Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population

Yang, Tengfei; Zeng, Haotian; Yang, Xing-An; Kong, Jiangwei; Chen, Xinzhe; Zhou, Nian; Zhao, Ziwei; Miao, Qifeng; Zhao, Weidong; Zhang, Fu; Liao, Xinbiao; Yu, Yan-Geng; Lan, Fengchong; Wang, Huijun; Li, Dongri

doi:10.1016/j.heliyon.2023.e12822

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Accident-causing factors include human characteristics, vehicles, road characteristics, and environmental conditions, such as rush hour and school term time in the morning. [20][21][22][23] Transportation Research Information Services (TRIS)…”

Section: Discussionmentioning

confidence: 99%

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Joomjee,

Raksin,

Paengprakhon

et al. 2024

F1000Res

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Introduction The importance of understanding the factors contributing to road accidents at schools cannot be overstated. This study aims to determine the risk of accidents in situations that could lead to accidents near schools. Methods A total of 10 kindergarten to junior high schools were selected for the study. The research used the Haddon Matrix to classify factors at risk of accidents, risk assessment through fault tree analysis, and the analytic hierarchy process (FTA-AHP) techniques. Fourteen sub-criteria were defined for risk identification, risk probability analysis, and risk assessment of the 10 roads. The likelihood of each event was analyzed using the AHP technique for all schools with an expert choice program. RI (random index) was calculated, and CR (consistency ratio) < 0.10 was considered satisfactory. Results The possibility of human accidents ranked highest in three areas: 1) Risk perception in SC 01, 03, and 02, with probabilities of 69.30%, 61.90%, and 57.4%, respectively. 2) The likelihood of accidents from vehicles/equipment, with the highest probabilities in a) Handling (SC01) at 64.70%, b) Braking (SC07) at 61.90%, and c) Lighting (SC03) at 57.80%. 3) The likelihood of accidents from the environment, with the highest probabilities in 1) driving at excessive speeds in areas SC01, 06, 03, and 09, which were 43.60%, 40.90%, and 40.00%, respectively. Conclusions The impacts of all three main factors were as follows: a) humans had the highest impact in the SC01 area (77.90%), b) vehicles/equipment had the highest impact in the SC01 area (75.90%), and c) the highest environmental impact in the SC01 area was 69.90%. The accident risk assessment revealed the highest risk score in three areas: 1) human risk perception, 2) environment with driving at excessive speeds, and 3) vehicle/equipment, including lighting, braking, and handling.

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Section: Discussionmentioning

confidence: 99%

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Joomjee,

Raksin,

Paengprakhon

et al. 2024

F1000Res

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“…The subjects were people who suffered RTCs in Guangzhou between 2017 and 2021 and were divided into migrant worker and control groups. Migrant workers were defined as migrant labourers with a non-urban registration, including registered labourers from other towns and rural areas outside the Guangzhou area, and were a special group under the Chinese registration system 23 ; migrant workers from other countries were excluded. The control group was defined as mainly local residents of Guangzhou and a small number of college students and businessmen from other provinces.…”

Section: Methodsmentioning

confidence: 99%

“…Our previous research 23 found that migrant workers are not only the main victims of RTCs but also have an important relationship with their occurrence. Therefore, we believe that it is of great practical importance to systematically study the role and characteristics of migrant workers in RTIs.…”

Section: Introductionmentioning

confidence: 99%

Overview of road traffic injuries among migrant workers in Guangzhou, China, from 2017 to 2021

Yang,

Kong,

Chen

et al. 2023

Inj Prev

Self Cite

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IntroductionThere are many migrant workers in China’s first-tier cities, but little is known about road safety. This paper systematically analysed road traffic injuries and risk factors among migrant workers in Guangzhou, China.MethodsRoad traffic crash data from 2017 to 2021 were obtained from the Guangzhou Public Security Traffic Management Integrated System. We plotted the crash network of road users in road traffic crashes and used logistic regression to analyse the risk factors for migrant workers of motorcycle and four-wheeled vehicle crashes. Moreover, the roles of migrant workers and control individuals as perpetrators in road traffic crashes were also analysed.ResultsBetween 2017 and 2021, 76% of road traffic injuries were migrant workers in Guangzhou. Migrant workers who were motorcyclist drivers most commonly experienced road traffic injuries. Crashes between motorcyclists and car occupants were the most common. The illegal behaviours of migrant worker motorcyclists were closely related to casualties, with driving without a licence only and driving without a licence and drunk driving accounting for the greatest number. Migrant workers were responsible for many injuries of other road users. Motorcycle drivers have a higher proportion of drunk driving.DiscussionMigrant workers play an important role in road traffic safety. They were both the leading source of road traffic injuries and the main perpetrators of road traffic crashes. Measures such as strict requirements for migrant workers to drive motorcycles with licences, prohibit drunk driving, greater publicity of road safety regulations, and combining compulsory education with punishment for illegal behaviours.

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“…Moreover, road traffic accidents have emerged as the primary cause of death for children and young adults globally ( 2 ). Road traffic accidents in China result in more than 250,000 deaths annually, accounting for approximately 19% of the total deaths worldwide( 3 ). Investigating the potential factors influencing road traffic accidents is essential for reducing and preventing them.…”

mentioning

confidence: 99%

“…The current global annual death toll from road traffic accidents is approximately 1.3 million people. Moreover, road traffic accidents have emerged as the primary cause of death for children and young adults globally (2). Road traffic accidents in China result in more than 250,000 deaths annually, accounting for approximately 19% of the total deaths worldwide (3).…”

mentioning

confidence: 99%

The Relationship Between the Atmospheric Environment and Road Traffic Fatalities — Shandong Province, China, 2012–2021

Wang,

Chu,

Yao

et al. 2024

China CDC Weekly

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Introduction This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities. Methods Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson’s correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities. Results From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM 10 ) and nitrogen dioxide (NO 2 ) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO 2 ) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers. Conclusions Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM 10 , NO 2 , and SO 2 .

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Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population

Cited by 11 publications

References 27 publications

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Overview of road traffic injuries among migrant workers in Guangzhou, China, from 2017 to 2021

The Relationship Between the Atmospheric Environment and Road Traffic Fatalities — Shandong Province, China, 2012–2021

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