Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints

Agyemang, Edmund F.; Mensah, Joseph A.; Ocran, Eric; Opoku, Enock; Nortey, Ezekiel N.N.

doi:10.1016/j.heliyon.2023.e22544

Cited by 5 publications

(1 citation statement)

References 56 publications

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“…Cheng et al [13] employed the SARIMA time-series-forecasting model to forecast the price trends of Bitcoin. Agyemang et al [14] utilized the SARIMA time-series forecasting model to predict the occurrence of road traffic accidents. Cui et al [15] applied the SARIMA time-series-forecasting model to the prediction of carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model to Predict the Seismic Lifecycle Behavior of a Cross-Sea Cable-Stayed Bridge

Lu,

Liu,

Zhang

2024

Buildings

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Cross-sea cable-stayed bridges encounter challenges associated with cable corrosion and cable-force relaxation during their service life, which significantly affects their structural performance and seismic response. This study focuses on a cross-sea cable-stayed bridge located in Hainan Province. Utilizing an LSTM deep learning model, this study aims to fill in the gaps in short-term cable-monitoring data from the past year using the available cable-force-monitoring data from the same period. The authors of this study interpolated the cable-force data in the absence of sensors and employed a SARIMA machine learning time-series-prediction model to predict the future trends of all cable forces. A finite-element model was constructed, and a dynamic time-history analysis of the seismic response of the cross-sea cable-stayed bridge was conducted, considering the influence of cable-force relaxation and cable corrosion in the future. The findings indicate that the LSTM-SARIMA model predicted an average decrease of 11.81% in the cable force of the cable-stayed bridge after 20 years. During the lifecycle of the cables, cable corrosion exerts a significant impact on the variation in cable stress within the bridge structure during earthquakes, while cable-force relaxation has a more pronounced effect on the vertical displacement of the main beam of the bridge structure during seismic events. Compared to when using the traditional model that only considers cable corrosion, the maximum negative vertical displacement of the main beam increases by 29.7% when using the proposed model if the earthquake intensity is 0.35 g after 20 years, which indicates that the proposed machine learning model can exactly determine the seismic behavior of the lifecycle cross-sea cable-stayed bridge, considering the impacts of both cable-force relaxation and cable corrosion.

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Section: Introductionmentioning

confidence: 99%