Short-term traffic prediction using physics-aware neural networks

Pereira, Mike; Lang, Annika; Kulcsár, Balázs

doi:10.1016/j.trc.2022.103772

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…The Artificial Neural Network (ANN) [29,30] is a data-mapping model that emulates the functioning of the human brain. It can effectively map complex causal relationships through adaptive learning from extensive sample data.…”

Section: Model Improvementmentioning

confidence: 99%

An Airfield Area Layout Efficiency Analysis Method Based on Queuing Network and Machine Learning

Chen,

Chong,

Liu

et al. 2024

Buildings

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The layout design of an airfield area plays a crucial role in ensuring the efficiency of aircraft ground operations. In order to minimize delays caused by insufficient capacity and prevent resource wastage due to excessive capacity during the operational phase, this paper developed a prediction model for operational efficiency leveraging queuing network theory and machine-learning models. Our approach involves four key steps: (1) establish a theoretical framework for analyzing the operational efficiency of airfield area layouts based on queuing network theory, (2) employ a combination of discrete modeling and multi-agent modeling to construct a simulation model for ground operations in the airfield area, (3) develop a prediction model, known as PSO-ANN, for forecasting the operational efficiency of the airfield area using the simulation results, (4) conduct computer-based simulation experiments to assess the sensitivity of airfield area parameters, observe traffic-flow phase transitions, and investigate the factors influencing operational efficiency. This methodology enables the rapid assessment of operational efficiency for small- and medium-sized airports, as well as regional multi-airport systems. It is particularly useful for program evaluation during the strategic planning phase.

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Section: Model Improvementmentioning

confidence: 99%

An Airfield Area Layout Efficiency Analysis Method Based on Queuing Network and Machine Learning

Chen,

Chong,

Liu

et al. 2024

Buildings

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“…The use of neural networks is an area of growing interest, especially due to the availability of large quantities of information from multiple sources [20]. The algorithms of Artificial Intelligence (AI) have been used frequently to model complex functions for which the traditional mathematical methods no longer suffice.…”

Section: Related Workmentioning

confidence: 99%

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Rocha¹,

Zela²,

Torres³

et al. 2022

IJACSA

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Artificial neural networks are mathematical models of artificial intelligence that intend to reproduce the behavior of the human brain and whose main objective is the construction of systems that are capable of demonstrating certain intelligent behavior. The purpose of the investigation is to determine the influence of the use of Information and Communication Technologies (ICTs) in the digital culture in the learning process of university students in Peru and Bolivia in the context of the Coronavirus -COVID 19 sanitary emergency, through the application of artificial neural network models. The investigation has a quantitative focus, the applied type, with a correlational level and a non-experimental design. Data was recollected by means of a digital questionnaire, applied to students of two universities. The population is composed of 3980 students of the Universidad Privada Domingo Savio (UPDS, Tarija, Bolivia) and 1506 of the Universidad Nacional de Moquegua (UNAM, Moquegua, Peru). The sample consists of 496 students. The hypothetical-deductive and the artificial intelligence methods were used. It was determined that the ability to install software and data protection programs, the use of mobile devices for academic purposes and the command of specialized software are the most influential factors in the digital culture of the students at UNAM and UPDS.

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“…Deep Artificial Neural Networks (ANNs) are particularly useful tools in machine learning that are commonly trained with gradient descent methods (GD). ANN architectures have made strides in recent years in solving complicated tasks, like image recognition, 1 natural language processing, 2 artificial image generation, 3 and other engineering tasks 4,5 where classical statistical models might struggle. Problems in the domain of Control Theory have also had a surge in ANN and ML related works 6–8 .…”

Section: Introductionmentioning

confidence: 99%

Controlled descent training

Andersson,

Varga,

Szolnoky

et al. 2024

Intl J Robust & Nonlinear

Self Cite

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SummaryIn this work, a novel and model‐based artificial neural network (ANN) training method is developed supported by optimal control theory. The method augments training labels in order to robustly guarantee training loss convergence and improve training convergence rate. Dynamic label augmentation is proposed within the framework of gradient descent training where the convergence of training loss is controlled. First, we capture the training behavior with the help of empirical Neural Tangent Kernels (NTK) and borrow tools from systems and control theory to analyze both the local and global training dynamics (e.g., stability, reachability). Second, we propose to dynamically alter the gradient descent training mechanism via fictitious labels as control inputs and an optimal state feedback policy. In this way, we enforce locally optimal and convergent training behavior. The novel algorithm, Controlled Descent Training (CDT), guarantees local convergence. CDT unleashes new potentials in the analysis, interpretation, and design of ANN architectures. The applicability of the method is demonstrated on standard regression and classification problems.

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Short-term traffic prediction using physics-aware neural networks

Cited by 12 publications

References 24 publications

An Airfield Area Layout Efficiency Analysis Method Based on Queuing Network and Machine Learning

An Airfield Area Layout Efficiency Analysis Method Based on Queuing Network and Machine Learning

The Use of ICTs in the Digital Culture for Virtual Learning of University Students Applying an Artificial Neural Network Model

Controlled descent training

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