Risk-Compensation Trends in Road Safety during COVID-19

Islam, Rakibul; Abdel-Aty, Mohamed; Islam, Zubayer; Zhang, Shile

doi:10.3390/su14095057

Cited by 17 publications

(15 citation statements)

References 49 publications

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“…6 shown indicates clearly how speed affected the situation of road traffic crashes before and after the SARS-CoV-2 pandemic. Even if different studies indicated that road traffic crashes and their outcomes were high during the SARS-CoV-2 pandemic due to the high speed (Islam et al, 2022), this study contradicts, road traffic crashes slightly decreases due to high-speed maneuvers of vehicles when compared before the SARS-CoV-2 pandemic in the city of Budapest. Even though, in both cases, before and after the SARS-CoV-2 pandemic in the city of Budapest, high number of road traffic crashes resulted with a normal speed that ranges from 25 km/h -50 km/h allowed for vehicular maneuvers in urban traffic.…”

Section: Road Traffic Crash and Level Of Alcohol Consumptioncontrasting

confidence: 72%

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Long Term, Pre, and Post Impacts of SARS-CoV-2 Pandemic on Road Traffic Crashes in the Case of Budapest, Hungary

Jima

Sipos

2023

Period. Polytech. Transp. Eng.

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SARS-CoV-2 is a pandemic that affects road traffic flaw and crashes globally. This study attempted to compare the situation of road traffic crashes in the city of Budapest before and after the SARS-CoV-2 pandemic to better understand its long-term percussive effects. The study considers 12208 road traffic crashes that registered between 20 May 2018 – 31 December 2021. The rate and severity of road traffic crashes during the SARS-CoV-2 pandemic examined by using a percentage frequency distribution and a severity index. This study depicted that most crashes reported during the normal daytime between15:01-18:00 (peak hour). The study indicated that during the SARS-CoV-2 pandemic the road traffic crashes were reduced by 20.15%. A rear-end collision was one of the most common type of catastrophes highly registered. Road users, particularly drivers, heavily endorsed crashes. Even though the proportion of road traffic crashes caused by alcohol consumption was modest (6%), the rate of alcohol consumption and its concentration increased slightly during the SARS-CoV-2 pandemic. At the same time the number of crashes caused by high-speed traffic maneuvers reduced. Improper interpretation of road traffic signs, road pavement condition and failure to respect proper sight distance were influential reasons of road traffic crashes among the top. Meanwhile, the distributional impact of careless driving in the aftermath of the SARS-CoV-2 pandemic causes a shift in rank. Therefore, this study proved that during SARS-CoV-2 pandemics road traffic crashes reduced, the rate and concentration of alcohol consumption increased, and careless driving was encouraged.

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Section: Road Traffic Crash and Level Of Alcohol Consumptioncontrasting

confidence: 72%

“…However, the increase in average speed during the SARS-CoV-2 period has an insignificant relationship with crash severity. During the SARS-CoV-2 period, fatal crashes increased while total crashes decreased; severe crashes decreased with the total crashes (Islam et al, 2022).…”

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confidence: 95%

Long Term, Pre, and Post Impacts of SARS-CoV-2 Pandemic on Road Traffic Crashes in the Case of Budapest, Hungary

Jima

Sipos

2023

Period. Polytech. Transp. Eng.

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“…Further, the study defined these three speed–volume clusters as follows: the high-speed and low-volume cluster was defined as high-severity level, the high-speed and high-volume cluster was defined as medium-severity level, and the low-speed and high-volume cluster was defined as low-severity level. The rationale for such definitions is that crashes are more likely to be severe and fatal in the high-speed and low-volume condition, incapacitating and non-incapacitating injuries are expected in the high-speed and high-volume condition, and property damage and low injury are expected to occur in the low-speed and high-volume condition ( 3, 59–61 ). Here, the crash severity levels are as per the KABCO scale, where K refers to a fatal crash, A refers to an incapacitating injury, B refers to a non-incapacitating injury, C refers to a possible injury, and O refers to property damage only ( 3 , 62 ).…”

Section: Resultsmentioning

confidence: 99%

“…The rationale for such definitions is that crashes are more likely to be severe and fatal in the high-speed and low-volume condition, incapacitating and non-incapacitating injuries are expected in the high-speed and high-volume condition, and property damage and low injury are expected to occur in the low-speed and high-volume condition ( 3, 59–61 ). Here, the crash severity levels are as per the KABCO scale, where K refers to a fatal crash, A refers to an incapacitating injury, B refers to a non-incapacitating injury, C refers to a possible injury, and O refers to property damage only ( 3 , 62 ). Finally, we proposed this crash severity clustering model to identify the severity level of a crash based on the speed and volume information collected from u/s detector location 2.…”

Section: Resultsmentioning

confidence: 99%

Real-Time Framework to Predict Crash Likelihood and Cluster Crash Severity

Islam

Abdel-Aty

Islam

et al. 2023

Transportation Research Record: Journal of the Transportation R

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This study proposes a three-stage framework for real-time crash likelihood and severity prediction. Firstly, a real-time crash likelihood prediction model was developed. Secondly, a real-time crash severity clustering model was proposed to cluster the crashes into different severity levels. Thirdly, a severity clustering validation model was developed to assess the performance of the proposed severity clustering model. Extensive data processing techniques were employed to collect real-time features from State Road 408 in Orlando, Florida, and a total of 6,750,072 events (625 crash events and 6,749,447 non-crash events) along with 24 real-time features were used. To develop the crash likelihood prediction model, nine machine-learning techniques were attempted, and the convolutional neural network model was found to provide the best result with respect to the sensitivity (0.916), false alarm rate (0.111), and area under the receiver operating characteristic curve (0.967). Davies–Bouldin index criteria were used to find the detector location that generated the most accurate traffic information to cluster the crashes into severity levels, and based on this traffic information, k-means clustering was applied to develop the severity clustering model. Finally, a severity clustering validation model was developed after investigating nine machine-learning techniques to validate the developed severity clustering model, and the decision tree model provided the best results based on three levels of sensitivity and specificity values. The developed framework has the potential to help traffic management centers to warn road users or develop transportation systems management and operations strategies in real time to avoid crashes or minimize the severity and, thus, can significantly contribute to improving road safety.

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“…Moutari et al [ 12 ] investigated the change in road safety trends from the pandemic, using data from 2017 to 2020 click or tap here to enter text. They focused on an area of Interstate-4 from Daytona beach to Tampa in Florida, United States.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia

Kabbush,

Almannaa,

Alarifi

et al. 2023

Arab J Sci Eng

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The coronavirus disease 2019 (COVID-19) pandemic led to rapid and unexpected changes across the world, particularly in road safety. Thus, this work assesses the impact of COVID-19 accompanied by government preventive policies on road safety in Saudi Arabia by investigating the crash frequency and crash rates. A 4-year crash dataset relating to 2018–2021 was collected, covering about 71,000 km in total road length. It covers all intercity roads and some of the major intercity roads in Saudi Arabia with over 40,000 data logs of involved crashes. We considered three different time phases to observe road safety. These time phases were identified by the duration of government curfew measures against COVID-19 (before, during, and after). The crash frequency analysis showed that the curfew during COVID-19 significantly impacted the crash reduction. At a national level, the crash frequency decreased during 2020 and reached a 33.2% reduction compared to 2019 (the previous year), and it surprisingly continued decreasing in 2021 (the consequent year) to another 37.7% reduction although the government measures were lifted. Moreover, considering the traffic volume and road geometry, we analyzed crash rates for 36 selected segments, and the results showed a significant reduction in the crash rate before and after the COVID-19 pandemic. Additionally, a random effect negative binomial model was developed to quantify the impact of the COVID-19 pandemic. The results showed that the reduction in crashes was significant during and after COVID-19. Also, single roads (two-lane, two-way) were found to be more dangerous than other types of roads.

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Risk-Compensation Trends in Road Safety during COVID-19

Cited by 17 publications

References 49 publications

Long Term, Pre, and Post Impacts of SARS-CoV-2 Pandemic on Road Traffic Crashes in the Case of Budapest, Hungary

Long Term, Pre, and Post Impacts of SARS-CoV-2 Pandemic on Road Traffic Crashes in the Case of Budapest, Hungary

Real-Time Framework to Predict Crash Likelihood and Cluster Crash Severity

Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia

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