scholarly journals Lagging Behind – The Emerging Influence of Jet Lag Symptoms on Road Safety

2019 ◽  
Vol 7 (2) ◽  
pp. 39-44 ◽  
Author(s):  
Shang Yuin Chai ◽  
Gerard Thomas Flaherty
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Public Awareness ◽  
International Travel ◽  
Multiple Time ◽  
Jet Lag ◽  
Road Traffic Accidents ◽  
Awareness Campaigns ◽  
The Impact

Road traffic accidents are the leading cause of death in international travelers. With the growth of international travel, the number of visitors who rent a vehicle upon arrival at their destination by air or by sea is expected to increase. Jet lag is a well-recognized maladaptation to international travel across multiple time zones. Little is known about the possible influence of jet lag symptoms on the risk of road traffic collisions. Lack of awareness or failure to recognize the debilitating effects of jet lag and driving immediately after a long-haul flight put the driver at risk of a fatal road traffic accident. This article summarizes the available evidence and suggests reasonable approaches which may serve to minimize the risk of jet lag-induced driver fatigue leading to road injuries and deaths. The future research agenda should focus on routinely recording whether jet lag was a co-factor in road traffic accidents, analyzing the effectiveness of public awareness campaigns to highlight this neglected issue, and investigating the impact of jet lag on pedestrian road safety.

scholarly journals Predictive analysis of the impact of the time of day on road accidents in Poland

2020 ◽  
Vol 11 (1) ◽  
pp. 142-150
Author(s):  
Anna Borucka ◽  
Edward Kozłowski ◽  
Piotr Oleszczuk ◽  
Andrzej Świderski
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Time Of Day ◽  
Steady Increase ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
Road Users ◽  
Kolmogorov Smirnov ◽  
The Impact

AbstractThe steady increase in the number of road users and their growing mobility mean that the issue of road safety is still a topical one. Analyses of factors influencing the number of road traffic accidents contribute to the improvement of road safety. Because changes in traffic volume follow a daily rhythm, hour of the day is an important factor affecting the number of crashes. The present article identifies selected mathematical models which can be used to describe the number of road traffic accidents as a function of the time of their occurrence during the day. The study of the seasonality of the number of accidents in particular hours was assessed. The distributions of the number of accidents in each hour were compared using the Kruskal-Wallis and Kolmogorov-Smirnov tests. Multidimensional scaling was used to present the found similarities and differences. Similar hours were grouped into clusters, which were used in further analysis to construct the ARMAXmodel and the Holt-Winters model. Finally, the predictive capabilities of each model were assessed.

scholarly journals Characteristics of Road Traffic Accident Cases Reported in a Tertiary Military Hospital

2020 ◽  
Vol 15 (2) ◽  
pp. 213-217
Author(s):  
Md Moshiur Rahman ◽  
Farzana Zafreen ◽  
Abu Noman Mohammed Mosleh Uddin
Keyword(s):  
Cause Of Death ◽  
Road Safety ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Military Hospital ◽  
Road Traffic Accidents ◽  
Traffic Crashes ◽  
Road Traffic Crashes

Introduction: Road traffic accidents (RTA) are the eighth leading cause of death globally, and the leading cause of death in Bangladesh. The road safety situation in Bangladesh has been deteriorating with increasing number of road accident deaths, largely as direct consequences of rapid growth in population, motorization, urbanization and lack of investment in road safety. Objectives: To describe the injury characteristics of road traffic accidents cases that reported at Combined Military Hospital (CMH), Dhaka. Materials and Methods: This descriptive cross-sectional hospital based study was conducted on all the RTA cases reported to medical inspection room CMH, Dhaka from 01 March to 30 June 2010. A total of 126 RTA victims irrespective of age and sex were included, data were collected by face to face interview using a pre-tested questionnaire. Results: A total of 126 road traffic accident victims both sexes were studied. The majority of the study subjects were men 103 (81.7%) and most of them were in 31-40 years group (27%), Muslims (98.41%) were the majority. In our study regarding educational qualification of RTA victims most of them were SSC qualified (44’4%) and primary level (15.1%). Military services holder (45.2%) was the majority of road traffic crash victims. Bus/Minibus (32.5%) was responsible for the majority of road traffic crashes and motor vehicle passengers (42.9%) were main victims. Laceration & cut injury were (38.1%) and multiple injury (18.3%) were the most common injury. Main roads (49.2%) were the commonest site of the accidents.  Regarding place of occurrence main roads 62 (49.2%) was the most common site and noon 41 (32.5%) was the most common time of occurrence of accidents. Conclusion: Road traffic crashes constitute a major public health problem in our setting and contribute significantly to unacceptably high morbidity and mortality. Urgent preventive measures targeting at reducing the occurrence of road traffic crashes is necessary to reduce the morbidity and mortality resulting from these injuries. Early recognition and prompt treatment of road traffic injuries is essential for optimal patient outcome. JAFMC Bangladesh. Vol 15, No 2 (December) 2019: 213-217

scholarly journals Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models

2021 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Muhammad Anus Hayat Khan ◽  
Ijaz Hussain
Keyword(s):  
Count Data ◽  
Data Model ◽  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Models ◽  
Panel Count Data ◽  
Road Traffic Accidents ◽  
Count Data Models ◽  
Count Data Model

Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.

ABOUT ADDITIONAL INDICATORS ROAD TRAFFIC ACCIDENTS IN THE RUSSIAN FEDERATION

2021 ◽  
pp. 29-34
Author(s):  
О.Н. Кузьмин ◽  
Е.В. Дедюлин
Keyword(s):  
Russian Federation ◽  
Road Safety ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
The Russian Federation ◽  
Network Safety

В статье анализируется информативность основных показателей аварийности, возможные неверные представления о повышении безопасности дорожного движения при снижении количества ДТП, необходимость учета степени безопасности дорожной сети при выборе мер, направленных на повышение безопасности дорожного движения, а также комплесная оценка этих мер и степени безопасности дорожной сети. The article analyzes the informativeness of the main indicators of accidents, possible misconceptions about improving road safety while reducing the number of accidents, as well as the need to take into account the degree of road safety when choosing measures aimed at improving road safety, assessing such measures in combination with the degree of road network safety.

scholarly journals Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1548
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Libor Švadlenka ◽  
Svetlana Čičević ◽  
Aleksandar Trifunović ◽  
Momčilo Dobrodolac
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Fuzzy Inference ◽  
Road Traffic ◽  
Space Perception ◽  
Road Traffic Accidents ◽  
Inference System ◽  
Interval Type ◽  
The Impact

A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

scholarly journals Comparison of road traffic accidents presenting to the emergency department of a teaching hospital before and during lockdown

2021 ◽  
Vol 8 (9) ◽  
pp. 4297
Author(s):  
Siddharth Rao P. S. ◽  
Sumayya Nazneen Sayyada ◽  
Souri Reddy Pyreddy
Keyword(s):  
Teaching Hospital ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Tertiary Care ◽  
Mass Casualties ◽  
Road Traffic Accidents ◽  
Patient Demographics ◽  
Disproportionate Number ◽  
Type Of Injury ◽  
The Impact

Background: Road traffic accidents (RTAs) are a major cause of misery, disability and death globally, with a disproportionate number occurring in developing countries. With COVID-19 reaching pandemic proportion, a nationwide lockdown was announced on 24 March 2020 which resulted in the complete closure vehicular movement. This study aimed to assess the impact of lockdown on the number of RTAs brought to our rural tertiary care teaching hospital situated on National highway number 65.Methods: Medico-legal records were reviewed retrospectively at Kamineni institute of medical sciences hospital. The cases were classified into two groups. The pre-lockdown group included cases reporting to casualty from 1 April 2019 to 31 July 2019. The lockdown group included cases reporting to casualty from 1 April 2020 to 31 July 2020. Patient demographics, type of injury, time of injury, mode of injury were collected for all cases and analysed using simple mathematical tools.Results: There was a significant decrease in the total number of RTAs during lockdown phases 1 and 2 and during unlocking phases 1 and 2 by 52.1%. Bike skid was the most common mode of injury. The highest number of RTAs was observed between 6 am to 6 pm and the most commonly affected gender was male especially in the age group of 15-45 years.Conclusions: RTA numbers can be reduced by strict implementation of traffic rules and better road infrastructure. One positive effect of the measures implemented to control the spread of COVID-19 was the reduction of traffic accidents and mass casualties.

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