Forecasting road accidental deaths in India: an explicit comparison between ARIMA and exponential smoothing method

Swain, Prafulla Kumar; Tripathy, Manas Ranjan; Agrawal, Khushi

doi:10.1080/17457300.2023.2225168

Cited by 1 publication

(1 citation statement)

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“…The results of this article allow us to develop more effective road safety plans taking into account these seasonal events. In [6][7], the application of the ARIMA model for predicting deaths in RTAs in India is investigated and the growth of RTAs is predicted, which emphasizes the relevance of these studies. In the article [8], the authors considered the time series of RTAs in Serbia (Belgrade) using the SARIMA model and as a result found that the time series has a pronounced seasonal character.…”

Section: Introductionmentioning

confidence: 99%

Analyzing and forecasting road traffic accidents and their consequences: a case study of the Udmurt republic

Vologdin,

Kasatkina,

Kasatkin

2024

E3S Web Conf.

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The article addresses the issue of road traffic accidents and their impact on population mortality and socio-economic processes in the region. Attention is given to the tasks of accident prediction and the assessment of the severity of their consequences. The dynamics of road accidents and their consequences in Udmurt Republic are analyzed using time series. The research reveals variations in the number of accidents over different time periods and seasonal fluctuations. Econometric models are constructed to predict the number of accidents and the severity of their consequences. These models take into account seasonal components, trends, and autoregressive processes. The forecasts for the number of accidents and their severity enable the development of measures for traffic management and road safety.

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Section: Introductionmentioning

confidence: 99%