ABSTRACT
This study analyzes crash data from 2016 to 2020 on a National Highway in Maharashtra, India. The impact of the COVID-19 lockdown on the road crashes of the study area is presented, and recommendations to improve road safety are proposed. The crash data is collected from the "National Highways Authority of India, Kolhapur” from 2016 to 2020, and the information is classified into three scenarios: Before Lockdown, After Lockdown, and Strict Lockdown. The crash data is analyzed under three scenarios for seven different classifications followed by their sub-classifications. The time-wise analysis of crash data is performed in four-time slots, namely 00:00–05:59 AM, 06:00–11:59 AM, 12:00–17:59 PM, and 18:00–23:59 PM. The season-wise analysis of crash data is performed in three seasons: Summer, Monsoon, and Winter. The crashes that occurred on 2-lane-straight roads having T-junction are more than 90% in all three scenarios. The significant factors responsible for crashes are "Head-on collision,” "Vehicle out of control,” and "Overspeeding.” Most crashes (more than 36%) occurred between 12:00 and 17:59 PM and in the Summer season (more than 42%) in all three scenarios. The crashes in the COVID-19 "Strict Lockdown” scenario witnessed a fall of 254.55% compared to 2019 and 2018. Surprisingly, there was a rise of 137.5% and a fall of 127.27% in crashes of the COVID-19 2020 "Strict Lockdown” scenario, compared to 2017 and 2016, respectively. The crashes under the sub-classifications "Right angle collision” and "Fatal” increased in 2020 compared to the previous 4 years due to the impact of COVID-19. © 2023, The Author(s).
ABSTRACT
Modeling traffic-accident frequency is a critical issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objectives of this study are to model traffic road accidents, fatalities and injuries in Jordan, using different modeling techniques, including regression, artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models and to evaluate the safety impact of travel-restriction strategies during Covid-19 pandemic on traffic-accident statistics for the year 2020. To accomplish these objectives, data of traffic accidents, registered vehicles (REGV), population (POP) and economic gross domestic product (GDP) from 1995 through 2020 were obtained from related sources in Jordan. The analysis revealed that accidents, fatalities and injuries have an increasing trend in Jordan. Root mean of square error (RMSE), mean absolute error (MAE) and coefficient of multiple determination (R2) were sued to evaluate the performance of the developed prediction models. Based on model performance, the ANN models are the best, followed by the ARIMA models and then the regression models. Finally, it was concluded that the strategies undertaken by the government of Jordan to combat Covid-19, including complete and partial banning of travel, resulted in a considerable reduction of accidents, injuries and fatalities by about 35%, 37% and 50%, respectively. © 2023, Jordan University of Science and Technology. All rights reserved.
ABSTRACT
People's travel has changed greatly under the impact of COVID-19. However, it is controversial that whether traffic restrictions of COVID-19 have a positive or negative impact on traffic accidents. At present, there are few studies on the variations of traffic accidents under the impact of COVID-19 in China, and quantitative analysis is rare. Therefore, this study explores the traffic accidents characteristics of W city seriously affected COVID-19. Based on wavelet transform, traffic accident prediction model is established using property damage only accidents data to predict accident frequency without the impact of COVID-19. Compared with the actual traffic accidents frequency, this paper quantitatively analyzes the impact of COVID-19 on traffic accident. The results show that traffic accidents show a trend of decline-bottom-recovery;the frequency of accidents after the recovery is more than the previous year's level;compared with other periods in 2020, the proportion of injury accidents increased sharply during the period when traffic restrictions were gradually loose. The result of accident prediction shows that BP neural network has the best prediction effect. After the implementation of traffic restrictions, the frequency of accidents shows three stages: rapid decline, bottom and continuous rise. In the three stages, the frequency of property damage only accidents decreased by 379.06, 654.72 and 288.19 per day on average. © 2022 IEEE.
ABSTRACT
This research evaluated the effect of the COVID-19 social isolation orders on traffic volume, traffic violations and road crashes in the city of Fortaleza, Brazil. Using data from automated traffic enforcement cameras, a reduction in traffic volume between 30% and 50% was observed during the social isolation period. However, even with the traffic volume reduction, the absolute number of speeding and red-light running violations were 13% and 26% higher than prepandemic levels, respectively. When controlling for traffic exposure, the violation rates increased by more than 100%. After social isolation restrictions were lifted and the traffic volumes returned to prepandemic levels, both traffic violations and traffic violation rates remained at elevated levels (14% to 44% higher than prepandemic levels), possibly related to a nationwide decision that delayed the issuing of violation tickets. Using an interrupted time-series approach and segmented Poisson and negative binomial regression models, it was found that the fatal crash rate was 1.66 times greater during the period of social isolation compared to the prepandemic levels but returned to prepandemic levels following the removal of the social isolation restrictions. A significant reduction in injury crash rate was observed during and following the period of social isolation restrictions;however, the authors hypothesize that this is related to injury crash underreporting during the pandemic. © 2022 American Society of Civil Engineers.
ABSTRACT
Currently, the major causes of death in Ecuador are, among others, the disease known as Covid-19 and traffic accidents. Therefore, the study aims to compare lethality and mortality rates due to Covid-19 and traffic accidents. A cross-sectional descriptive study was carried out based on official records of bulletins and infographics of Covid-19 reported by the Ministry of Public Health (MSP) and the National Transit Agency (ANT) from March 2020 to March 2021. From these, lethality rates (×1,000 inhabitants) and mortality rates (×100,000 inhabitants) were calculated while statistical comparisons were made using the t-student and Mann-Whitney tools. According to estimates, in May 2020 a higher number of deaths from Covid-19 occurred, reaching a mortality rate of 1.4 (×100,000 inhabitants) while the mortality rate for traffic accidents reached 1.1 (×100,000 inhabitants) in December 2020, although the mortality and lethality rate is higher in traffic accidents in the studied interval it is not part of this study to minimize the problems that exist due to the pandemic generated by the coronavirus. The results of this study highlight the need to create social awareness about the benefits of taking control measures and creating safe practices to mitigate the number of deaths in Ecuador due to these causes. © 2022, Springer Nature Switzerland AG.
ABSTRACT
This article analyzes the impact of pandemic COVID-19 on statistics of traffic accidents in the Slovak republic in the time period from the 1st of Jan 2014 until the 31st of Dec of 2020. The article is the definition of differences between case fortuity and traffic accident according to valid legislation in Slovak republic, discripted pandemic situation in Slovak republic and intensity of traffic too. The next added value of the article is complete analyzes of all traffic accidents, traffic accidents with consequences for life or health and traffic accidents caused by drinking alcoholic beverages. © 2021 Kaunas University of Technology. All rights reserved.