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.
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “traffic intersection”
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.
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.
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.
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.
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.
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…
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.
Paper develops a method to detect abnormal road user trajectories using deep autoencoder.
problem Insufficient abnormal data for training machine learning models.
method Used a deep autoencoder network trained on augmented normal data.
result Performs better than classical outlier detection methods on various scenes.
Neural networks predict traffic flow in smart cities.
problem Forecasting stochastic and nonlinear traffic flow.
method Various recurrent neural networks trained on intersection data.
result Vector output model with gated recurrent units performed best.
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%.
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphscs.LG
Neural networks improve traffic flow predictions by considering lane interactions.
problem Improving traffic flow predictions at the lane level.
method Applying neural attention to deep neural networks for modeling spatiotemporal traffic dynamics.
result Attentional neural network models can use information from nearby lanes to improve predictions.
Paper proposes an intersection decision algorithm for autonomous vehicles.
problem Navigating intersections with non-automated vehicles.
method Combines reinforcement learning for high-level decisions and model predictive control for low-level planning.
result The proposed algorithm outperforms another controller in success rate and training episodes.
New approach learns traffic behavior from unlabelled video.
problem Learning from demonstration in settings with unlabelled raw video data.
method Calibrates camera, detects objects, tracks them, and uses trajectories for LfD.
result Can learn traffic behavior from raw video without additional expert knowledge.
Automated road infrastructure mapping using connected vehicle data and deep learning.
problem Manual identification of intersections is laborious and time-consuming.
method Geohashing, YOLOv5 algorithm for classification of road segments and intersections.
result Overall classification accuracy of 95%, with high F1 scores for straight roads and intersections.
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.
Paper automates car negotiation in intersections using Q-learning.
problem Automated vehicles negotiate with human-driven cars in intersections.
method Deep Q-learning applied to simulated traffic with various driver behaviors.
result 98% success rate in avoiding collisions with other vehicles.
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%.
Proposes a method to model multi-vehicle interactions using Gaussian processes.
problem Challenges in modeling correlations between multiple road users over time.
method Uses a stochastic vector field model and non-parametric Bayesian learning.
result Captures motion patterns from complex multi-vehicle interactions without heroic prior assumptions.
Deep learning models improve traffic image segmentation accuracy but vary by city and channel.
problem Improving semantic segmentation accuracy for traffic analysis.
method Evaluation of PSPNet and ICNet on Cityscapes and custom urban images.
result Different models have varying accuracy and inference time for different cities and channels.
Improved pedestrian motion prediction in urban intersections.
problem Accurate prediction of pedestrian trajectories in crowded urban environments.
method Incorporates semantic features from the environment into a Gaussian Process (GP) model for pedestrian motion prediction.
result 12.5% improvement in prediction accuracy and 2.65 times reduction in AUC.
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.
Critical to evaluating the capacity, scalability, and availability of web systems are realistic web traffic generators. Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server. Administrator…
TM-CNN predicts lane-level traffic speeds considering volume impact.
problem Aggregated lane-level traffic speed prediction and volume impact.
method Two-stream multi-channel CNN, data conversion, two-stream deep neural network, loss function.
result TM-CNN outperforms existing models in multi-lane traffic speed prediction.
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph C…
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.
Model predicts traffic incident duration and identifies key features.
problem Predict traffic incident duration and identify critical features.
method Multi-task learning framework with sparsity optimization and ADMM algorithm.
result Model predicts incident duration and identifies key features effectively.
Model predicts traffic speed using urban incidents.
problem Accurately predicting traffic speed in urban areas.
method Deep Incident-Aware Graph Convolutional Network (DIGC-Net).
result Model outperforms competing benchmarks in traffic speed prediction.
Deep neural network reconstructs traffic speeds from sparse vehicle data.
problem Reconstructing traffic speeds from limited probe vehicle data.
method Convolutional neural network architecture for spatio-temporal learning.
result The method can reconstruct traffic speeds with low probe vehicle penetration.
A scoring method for driving safety using trajectory data.
problem Managing traffic safety through driver behaviors and violations.
method Extract driving habits and violations from trajectories, train a model, score drivers.
result Proves the effectiveness of the scoring method using traffic simulation.
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.
This study compares transfer learning and multi-agent learning for AI-driven traffic agents.
problem Improving traffic flow in mixed-intelligence highway scenarios.
method Online MIT DeepTraffic simulation, deep reinforcement learning, elitist evolutionary algorithm, hyperparameter search, transfer learning, multi-agent learning.
result Transfer learning and multi-agent learning yield different average speeds for AI-driven traffic agents.
Model traffic congestion events using multi-modal data and attention-based neural networks.
problem Capture non-homogeneous temporal and directional spatial dependencies in traffic congestion events.
method Attention-based neural networks for point processes, adapted tail-up model for spatial statistics.
result Superior performance compared to state-of-the-art methods on synthetic and real data.
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …
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.
Google uses continuous streams of data from industry partners in order to deliver accurate results to users. Unexpected drops in traffic can be an indication of an underlying issue and may be an early warning that remedial action may be necessary. Detecting such drops is non-trivial because streams are variable and noi…
Model predicts traffic flow dynamics from sparse data.
problem Predict traffic flow from limited data.
method Mesoscopic model using factor graphs and message passing.
result Efficiently estimates traffic conditions with low probe vehicle penetration.
MSCT predicts post-crash traffic speed using causal inference.
problem Time-varying confounding bias in post-crash traffic prediction.
method Marginal Structural Causal Transformer (MSCT) incorporating Marginal Structural Models and balanced loss function.
result MSCT outperforms state-of-the-art models in multi-step-ahead prediction.
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.
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.
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.
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.
New CapsNet and NLSTM framework improves traffic forecasting accuracy.
problem Accurate traffic forecasting for complex transportation networks.
method CapsNet for spatial features, nested LSTM for temporal dependencies.
result Framework outperforms multiple baseline models in Beijing network.
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.
Study compares neural nets and gradient boosting for traffic optimization, revealing accuracy issues near local optima.
problem Accuracy of neural nets and gradient boosting models in traffic optimization near local optima.
method 16 neural nets and 20 genetic algorithm settings analyzed for traffic optimization.
result Accuracy drops near local optima, affecting traffic optimization efficiency.
Improved LSTM and ARIMA model for traffic flow forecasting.
problem Poor stability, high data requirements, and adaptability issues in existing traffic flow prediction methods.
method Combination prediction method based on improved LSTM and ARIMA models.
result The SDLSTM-ARIMA model achieves higher accuracy in traffic flow prediction.
Study uncovers uncertainty in traffic prediction models across cities.
problem Lack of interpretability in deep learning models for traffic prediction.
method Investigated uncertainty quantification methods for image-based traffic prediction.
result Meaningful uncertainty estimates can be recovered for traffic prediction.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.