A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks

Pitombeira‐Neto, Anselmo Ramalho; Loureiro, Carlos Felipe Grangeiro; Carvalho, Luís

doi:10.1007/s11067-019-09490-5

Cited by 11 publications

(4 citation statements)

References 61 publications

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“…Origin-destination matrices have also been used to measure the volume of use of public transport 16 17 , 18 and public transport planning 19 , 20 . Some approaches for modeling and generating origin-destination matrices include gravity models 21 – 23 , Bayesian models 24 , 25 , 26 , linear assignment matrix approximation 27 , Principal Components Analysis 28 and gradient approximation method 29 , among others.…”

Section: Introductionmentioning

confidence: 99%

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Fontanelli

Guzmán²,

Viveros

et al. 2023

Sci Rep

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Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society’s local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020–2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

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Section: Introductionmentioning

confidence: 99%

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Fontanelli

Guzmán²,

Viveros

et al. 2023

Sci Rep

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“…Our work is based on hierarchical Bayesian modelling, which has been extensively reviewed, for instance, in chapters 5 and 15 of Gelman et al (2014). Hierarchical Bayesian models (HBMs) have been applied in a broad and diverse range of research and development areas, including healthcare medicine (see Kantorová et al, 2020), transportation networks (see Pitombeira-Neto et al, 2020), clinical trials (see Chu & Yuan, 2018), water resources (see Bracken et al, 2018) and economics (see Meager, 2019). Moreover, HBMs have been applied widely in medical imaging, for example DCE-MRI.…”

Section: Introductionmentioning

confidence: 99%

Classification of Myocardial Blood Flow based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging using Hierarchical Bayesian Models

Yang

Gao

Berry

et al. 2022

Journal of the Royal Statistical Society Series C: Applied Statistics

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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising approach to assess microvascular blood flow (perfusion) within the myocardium, and the Fermi microvascular perfusion model is widely applied to extract estimates of the myocardial blood flow (MBF) from DCE-MRI data sets. The classification of myocardial tissues into normal (healthy) and hypoperfused (lesion) regions provides new opportunities for the diagnosis of coronary heart disease and for advancing our understanding of the aetiology of this highly prevalent disease. In the present paper, the Fermi model is combined with a hierarchical Bayesian model (HBM) and a Markov random fields prior to automate this classification. The proposed model exploits spatial context information to smooth the MBF estimates while sharpening the edges between lesions and healthy tissues. The model parameters are approximately sampled from the posterior distribution with Markov chain Monte Carlo (MCMC), and we demonstrate that this enables robust classification of myocardial tissue elements based on This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…By combining message propagation, a traffic distribution messaging mechanism on a network for the random network is proposed. However, this convergence is not guaranteed in graphs with rings and its computation is very difficult for complex models with non-Gaussian continuity variables [6]. For this reason, a nonparametric belief propagation algorithm is proposed by combining the ideas of Monte Carlo and particle filtering for modeling uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

Zhou¹

2022

Discrete Dynamics in Nature and Society

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In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource-sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.

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A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks

Cited by 11 publications

References 61 publications

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Classification of Myocardial Blood Flow based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging using Hierarchical Bayesian Models

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

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