On the public citation graph

Decentralized network level adaptive signal control by multi-agent deep reinforcement learning

https://doi.org/10.1016/j.trip.2019.100020

0 signed · 89 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-56994-9openalex-snapshot

Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

inferredcites →https://doi.org/10.1007/bf00992698openalex-snapshot

Q-learning

inferredcites →https://doi.org/10.1016/j.aap.2018.07.031openalex-snapshot

Approach-level real-time crash risk analysis for signalized intersections

inferredcites →https://doi.org/10.1016/j.trc.2018.10.009openalex-snapshot

Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot

Human-level control through deep reinforcement learning

inferredcites →https://doi.org/10.1049/iet-its.2017.0153openalex-snapshot

Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning

inferredcites →https://doi.org/10.1080/15472450.2013.810991openalex-snapshot

Design of Reinforcement Learning Parameters for Seamless Application of Adaptive Traffic Signal Control

inferredcites →https://doi.org/10.1109/9.580874openalex-snapshot

An analysis of temporal-difference learning with function approximation

inferredcites →https://doi.org/10.1109/jas.2016.7508798openalex-snapshot

Traffic signal timing via deep reinforcement learning

inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot

Reinforcement Learning: An Introduction

inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot

Reinforcement Learning: An Introduction

inferredcites →https://doi.org/10.1177/0361198119840611openalex-snapshot

Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network

inferredcites →https://doi.org/10.1609/aaai.v30i1.10295openalex-snapshot

Deep Reinforcement Learning with Double Q-Learning

inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

inferredcites →https://doi.org/10.48550/arxiv.1703.09035openalex-snapshot

Deep Deterministic Policy Gradient for Urban Traffic Light Control

inferredcites →https://doi.org/10.48550/arxiv.1705.02755openalex-snapshot

Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

inferredcites →https://doi.org/10.48550/arxiv.1901.00960openalex-snapshot

Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4665764openalex-extracted

An Environmentally Friendly Integration Control Model Based on Multi Agent System and a Novel Stackelberg Games in Mixed Networks

inferred← citeshttps://doi.org/10.1007/978-981-33-6977-1_41openalex-snapshot

Intelligent Transportation System: The Applicability of Reinforcement Learning Algorithms and Models

inferred← citeshttps://doi.org/10.1007/s10489-022-04105-yopenalex-snapshot

A review of cooperative multi-agent deep reinforcement learning

inferred← citeshttps://doi.org/10.1007/s10586-026-06275-7openalex-snapshot

Optimized adaptive traffic management using double DQN and prioritized experience replay in a multi-agent traffic control setup

inferred← citeshttps://doi.org/10.1007/s42421-020-00020-1openalex-snapshot

Applications of Deep Learning in Intelligent Transportation Systems

inferred← citeshttps://doi.org/10.1016/j.aap.2020.105655openalex-snapshot

Multi-Objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control

inferred← citeshttps://doi.org/10.1016/j.aap.2020.105713openalex-snapshot

Self-learning adaptive traffic signal control for real-time safety optimization

inferred← citeshttps://doi.org/10.1016/j.aap.2020.105799openalex-snapshot

Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network

inferred← citeshttps://doi.org/10.1016/j.aap.2025.108161openalex-snapshot

Two-step deep reinforcement learning for traffic signal control to improve pedestrian safety using connected vehicle data

inferred← citeshttps://doi.org/10.1016/j.asoc.2025.113333openalex-snapshot

Incorporating vision-based artificial intelligence and large language model for smart traffic light control

inferred← citeshttps://doi.org/10.1016/j.cacaie.2026.100048openalex-snapshot

Online closed-loop traffic signal control scheme updating using learning-based turning ratio prediction

inferred← citeshttps://doi.org/10.1016/j.commtr.2023.100104openalex-snapshot

Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111813openalex-snapshot

Cooperative traffic signal control for a partially observed vehicular network using multi-agent reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.116830openalex-snapshot

Reinforcement learning in urban network traffic signal control: A systematic literature review

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127744openalex-snapshot

Learning multiple coordinated agents under directed acyclic graph constraints

inferred← citeshttps://doi.org/10.1016/j.jnca.2022.103497openalex-snapshot

Traffic flow control using multi-agent reinforcement learning

inferred← citeshttps://doi.org/10.1016/j.jsr.2022.02.014openalex-snapshot

Unveiling the relevance of traffic enforcement cameras on the severity of vehicle–pedestrian collisions in an urban environment with machine learning models

inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110440openalex-snapshot

Deep reinforcement learning for traffic signal control with consistent state and reward design approach

inferred← citeshttps://doi.org/10.1016/j.multra.2024.100164openalex-snapshot

Reinforcement learning in transportation research: Frontiers and future directions

inferred← citeshttps://doi.org/10.1016/j.multra.2025.100190openalex-snapshot

Multimodal adaptive traffic signal control: A decentralized multiagent reinforcement learning approach

inferred← citeshttps://doi.org/10.1016/j.simpat.2025.103100openalex-snapshot

Analyzing communication policies in cooperative multi-agent reinforcement learning for traffic signal control: A simulation-based study

inferred← citeshttps://doi.org/10.1016/j.trc.2022.103728openalex-snapshot

CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles

inferred← citeshttps://doi.org/10.1016/j.trc.2025.105017openalex-snapshot

A novel game approach to integrating traffic assignment and signal control for enhanced efficiency and environmental performance in mixed networks

inferred← citeshttps://doi.org/10.1016/j.trip.2021.100425openalex-snapshot

Deep reinforcement learning in transportation research: A review

inferred← citeshttps://doi.org/10.1016/j.trpro.2024.12.018openalex-snapshot

Reduction of Delays at Isolated Signalized Intersection using Novel Golden Eagle-Based Fuzzy Signal Controller

inferred← citeshttps://doi.org/10.1038/s41598-023-36606-2openalex-snapshot

A traffic light control method based on multi-agent deep reinforcement learning algorithm

inferred← citeshttps://doi.org/10.1049/itr2.70147openalex-snapshot

Signal Timing and CAV Trajectory Joint Control Under Mixed Vehicular Environments With Hierarchical Proximal Policy Optimisation

inferred← citeshttps://doi.org/10.1080/01441647.2024.2377637openalex-snapshot

A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles

inferred← citeshttps://doi.org/10.1080/21680566.2022.2141911openalex-snapshot

Real-Time multi-objective optimization of safety and mobility at signalized intersections

inferred← citeshttps://doi.org/10.1109/access.2020.3034141openalex-snapshot

Deep Reinforcement Learning for Traffic Signal Control: A Review

inferred← citeshttps://doi.org/10.1109/access.2024.3351225openalex-snapshot

Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

inferred← citeshttps://doi.org/10.1109/auteee56487.2022.9994296openalex-snapshot

Training for More Robust and Practical Adaptive Signal Control Models

inferred← citeshttps://doi.org/10.1109/cec53210.2023.10254169openalex-snapshot

Efficient Multi-Agent Exploration with Mutual-Guided Actor-Critic

inferred← citeshttps://doi.org/10.1109/cicn59264.2023.10402234openalex-snapshot

Revolutionizing Transportation Using Deep Reinforcement Learning: A Comprehensive Review

inferred← citeshttps://doi.org/10.1109/icaibd51990.2021.9459029openalex-snapshot

Traffic Signal Control Based on Deep Reinforcement Learning with Simplified State and Reward Definitions

inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10725419openalex-snapshot

Multi Agent Deep Reinforcement Learning approach for Traffic Signal Control

inferred← citeshttps://doi.org/10.1109/icecet55527.2022.9873047openalex-snapshot

Calibration-free Traffic Signal Control Method Using Machine Learning Approaches

inferred← citeshttps://doi.org/10.1109/tcds.2020.3035778openalex-snapshot

Behavior Decision of Mobile Robot With a Neurophysiologically Motivated Reinforcement Learning Model

inferred← citeshttps://doi.org/10.1109/tit.2024.3427141openalex-snapshot

Provably Efficient Offline Reinforcement Learning With Trajectory-Wise Reward

inferred← citeshttps://doi.org/10.1109/tits.2022.3140511openalex-snapshot

A bi-Hierarchical Game-Theoretic Approach for Network-Wide Traffic Signal Control Using Trip-Based Data

inferred← citeshttps://doi.org/10.1109/tits.2022.3141730openalex-snapshot

Integrated Traffic Control for Freeway Recurrent Bottleneck Based on Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1109/tits.2022.3179893openalex-snapshot

Adaptive Traffic Signal Control With Deep Reinforcement Learning and High Dimensional Sensory Inputs: Case Study and Comprehensive Sensitivity Analyses

inferred← citeshttps://doi.org/10.1109/tits.2022.3208236openalex-snapshot

Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control

inferred← citeshttps://doi.org/10.1109/tits.2022.3216203openalex-snapshot

Distributed Signal Control of Arterial Corridors Using Multi-Agent Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1109/tits.2025.3548945openalex-snapshot

Decentralized and Communication-Based Multi-Agent Traffic Signal Control Model Employing a Graph Representation for the State

inferred← citeshttps://doi.org/10.1109/tits.2026.3664174openalex-snapshot

Traffic Signal-Vehicle Cooperative Control via Multimodal Heterogeneous Subgraph Learning

inferred← citeshttps://doi.org/10.1109/tnnls.2023.3264540openalex-snapshot

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

inferred← citeshttps://doi.org/10.1109/tvt.2024.3444475openalex-snapshot

A Graph Deep Reinforcement Learning Traffic Signal Control for Multiple Intersections Considering Missing Data

inferred← citeshttps://doi.org/10.1111/mice.12897openalex-snapshot

Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles

inferred← citeshttps://doi.org/10.1145/3447556.3447565openalex-snapshot

Recent Advances in Reinforcement Learning for Traffic Signal Control

inferred← citeshttps://doi.org/10.1145/3511808.3557569openalex-snapshot

Cooperative Max-Pressure Enhanced Traffic Signal Control

inferred← citeshttps://doi.org/10.1145/3625236openalex-snapshot

Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles

inferred← citeshttps://doi.org/10.1155/2020/8873962openalex-snapshot

Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

inferred← citeshttps://doi.org/10.1155/atr/5579549openalex-snapshot

A Deep Reinforcement Learning–Based Urban Traffic Control Model for Vehicle‐to‐Everything Ecosystem

inferred← citeshttps://doi.org/10.1177/03611981211062891openalex-snapshot

Real-Time Crash-Risk Optimization at Signalized Intersections

inferred← citeshttps://doi.org/10.1177/03611981231201107openalex-snapshot

Traffic Safety Assessment with Integrated Communication System of Connected and Automated Vehicles at Signalized Intersections

inferred← citeshttps://doi.org/10.1177/03611981241253581openalex-snapshot

Deep Reinforcement Learning Technique for Traffic Metering in Connected Urban Street Networks

inferred← citeshttps://doi.org/10.1177/03611981251384965openalex-snapshot

Impact of Pedestrian and Vehicle Connectivity on Intersection Performance: A High-Fidelity Simulation with Deep Reinforcement Learning Control

inferred← citeshttps://doi.org/10.2139/ssrn.5077753openalex-snapshot

Network Adaptive Signal Control Using Closed-Loop Online Updating Strategy and Learning-Based Turning Ratio Prediction

inferred← citeshttps://doi.org/10.31224/osf.io/ewxrjopenalex-snapshot

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

inferred← citeshttps://doi.org/10.31274/td-20240329-826openalex-snapshot

Transportation safety enhancement by implementing computer vision based models in intelligent transportation systems

inferred← citeshttps://doi.org/10.3390/app112210688openalex-snapshot

Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning

inferred← citeshttps://doi.org/10.3390/infrastructures6100138openalex-snapshot

Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks

inferred← citeshttps://doi.org/10.3390/s21072302openalex-snapshot

Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.3390/su14031542openalex-snapshot

Theory and Experiment of Cooperative Control at Multi-Intersections in Intelligent Connected Vehicle Environment: Review and Perspectives

inferred← citeshttps://doi.org/10.3390/su14042436openalex-snapshot

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

inferred← citeshttps://doi.org/10.3390/su141710467openalex-snapshot

Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestrians

inferred← citeshttps://doi.org/10.3934/era.2024174openalex-snapshot

Uniformity of markov elements in deep reinforcement learning for traffic signal control

inferred← citeshttps://doi.org/10.48550/arxiv.2010.06187openalex-snapshot

Deep Reinforcement Learning and Transportation Research: A Comprehensive Review

inferred← citeshttps://doi.org/10.48550/arxiv.2106.11854openalex-snapshot

Off-Policy Reinforcement Learning with Delayed Rewards

inferred← citeshttps://doi.org/10.48550/arxiv.2111.02845openalex-snapshot

Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles

inferred← citeshttps://doi.org/10.52013/2658-7556-57-7-6openalex-snapshot

DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.