arXiv research

Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.

Study predicts traffic congestion based on population mobility data.

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…

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…

Study of congestion in negative curvature manifolds using fair-division algorithms.

Predicts morning traffic congestion using social media data from the previous evening.

Dynamic linear models improve travel time prediction for congested freeways.

Online learning improves traffic congestion prediction over time.

New framework analyzes LLM personalization trade-offs under congestion.

Model traffic congestion events using multi-modal data and attention-based neural networks.

The paper tackles Nash-regret minimization in congestion games with bandit feedback.

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…

New method estimates traffic congestion delays using statistical causality.

Wasserstein GANs with Gradient Penalty compute a different optimal transport problem called congested transport.

Proposes active learning for meta-learning in graph node response prediction.

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 …

NAC-FL optimizes model updates in FL systems by adapting compression to network congestion.

New algorithm for traffic routing in congested conditions.

Paper uses DeePC to improve urban traffic lights, reducing congestion and 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…

Study on-chain peak shaving to reduce Ethereum transaction costs.

H-STGCN predicts traffic using navigation data and improves accuracy.

EUREKA builds classifiers that use surprising features.

Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.

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 …

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…

This paper connects deep neural networks to game theory, revealing their congestion game properties.

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…

Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.

This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.

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…

Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco…

A new framework reduces traffic congestion by 36%.

Service-induced congestion in memory-constrained LLM serving

RL techniques improve radar spectrum sharing in crowded conditions.

Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their highly dimensional and heterogeneous nature. Low-dimensional representations of EHR data can potentially mitigate these challenges. In this pap…

Study develops a machine learning-based ramp metering model to improve freeway efficiency.

Proposes three decentralized multi-agent reinforcement learning algorithms to reduce network congestion.

This study develops methods to coordinate travel routes to reduce congestion.

Backdoor attacks on DRL-based traffic controllers cause stop-and-go waves or crashes.

Graph neural networks improve topology control of power grids.

Deep learning predicts frame errors in CIRN using SC2 dataset.

Model predicts and optimizes trading of electricity price spreads across multiple zones.

We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which as…

Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…

Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have t…

The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embe…