Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

Alghamdi, Taghreed; Elgazzar, Khalid; Sharaf, Taysseer

doi:10.3390/fi13090225

Cited by 2 publications

(1 citation statement)

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“…Assume that the attribute set of the shared scheduling node graph model of public library's massive resource base under the network environment and cultural construction is [s], and use the decision tree and directed graph analysis model to split the binary tree of public library's massive data under the network environment and cultural construction, so that we can get different attribute sets and the distributed storage structure model of public library's massive resources under the condition of network environment and culture construction is shown in Figure 1. In Figure 1, the distribution pattern of public library's massive resource sharing scheduling chart under the condition of network environment and culture construction consists of nodes and edges [10][11][12]. rough principal component analysis and modeling method, a hierarchical adaptive hierarchical planning model of public library's massive resources under the condition of network environment and culture construction is formed, and the dataset classification and information fusion of public library's massive resources under the condition of network environment and culture construction are carried out in combination with statistical feature distribution.…”

Section: Distribution Of Scheduling Nodes For Mass Resource Sharing I...mentioning

confidence: 99%

Cloud Computing-Based Online Sharing Method of Mass Resources in Public Libraries

Jun

2022

Mobile Information Systems

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In order to realize the online sharing of massive resources and capitals in public libraries under the circumstances of network environment and cultural construction, it is desirable to invent new sharing policies. In this paper, we put forward a method of online sharing of massive resources in public libraries which is established on cloud computing. Cloud computing technique is implemented to collect the statistical information characteristics of public library’s massive resources under the condition of network environment and culture construction, and an adaptive fusion clustering processing model of public library’s massive resources under the condition of network environment and culture construction is constructed. Moreover, the correlation statistical feature detection approach is used to accomplish fuzzy scheduling and feature mining of public library’s massive resources under the condition of network environment and culture construction. The information storage center management database of public library’s massive resources under the condition of network environment and culture construction is established. The fuzzy C-means clustering approach is implemented in the database to cluster the massive resources of public library under the condition of network environment and culture construction. Combined with cloud computing technology, the sharing and classified management scheduling of massive resources of public library under the condition of network environment and culture construction are comprehended. The simulation outcomes confirm that the adaptive performance of public library’s massive resource sharing management under the condition of network environment and culture construction is better, and the adaptive scheduling performance of resources is better. The data clustering of public library’s massive resources under the condition of network environment and culture construction is stronger, which improves the online sharing level of public library’s massive resources under the condition of network environment and culture construction.

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Section: Distribution Of Scheduling Nodes For Mass Resource Sharing I...mentioning

confidence: 99%

Cloud Computing-Based Online Sharing Method of Mass Resources in Public Libraries

Jun

2022

Mobile Information Systems

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High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia

Jaya,

Folmer

2024

Mathematics

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Accurate forecasting of high-resolution particulate matter 2.5 (PM2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approach to handle this problem. The first stage is a multivariate spatial time series (MSTS) model, used to generate forecasts for the sampled spatial units and to impute missing observations. The MSTS model utilizes the similarities between the temporal patterns of the time series of the spatial units to impute the missing data across space. The second stage is the high-resolution prediction model, which generates predictions that cover the entire study domain. The second stage faces the big N problem giving rise to complex memory and computational problems. As a solution to the big N problem, we propose a Gaussian Markov random field (GMRF) for innovations with the Matérn covariance matrix obtained from the corresponding Gaussian field (GF) matrix by means of the stochastic partial differential equation (SPDE) method and the finite element method (FEM). For inference, we propose Bayesian statistics and integrated nested Laplace approximation (INLA) in the R-INLA package. The above approach is demonstrated using daily data collected from 13 PM2.5 monitoring stations in Jakarta Province, Indonesia, for 1 January–31 December 2022. The first stage of the model generates PM2.5 forecasts for the 13 monitoring stations for the period 1–31 January 2023, imputing missing data by means of the MSTS model. To capture temporal trends in the PM2.5 concentrations, the model applies a first-order autoregressive process and a seasonal process. The second stage involves creating a high-resolution map for the period 1–31 January 2023, for sampled and non-sampled spatiotemporal units. It uses the MSTS-generated PM2.5 predictions for the sampled spatiotemporal units and observations of the covariate’s altitude, population density, and rainfall for sampled and non-samples spatiotemporal units. For the spatially correlated random effects, we apply a first-order random walk process. The validation of out-of-sample forecasts indicates a strong model fit with low mean squared error (0.001), mean absolute error (0.037), and mean absolute percentage error (0.041), and a high R² value (0.855). The analysis reveals that altitude and precipitation negatively impact PM2.5 concentrations, while population density has a positive effect. Specifically, a one-meter increase in altitude is linked to a 7.8% decrease in PM2.5, while a one-person increase in population density leads to a 7.0% rise in PM2.5. Additionally, a one-millimeter increase in rainfall corresponds to a 3.9% decrease in PM2.5. The paper makes a valuable contribution to the field of forecasting high-resolution PM2.5 levels, which is essential for providing detailed, accurate information for public health policy. The approach presents a new and innovative method for addressing the problem of missing data and high-resolution forecasting.

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Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

Cited by 2 publications

References 50 publications

Cloud Computing-Based Online Sharing Method of Mass Resources in Public Libraries

Cloud Computing-Based Online Sharing Method of Mass Resources in Public Libraries

High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia

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