Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.
problem Urban traffic congestion in expanding cities.
method Behavioral system theory, data-driven control, DeePC algorithm.
result DeePC outperforms existing traffic control methods in travel time and CO2 emissions.
Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration…
DQN optimizes traffic light control policies in ITSs.
problem Challenges in scalable real-time actuation mechanisms for smart traffic management.
method Exploration of Deep Q-Networks (DQN) for traffic light control policies.
result DQN algorithms produce intelligent behavior, such as greenwave patterns.
QCOMBO optimizes traffic signals across large networks using a combination of independent and centralized RL.
problem Optimizing global traffic conditions over large road networks using single-agent reinforcement learning is challenging.
method QCOMBO integrates independent and centralized learning to optimize traffic signals, using a consistency regularization loss to ensure scalability.
result QCOMBO outperforms state-of-the-art MARL algorithms in diverse road topologies and traffic flow conditions.
Paper proposes a deep RL approach for traffic signal control balancing efficiency and equity.
problem Inefficient and inflexible traffic signal controllers.
method Deep reinforcement learning with a novel reward function combining efficiency and equity.
result The proposed algorithm achieves state-of-the-art performance on various traffic scenarios.
Novel RL method handles urban driving tasks including traffic lights.
problem Difficulties in reinforcement learning for complex urban driving tasks.
method Implicit affordances for model-free RL.
result Successfully handles urban driving tasks including traffic lights.
Project aims to reduce traffic congestion in Singapore using CNNs.
problem Traffic congestion in Singapore.
method Convolutional Neural Networks (CNNs) for traffic density estimation; traffic signal control algorithms.
result CNNs effectively estimate traffic density from images, leading to improved traffic control.
Deep learning and prior maps improve traffic light recognition for autonomous cars.
problem Recognizing traffic lights for autonomous cars in urban environments.
method Combining deep learning-based detection with prior maps for traffic light identification and state recognition.
result The proposed system correctly identified relevant traffic lights along predefined routes.
Paper optimizes traffic signal control for better traffic flow.
problem Optimizing traffic signal control to reduce congestion and improve safety.
method Value-based reinforcement learning with interpretable policy functions (polynomial functions).
result Deep Regulatable Hardmax Q-learning variant reduces vehicle delay by up to 19.4%.
It is well known that any sufficiently regular one-dimensional payoff function has an explicit static hedge by bonds, forward contracts and lots of vanilla options. We show that the natural extension of the corresponding representation leads to a static hedge based on the same instruments along with traffic light optio…
Adaptive traffic control uses deep RL to improve decision-making.
problem Improving traffic control using deep RL.
method Integrates recent deep RL techniques into a novel DQN-based algorithm (TC-DQN+) for traffic control.
result Proposes a new reward function for traffic control.
Develops a new model for controllable and realistic traffic simulation.
problem Lack of models that offer both controllability and realism in traffic simulation.
method Guided Conditional Diffusion (CTG) model using diffusion modeling and differentiable logic.
result Improves controllability-realism tradeoff over strong baselines.
Paper develops a framework to test deep RL traffic controllers under various uncertainties.
problem Developing robust deep RL traffic controllers for dynamic urban areas.
method Open-source callback-based framework for evaluating deep RL configurations in a traffic simulation.
result Deep RL controllers perform well under demand surges, incidents, and sensor failures.
Survey on modern traffic signal control methods.
problem Minimizing travel time at road intersections.
method Machine learning, reinforcement learning.
result Promotes interdisciplinary research on traffic signal control.
A new deep reinforcement learning model for urban traffic control.
problem Complex traffic dynamics in urban intersections.
method Combines deep learning tricks to solve multiple intersections control problems efficiently.
result Outperforms traditional rule-based approaches in simulations.
Backdoor attacks on DRL-based traffic controllers cause stop-and-go waves or crashes.
problem Vulnerability of DRL-based traffic controllers to machine learning attacks.
method Developed a trigger design methodology based on traffic physics principles.
result Backdoored models can cause stop-and-go traffic waves or AV crashes when triggered.
Deep reinforcement learning system improves air traffic control efficiency and safety.
problem High-density, dynamic, and stochastic air traffic control challenges.
method Deep multi-agent reinforcement learning framework using actor-critic model with PPO loss function.
result Framework resolves 99.97% of all conflicts in extreme high-density scenarios.
GeneraLight improves traffic signal control models' generalization ability.
problem Overfitting and lack of generalization ability in RL TSC models.
method GeneraLight uses a meta-RL framework with a traffic flow generator based on GANs.
result GeneraLight significantly boosts generalization performance across different traffic flows.
A new traffic signal control method using phase competition.
problem Improving urban transportation efficiency through advanced learning techniques.
method Intuitive phase competition principle applied to reinforcement learning for traffic signal control.
result Our model achieves better solutions, faster convergence, and superior generalizability compared to existing RL methods.
GMM uses mmWave radar to classify traffic modes in poor lighting.
problem Classifying traffic modes in poor lighting conditions.
method GMM on mmWave radar point clouds.
result Good segmentation performance in pedestrian and car classification.
IG-RL learns adaptive traffic signals for any network, outperforming existing methods.
problem Adaptive traffic signal control for large networks with combinatorial state and action spaces.
method Graph-Convolutional Networks for decentralized, flexible control.
result IG-RL generalizes to new networks and traffic conditions without additional training.
MuJAM learns traffic signal control policies that generalize to unseen intersections and traffic conditions.
problem Lack of transferability in reinforcement learning methods for traffic signal control.
method Model-based graph reinforcement learning with explicit coordination and generalization to both cyclic and acyclic constraints.
result MuJAM outperforms existing methods in zero-shot and larger transfer settings.
Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.
problem Lack of robustness and stability in adaptation for traffic signal control.
method Value-based Bayesian meta-reinforcement learning framework BM-DQN with fast-adaptation variation and DQN fast-update advantage.
result Framework adapts more quickly and robustly to new scenarios than previous methods.
A new framework reduces traffic congestion by 36%.
problem Efficient traffic signal control in a metropolitan area.
method Two-stage framework combining Evolution Strategies and multi-agent reinforcement learning.
result Reduces traffic congestion by 36%.
A2C-based MARL controls large-scale traffic signals more efficiently.
problem Scalability issue in centralized RL for large-scale traffic control.
method Decentralized Multi-Agent A2C with improved observability and reduced learning difficulty.
result Optimal, robust, and sample-efficient control over other algorithms.
The paper compares LSTM and ARIMA for predicting and classifying network traffic.
problem Predicting and classifying network traffic in cellular networks.
method Employed LSTM and ARIMA for time series prediction and classification.
result LSTM outperforms ARIMA in general, especially with longer training series and optimal feature selection.
New method allows backtesting of systemic risk forecasts.
problem Systemic risk measures are not elitable and identifiable, making backtesting impossible.
method Introduces multi-objective elicitability and Diebold--Mariano type tests.
result Proposes a traffic-light approach for backtesting.
Team MIE-Lab forecasts city traffic using past hour's multi-channel images.
problem Predict city-wide traffic status within 15 mins using past hour's multi-channel images.
method Evaluated network architectures, analyzed data, considered spatio-temporal context.
result Best submission in IARAI competition traffic4cast.
Traffic actors' future motion predicted using a hybrid graph model.
problem Predicting long-term behaviors of traffic actors in complex scenes.
method A hybrid graph model with nodes for actors and traffic elements, and edges for interaction types.
result TrafficGraphNet achieves state-of-the-art trajectory prediction accuracy.
Predicts user traffic for better resource management in cellular networks.
problem Lack of research on traffic prediction in cellular networks at the user level.
method Statistical, rule-based, and deep machine learning methods.
result Deep machine learning outperforms linear statistical learning after a threshold number of previous observations.
This paper tackles efficient cooperative control for large-scale traffic signals using tensor-based deep learning.
problem Efficient training and control for large-scale multi-intersection traffic signals.
method Tensor representation, multi-task learning, imitation learning, proximal policy optimization.
result The proposed model achieves better performance compared to existing methods.
Co-DQL improves traffic signal control using multi-agent reinforcement learning.
problem Optimizing signal timing for large-scale traffic control.
method Cooperative double Q-learning (Co-DQL) with mean field approximation and reward allocation.
result Co-DQL reduces average waiting time for vehicles in the road system.
A new framework uses deep reinforcement learning to improve aircraft separation in busy airspace.
problem Improving aircraft separation in high-density, dynamic airspace constrained by human controllers.
method Proximal Policy Optimization with an attention network for distributed vehicle autonomy.
result The framework significantly reduces offline training time and increases performance.
BiCB combines traffic prediction and bidding optimization for live advertising.
problem Real-time bidding in live advertising with unknown future traffic.
method Binary Constrained Bidding (BiCB) that merges mathematical analysis and statistical traffic estimation.
result BiCB achieves good approximation to optimal bidding results with low complexity.
This study evaluates methods to measure traffic forecasting model confidence.
problem Lack of consensus on uncertainty types and techniques for traffic forecasting models.
method Reviews and compares different uncertainty estimation techniques using real traffic data.
result Empirical evidence shows benefits and caveats of various techniques.
Bayesian model improves traffic prediction with uncertainty estimates.
problem Lack of uncertainty estimates in deep-learning traffic models.
method Proposes a Bayesian recurrent neural network with spectral normalization.
result Spectral normalization improves uncertainty estimates and generalizability.
A deep learning model for traffic forecasting in telecommunication networks.
problem Complex spatial-temporal dependency in traffic forecasting.
method Spatio-Temporal Hybrid Graph Convolutional Network (STHGCN) combining GRUs and hybrid-GCN.
result The proposed model outperforms classical and state-of-the-art methods.
Survey of deep RL in intelligent transportation systems.
problem Optimizing traffic signals and autonomous driving using deep RL.
method Comprehensive review of deep RL applications in traffic control and autonomous driving.
result Summarizes existing works in deep RL-based transportation applications.
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a nove…
MAT combines meta-learning and adversarial training to defend against universal patches.
problem Defending against universal patches that fool models in various contexts.
method Meta adversarial training (MAT) integrates meta-learning with adversarial training.
result MAT increases robustness against universal patch attacks on image classification and traffic-light detection.
This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.
problem Analyzing multi-modal traffic data with overlapping and distinct variables.
method Developed MM-DAGs for multi-task, multi-modal DAG learning, using multi-modal regression and CD measure.
result Proved the effectiveness of MM-DAGs in traffic congestion analysis.
T-GCN predicts traffic using neural networks for spatial and temporal data.
problem Accurate real-time traffic forecasting in urban networks.
method Combines GCN for spatial and GRU for temporal data analysis.
result T-GCN outperforms state-of-the-art baselines on real-world traffic datasets.
NCPF model improves traffic data imputation with neural and tensor methods.
problem Pervasive missing data in traffic analysis due to sensor failures and gaps.
method Neural Canonical Polyadic Factorization (NCPF) integrating CP decomposition and deep learning.
result NCPF outperforms state-of-the-art baselines in urban traffic datasets.
MPE models traffic trajectory data to predict next locations.
problem Predicting next locations from traffic trajectory data.
method Mobility pattern embedding model MPE.
result MPE significantly outperforms state-of-the-art methods in next location prediction.
This paper improves MARL for networked systems through new protocols and discount factors.
problem Improving control in networked systems using multi-agent reinforcement learning.
method Formulated as a spatiotemporal Markov decision process, introduced a spatial discount factor, and proposed NeurComm.
result Appropriate spatial discount factor enhances learning curves of non-communicative MARL algorithms.
New method improves traffic data recovery for streaming data.
problem Improve data quality in traffic data for ITS.
method Online robust tensor recovery algorithm leveraging spatio-temporal correlations and local consistency.
result Significantly improved computational efficiency and high recovery accuracy.
Paper proposes a new model for imputing missing spatiotemporal traffic data.
problem Missing data and sparsity in spatiotemporal traffic data.
method Low-rank tensor completion (LRTC) framework with truncated nuclear norm (TNN).
result The proposed model outperforms state-of-the-art imputation models in various scenarios.
MER-SDN uses machine learning to optimize energy efficiency in SDN networks.
problem Energy efficiency in SDN networks is challenging and impacts both economics and environment.
method Feature extraction, training, and testing phases of a machine learning framework.
result Achieves up to 25X speedup in predicting optimal parameters for energy efficient routing.