Time Series Analysis of Mortalities Resulting from Car Accidents in the Injured Individuals Hospitalized in Shiraz Shahid Rajaee Hospital During 2010 - 2016

Ghaem, Haleh; Hajipour, Mahmoud; Tabatabaee, Hamid Reza; Yadollahi, Mahnaz; Izanloo, Fatemeh

doi:10.5812/traumamon.13573

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…A significant relationship has been reported between road traffic accidents (RTA) and time (e.g., the time of the day) in Yazd, Iran, during the New Year holidays and sum- (21). The mortality rate due to traffic accidents is higher in Iran at midnight and summer (22,23) and in spring and summer in Shiraz (24) than in other times. The present study showed that the speeding rate was higher from midnight to early morning throughout Iran.…”

Section: Discussionmentioning

confidence: 85%

Investigation of the Hourly and Spatial Patterns of Traffic Offenses During March-April 2019 in Iran Using Bivariate Generalized Additive Models and Integrated Nested Laplace Approximation

Fayaz

Abadi

Razzaghi

et al. 2022

Int J High Risk Behav Addict

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Background: The control, management, and prevention of driving accidents and risky driving are regarded as concerns for numerous countries, according to the World Health Organization. In this regard, many technologies, such as count stations, are recommended. They count traffic offenses, such as speeding and unsafe distance, hourly and daily, and have different patterns according to the hour of the day and the location. Objectives: This study aimed to investigate the risky driving behaviors according to traffic offenses in Iran and estimate their hourly and spatial patterns using generalized additive models (GAMs) and stochastic partial differential equation methods. Methods: There were 2,316 count data stations for one month within March-April 2019. This study estimated the hourly average of each traffic offense, Pearson’s and Spearman’s correlations, and the energy statistics for testing the bivariate normal distribution. There are five distributions, such as univariate Poisson, quasi-likelihood Poisson, Gaussian, location-scale Gaussian, and bivariate Gaussian in GAMs, to study the hourly patterns which were compared to the mean squared error (MSE) and correlation. Results: The hourly average of total vehicles and number of speeding and unsafe distance offenses per count station had positive skew distributions with mean values equal to 347 ± 456, 22.5 ± 44.2, and 65.9 ± 150, respectively. The correlation between traffic offenses in most provinces was significant, not large, and different. The GAM with the bivariate Gaussian distribution had the best performance according to the MSE and correlation. It revealed three hourly patterns for count predictions; the first was that speeding is higher than unsafe distances; the second was that unsafe distances are higher than speeding; the third was that speeding and unsafe distances do not have a specific pattern in some hours. The percentage of speeding was higher in the central, northeast, and southeast regions than in other parts of Iran, and the percentage of unsafe distances was higher for the north, northwest, west, and some parts of the southwest than in other parts of Iran, respectively. Conclusions: The hourly pattern of traffic offenses exists and has a complex structure. The spatial pattern of traffic offenses shows the riskiest points in Iran.

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Section: Discussionmentioning

confidence: 85%

Investigation of the Hourly and Spatial Patterns of Traffic Offenses During March-April 2019 in Iran Using Bivariate Generalized Additive Models and Integrated Nested Laplace Approximation

Fayaz

Abadi

Razzaghi

et al. 2022

Int J High Risk Behav Addict

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“…Applications of time series analysis in road traff c mortality and serious injuries are reported in [8], [9], [10], among others.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis for Assessing and Forecasting of Road Traffic Accidents - Case Studies

Popescu

2020

Wseas Transactions on Mathematics

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Road traffic accident is one of the main causes of injuries and fatalities worldwide, serious injuries and mortality in road collisions being a public health problem. The paper gives a overview of time series modeling and forecasting with application in road traffic injuries monitoring. After presenting of the main models and the methodological issues used in Box-Jenkins approach, the paper discusses two case studies, using a multiplicative SARIMA model and an intervention model, for a time series representing the number of mortal traffic accidents in USA, and the road traffic accidents with death and serious injuries in the UK, before and after the imposition of the Arabian embargo in November 1973.

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“…Based on the results of a review of the literature, there is no study that have analyzed traffic accidents in Iran and predicted its trend by 2021. It is of utmost importance to prevent traffic accidents, as it helps to avoid the imposition of costs and the associated adverse effects [10]. In order to implement prevention policies, it is essential to exactly identify the factors and assess the effects of the implementation of the solutions.…”

Section: Introductionmentioning

confidence: 99%

Five-Year Forecasting Deaths Caused by Traffic Accidents in Fars Province of Iran

Yadollahi

Gholamzadeh

2019

BEAT

Self Cite

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Objective:The aim of study was to conduct a time-based analysis to utilize the obtained knowledge for forecasting the trend of accidents in the future.Methods:The present study, which was conducted as a cross-sectional research, investigated deaths from traffic accidents in Fars Province during a five-year period from 2013 to 2018. The pseudo-regression model of Spline was used to predict the increase in mortality rate by 2021. Results:The forecasted values indicated a decline in deaths from traffic accidents by 2021. A total of 8020 records of accidents leading to death were included in the study. The mean mortality rate from traffic accidents in the province was approximately estimated to be 33.7 per 100,000 populations. More than half of the people who died (52.36%) were in a car, 25.57% were motorcycle riders, and 19.93% were pedestrians. The highest rate of deaths was observed in the age group of 16 to 25 years old (21.5%). The data indicated a reduction in the rate of death among car riders and pedestrians and an increase in the number of deaths among motorcycle riders. The trend of deaths occurred outside the city had been increasing while the trend of deaths occurred inside the city had been decreasing. Conclusion:The present regulations are only able to reduce a small number of deaths each year. In order to achieve a downward trend in mortality with a steeper pace, it is necessary to design and implement more intelligent standards, not merely the stricter ones.

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Time Series Analysis of Mortalities Resulting from Car Accidents in the Injured Individuals Hospitalized in Shiraz Shahid Rajaee Hospital During 2010 - 2016

Cited by 4 publications

References 16 publications

Investigation of the Hourly and Spatial Patterns of Traffic Offenses During March-April 2019 in Iran Using Bivariate Generalized Additive Models and Integrated Nested Laplace Approximation

Investigation of the Hourly and Spatial Patterns of Traffic Offenses During March-April 2019 in Iran Using Bivariate Generalized Additive Models and Integrated Nested Laplace Approximation

Time Series Analysis for Assessing and Forecasting of Road Traffic Accidents - Case Studies

Five-Year Forecasting Deaths Caused by Traffic Accidents in Fars Province of Iran

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