Proposes a method to find traffic regulations efficiently.
problem Finding appropriate traffic regulations in congested events.
method Graph Convolutional Networks for modeling regulation effects.
result The method can find a road to close that reduces travel time.
Paper tackles ML for non-scheduled URLLC traffic in 5G networks.
problem Coexistence of scheduled and non-scheduled URLLC traffic with stringent reliability and latency requirements.
method Distributed risk-aware ML solution for resource management (RRM).
result 75% increase in data rate for scheduled traffic with 99.99% reliability for both types.
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.
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%.
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.
We propose a new backtesting framework for Expected Shortfall that could be used by the regulator. Instead of looking at the estimated capital reserve and the realised cash-flow separately, one could bind them into the secured position, for which risk measurement is much easier. Using this simple concept combined with …
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…
The goal of this project is to introduce and present a machine learning application that aims to improve the quality of life of people in Singapore. In particular, we investigate the use of machine learning solutions to tackle the problem of traffic congestion in Singapore. In layman's terms, we seek to make Singapore …
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.
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.
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…
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.
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.
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.
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.
Stochastic models analyze traffic network performance.
problem Evaluate traffic system performance.
method Stochastic cell transmission models, preference functionals, Gaussian process regression.
result Illustrated in two case studies.
PSTN improves traffic condition forecasting with deep neural networks.
problem Challenges in accurately forecasting traffic conditions due to complex spatiotemporal correlations.
method Proposes PSTN with three modules: graph convolutional network, temporal convolutional network, and gated recurrent unit framework.
result Significantly outperforms state-of-the-art benchmarks in short-term traffic conditions forecasting.
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.
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.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
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.
New neural network predicts traffic flow across different cities.
problem Forecasting traffic flow across different cities is challenging due to spatio-temporal correlations.
method Proposes a local-spacetime neural network (STNN) that captures universal spatio-temporal correlations.
result Improves prediction accuracy by 4% over state-of-the-art methods.
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.
ATFM predicts traffic flow using attention mechanism and neural networks.
problem Predicting traffic flow with diverse factors integration.
method Unified neural network (ATFM) with attention mechanism and ConvLSTM units.
result ATFM outperforms in predicting citywide short-term/long-term traffic flow.
DistPre predicts traffic speeds efficiently for large networks.
problem Fine-grained, accurate speed prediction for large-scale transportation networks.
method Customizes LSTM models on a cluster, sharing trained models between detectors.
result Efficient and accurate fine-grained traffic-speed prediction.
Project analyzes traffic videos to improve Jakarta's safety.
problem Improving traffic safety in Jakarta.
method Developed a pipeline to analyze traffic videos, turning them into usable databases.
result Better understanding of traffic challenges and safety risks.
Simple model classifies traffic tweets from Twitter using word embeddings.
problem Sparsity and curse of dimensionality in bag-of-words representation for traffic tweets.
method Proposes a word embedding-based framework to classify traffic-related tweets from non-traffic ones.
result State-of-the-art test accuracy of 95.9% achieved with a simple model.
Graph-partitioning-based DCRNN improves traffic forecasting for large highways.
problem Challenges in accurately forecasting traffic on large highway networks.
method Graph-partitioning method to decompose large networks into smaller, independent networks.
result Demonstrated improved traffic forecasting on a large California highway network.
Two autoencoding models learn latent traffic scene representations.
problem Learning latent representations of traffic scenarios.
method CNN and RNN models for spatio-temporal and temporal data, incorporating permutation invariance.
result Latent scenario embeddings can be used for clustering and similarity retrieval.
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
problem Limited transferability of deep learning models for traffic flow prediction across different locations.
method Integrates Newell's traffic flow estimators to capture broader dynamics and incorporates spatial dependencies.
result Improves model performance in predicting traffic flows over different horizons.
Proposes a graph neural network for traffic forecasting in WANs.
problem Traffic forecasting challenges in WANs due to dynamic and large data volumes.
method Dynamic diffusion convolutional recurrent neural networks for multistep traffic forecasting.
result Significant improvements in forecasting accuracy compared to classical methods.
Unified approach detects traffic conflicts across various interactions.
problem Inconsistent detection of traffic conflicts across different interactions.
method Unified probabilistic approach decomposes conflicts into statistical learning tasks.
result Effective collision warnings across diverse datasets and environments.
Graphs model traffic interactions, reducing prediction error by 30%.
problem Traffic prediction accuracy with complex interactions.
method Graph Neural Networks (GNN) for modeling vehicle interactions.
result Prediction error decreases by 30% with GNNs compared to non-interaction models.