doi-reference
Towards multi-agent reinforcement learning based traffic signal control through spatio-temporal hypergraphs
10.48550/arxiv.2404.11014 · ExternalCitation · doi-reference
Balancing efficiency and fairness in traffic light control through deep reinforcement learning
10.48550/arxiv.2605.10170 · ExternalCitation · doi-reference
