High-level synthesis (HLS) shortens the development time of hardware designs and enables faster design space exploration at a higher abstraction level. Optimization of complex applications in HLS is challenging due to the effects of implementation issues such as routing congestion. Routing congestion estimation is abse…
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48 results for “congestion level”
Applications of deep learning in traffic congestion detection, prediction and alleviation: A surveycs.LG
Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.
problem Improving transportation network service by detecting, predicting, and alleviating traffic congestion.
method Comprehensive review of deep learning applications in transportation, focusing on challenges specific to congestion.
result Identification of challenges and gaps in current research on deep learning for traffic congestion.
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Datacs.LG
Online learning improves traffic congestion prediction over time.
problem Model degradation due to concept drift in traffic data.
method Incremental learning from non-stationary time series data.
result Performance of models degrades with increased prediction horizon.
Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.
problem Online congestion games with exponential action sets and strict Nash equilibria.
method CongestEXP algorithm using exponential weights method.
result CongestEXP achieves regret bound and almost exponential convergence to strict Nash equilibrium.
Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion, but detection is not enough because congestion can be further classified as recurrent and non-recurrent congestion (NRC). In particular, NRC in an urban network is mainly caused by incidents, workzones, special events and adverse weather. We p…
As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoi…
On-chain Peak Shavingecon.EM
Study on-chain peak shaving to reduce Ethereum transaction costs.
problem Reducing transaction costs in blockchain networks, especially during congested periods.
method Analyzing transaction-level data from multiple firms across various industries to understand scheduling responses and cost management strategies.
result Firms' scheduling responses to congestion vary, leading to different fee savings and residual costs.
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.
The paper tackles Nash-regret minimization in congestion games with bandit feedback.
problem Minimizing Nash-regret in congestion games with bandit feedback.
method Proposes centralized and decentralized algorithms for congestion games with bandit feedback, and a centralized algorithm for Markov congestion games.
result Sample complexity depends polynomially on the number of players and facilities, not the size of the action set.
Study of congestion in negative curvature manifolds using fair-division algorithms.
problem Estimating and predicting the size and location of congestion core in negative curvature manifolds.
method Introducing a novel fair-division algorithm to estimate congestion core.
result Demonstrated the effectiveness of fair-division algorithms in estimating congestion core.
New method estimates traffic congestion delays using statistical causality.
problem Accurate estimation of traffic congestion delays during accidents.
method Proposes a novel time delay estimation method using lag-specific transfer entropy (TE) and Markov bootstrap techniques.
result Validated the method's efficacy using simulated and real data.
Wasserstein GANs with Gradient Penalty compute a different optimal transport problem called congested transport.
problem Training generative models to produce high-quality synthetic data.
method Wasserstein GANs with Gradient Penalty (WGAN-GP) approach to calculate the Wasserstein 1 distance.
result WGAN-GP computes the minimum of the congested transport problem, not the Wasserstein 1 distance.
NAC-FL optimizes model updates in FL systems by adapting compression to network congestion.
problem Federated Learning systems face congestion and delays in data exchanges.
method NAC-FL dynamically adjusts client compression based on network congestion.
result NAC-FL reduces training time and achieves robust performance improvements.
New algorithm for traffic routing in congested conditions.
problem Optimal routing in congested traffic networks.
method Congested Bandits model, UCB algorithm, iterative least squares planner.
result No-regret algorithms for congestion-aware routing.
Study predicts traffic congestion based on population mobility data.
problem Predicting traffic congestion in multimodal transport networks.
method Machine learning methods applied to population mobility data.
result Likely prediction of congestion based on population movements.
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.
Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control str…
New framework analyzes LLM personalization trade-offs under congestion.
problem Tension between personalization and resource sharing in LLMs.
method Developed a statistical-economic framework to model user incentives.
result Congestion can flip rankings of SFT and ICL, and offers both methods never hurt profits.
EUREKA builds classifiers that use surprising features.
problem Building classifiers that are interesting, not just accurate.
method Uses large language models to rank features by interestingness and builds interpretable classifiers using only selected features.
result EUREKA discovers non-obvious yet predictive features, improving accuracy and offering insights.
Predicts morning traffic congestion using social media data from the previous evening.
problem Challenges in predicting early morning traffic dynamics.
method Mining Twitter messages to understand evening/midnight work and rest patterns.
result People's tweeting patterns before the morning commute are associated with traffic congestion.
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …
In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216,394 patients. We believe this to be the largest study in CHF onset …
Vault as a credit instrumentq-fin.RM
Derives metrics for DeFi vaults, addressing credit risk.
problem Credit risk in DeFi lending vaults.
method Three-level decomposition of vault risk; six structural features identified.
result Estimation architecture for credit risk metrics.
Dynamic linear models improve travel time prediction for congested freeways.
problem Accurate travel time prediction for congested freeways.
method Dynamic linear models (DLMs) with time-varying parameters.
result Significant improvements in travel time prediction accuracy, especially for short-term predictions.
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyondcs.LG
This paper connects deep neural networks to game theory, revealing their congestion game properties.
problem Understanding the behavior of deep neural networks using game theory.
method Analyzing deep neural networks as congestion games and applying game theory results.
result Deep neural networks exhibit congestion game properties, linking them to game theory.
Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.
problem Optimizing energy and reserve allocation between zones to minimize costs and ensure deliverability.
method Developed allocation models for co-allocating traded energy and reserve products, considering both deterministic and stochastic flows.
result Excess reserve supplies can be used for congestion management, leading to additional network benefits.
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.
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%.
Service-induced congestion in memory-constrained LLM serving
problem Service-induced congestion in memory-constrained large language model (LLM) serving
method Developing a discrete-time dynamical model of memory-constrained LLM inference
result The system converges to a unique worst-case limit cycle that is asymptotically stable outside a Lebesgue-measure-zero exact-capture set, with throughput losses as large as 50%.
Proposes three decentralized multi-agent reinforcement learning algorithms to reduce network congestion.
problem Finding a joint policy maximizing long-term return in a decentralized multi-agent system.
method Three fully decentralized multi-agent natural actor-critic (MAN) algorithms using linear function approximations.
result The proposed algorithms achieve performance comparable to or better than standard methods in reducing network congestion.
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.
Graph neural networks improve topology control of power grids.
problem Grid congestion due to renewable energy and electrification.
method Investigated the effect of graph representation on GNN effectiveness for topology control.
result Heterogeneous graph representation outperforms homogeneous in topology control tasks.
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for…
This paper gives a critical account of the minority game literature. The minority game is a simple congestion game: players need to choose between two options, and those who have selected the option chosen by the minority win. The learning model proposed in this literature seems to differ markedly from the learning mod…
In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL …
We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable. The target application is a large scale partially observed system, like e.g. a traffic network, where a small pr…
Agent-based simulation assesses tradable credit schemes for congestion reduction.
problem Simplistic modeling of TCS impacts in transportation research.
method Agent- and activity-based simulation framework within SimMobility.
result TCS stabilizes network and market performance over time, reducing congestion.
Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation. In this study, we develop a semi-supervised deep residual network (ResNet) framework to utilize Wi-Fi communications obtained from smartphones for the pur…
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far rend…
Proposes active learning for meta-learning in graph node response prediction.
problem Difficulty in improving performance with meta-learning due to unbalanced observations.
method Combines graph convolutional neural networks and reinforcement learning for both prediction and node selection.
result Can predict responses and select nodes even for unseen response variables.
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 …
Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Datastat.ML
Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to dete…
Kernel-based mean-field games use MMD penalties for interaction and target costs.
problem Optimizing mean-field games with specific cost functions.
method Kernel structure, random Fourier U-statistics, neural network training.
result Sample-level convergence theorem and rate of convergence proved.
Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the ot…
H-STGCN predicts traffic using navigation data and improves accuracy.
problem Limited accuracy in traffic forecasting due to lack of contextual information.
method Proposes H-STGCN, a hybrid spatio-temporal graph convolutional network.
result H-STGCN outperforms state-of-the-art methods in various metrics, especially for non-recurring congestion.
Potential games, originally introduced in the early 1990's by Lloyd Shapley, the 2012 Nobel Laureate in Economics, and his colleague Dov Monderer, are a very important class of models in game theory. They have special properties such as the existence of Nash equilibria in pure strategies. This note introduces graphical…
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.
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temp…