scholarly journals Modelling Deaths Associated with Road Traffic Accidents and other Factors on Great North Road in Zambia between the Years 2010 and 2016 Using Poisson Models

2019 ◽  
Vol 12 (1) ◽  
pp. 68-77
Author(s):  
Ronald Fisa ◽  
Chola Nakazwe ◽  
Charles Michelo ◽  
Patrick Musonda
Keyword(s):  
Incidence Rate ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
World Health ◽  
P Value ◽  
Road Traffic Accidents ◽  
Poisson Models ◽  
Health Organization ◽  
Best Fit

Background: According to the World Health Organization (WHO), 1.24 million people die annually on the world’s roads, with 20-50 million sustaining non-fatal injuries. More than 85% (1.05 million) of the global deaths due to injuries occur in the developing world. Road traffic deaths and injuries are a major but neglected public health challenge that requires concerted efforts for effective and sustainable prevention. The objectives of the study were to estimate the incidence rate of death from RTAs, to determine factors associated with serious and fatal Road Traffic Accidents (RTAs) and to determine which of the poisson models fit the count data better. Methods: Data was collected from Zambia Police (ZP), Traffic Division on accidents that occurred on the Great North Road (GNR) highway between Lusaka and Kapiri-Mposhi in Zambia from January 1, 2010 to December 31, 2016. Results from standard Poisson regression were compared to those obtained using the Negative Binomial (NB), Zero-Truncated Negative Binomial (ZTNB) and the Zero-Truncated Poisson (ZTP) regression models. Diagnostic tests were used to determine the best fit model. The data was analysed using STATA software, version 14.0 SE (Stata Corporation, College Station, TX, USA). Results: A total of 1, 023 RTAs were analysed in which 1, 212 people died. Of these deaths, 82 (7%) were Juveniles and 1, 130 (93%) were adults. Cause of accident such as pedestrians crossing the road accounted for 30% (310/1,023) while 29% (295/1,023) were as a result of driver’s excessive speed. The study revealed that driving in the early hours of the day (1AM-6AM) as compared to driving in the night (7PM-12AM) had a significant increase in the incidence rate of death from RTAs, Incidence Rate Ratio (IRR) of 2.1, (95% CI={1.01-4.41}), p-value=0.048. Results further showed that public transport as compared to private transport had an increased incidence rate of death from RTAs (IRR=5.65, 95% CI={2.97-10.73}), p-value<0.0001. The two competing models were the ZTP and the ZTNB. The ZTP had AIC=1304.55, BIC= 1336.55, whereas the ZTNB had AIC=742.25 and BIC=819.69. This indicated that the ZTNB with smaller AIC and BIC was the best fit model for the data. Conclusion: There is a reduced incidence of dying if one is using a private vehicle as compared to a public vehicle. Driving in the early hours of the day (1AM and 6AM) had an increased incidence of death from RTAs. This study suggests that when dealing with counts in which there are a few zeros observed such as in serious and fatal RTAs, ZTNB fits the data well as compared to other models.

scholarly journals Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model

2018 ◽  
Vol 5 (9) ◽  
pp. 3962
Author(s):  
Manikandan M. ◽  
Vishnu Prasad R. ◽  
Amit Kumar Mishra ◽  
Rajesh Kumar Konduru ◽  
Newtonraj A.
Keyword(s):  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Series Data ◽  
Road Traffic Accidents ◽  
Increasing Trend ◽  
Health Organization

Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.

scholarly journals Identification of Risk Factors Influencing Road Traffic Accidents

2018 ◽  
Vol 8 (1) ◽  
pp. 2417-2421 ◽  
Author(s):  
M. Touahmia
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Public Awareness ◽  
Seat Belt ◽  
World Health ◽  
Road Traffic Accidents ◽  
The World ◽  
Traffic Law Enforcement ◽  
Health Organization

Road traffic accidents (RTAs) are becoming a major problem around the world, incurring enormous losses of human and economic resources. Recent reports from the World Health Organization (WHO) reveal that each year more than 1.25 million people are killed and 50 million are injured in road traffic accidents worldwide. In Saudi Arabia, statistics show that at least one traffic accident occurs every minute, causing up to 7,000 deaths and over 39,000 injuries annually. In this study, the main causes of RATs in the province of Hail are examined. The data was collected through the use of a survey which was developed to evaluate the effect of influencing parameters on RTA rate. The results show that 67% of RTAs result from human factors, 29% from road conditions and 4% from vehicle defects. Excessive speed and violation of traffic rules and regulations were found to be the main causes of RATs. Low rates of compliance with speed limit signs and seat-belt regulations were also observed. These findings highlight the need of strengthening effective traffic law enforcement alongside with improving traffic safety and raising public awareness.

scholarly journals ROAD TRAFFIC ACCIDENTS IN INDIA: AN OVERVIEW

2018 ◽  
Vol 4 (4) ◽  
pp. 36-38
Author(s):  
Thokchom Shantajit ◽  
Chirom Ranjeev Kumar ◽  
Quazi Syed Zahiruddin
Keyword(s):  
Road Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Preventive Measures ◽  
Road Traffic ◽  
Morbidity And Mortality ◽  
World Health ◽  
Road Traffic Accidents ◽  
The World ◽  
Health Organization

Road traffic accidents claim over a million lives every year in the world. As per World Health Organization (WHO) it is one of the leading cause of death. India, being a rapidly developing country with expanding economy has its own issues as regarding road traffic accidents due to rapid proliferation of motorization. Road traffic accidents causes enormous morbidity and mortality and at the same time, the toll on the economy of the country as a result of it is quite heavy. Road traffic accident is a result of an interaction among different factors which include the environment, vehicle and the human being. Traditionally it is considered that road traffic accidents are accidents which are unpredictable, inevitable and not preventable. But road traffic accidents are indeed predictable and preventable in majority of the cases. This require the knowledge of factors contributing and leading to road traffic accidents. There are certain preventive measures which if adopted can lead to decrease in morbidity and mortality resulting from RTA. Hence, it is the responsibility of all to contribute in reducing road traffic accidents.Keywords: Road traffic accidents; Road traffic injuries; Roads in India, Road safety; Vehicular registration.

Financial impact of road traffic accidents on the society

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Dr. R K Gorea
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
World Health ◽  
Road Traffic Accidents ◽  
The Road ◽  
Physical Injuries ◽  
Health Organization ◽  
Financial Losses

Road traffic accidents (RTA) are a global problem resulting in deaths, physical injuries, psychological problems and financial losses. These financial damages have immediate consequences and long term consequences on the victims and their families. Different countries have different impact of road traffic accidents and therefore spend dissimilar amounts in their budgets to prevent the road traffic accidents. If the financial losses due to road traffic accidents are calculated and highlighted by the researchers, the respective governments will be willing to spend higher amount in their budgets to prevent such accidents; as governments will be able to directly see the benefits to their countries, of spending higher budget amounts. Various countries are acting differently to reduce this menace of road traffic accidents and World Health Organization (WHO) is celebrating “Decade for road safety” to reduce the accidents and thus the financial loses to the society.

scholarly journals Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hesham M. Eraqi ◽  
Yehya Abouelnaga ◽  
Mohamed H. Saad ◽  
Mohamed N. Moustafa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Accidents ◽  
Ad Hoc ◽  
Road Traffic ◽  
Driver Distraction ◽  
World Health ◽  
Skin Segmentation ◽  
Visual Elements ◽  
Health Organization

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically weighted ensemble of convolutional neural networks; we show that a weighted ensemble of classifiers using a genetic algorithm yields a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.

scholarly journals Maxillofacial Injuries Due to Road Traffic Accidents in Saudi Arabia: A Review of Incidence, Demographic Factors & Prevention Strategies

2017 ◽  
Vol 6 (1) ◽  
pp. 1386
Author(s):  
Kamran Bokhari Syed
Keyword(s):  
Saudi Arabia ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Seat Belt ◽  
Demographic Factors ◽  
Traffic Monitoring ◽  
World Health ◽  
Major Public Health Problem ◽  
Road Traffic Accidents ◽  
Maxillofacial Injuries

<p><strong>Background</strong>: World Health Organization has estimated that nearly 25% of all injuries fatalities worldwide are a result of road traffic crashes with 90% of the fatalities occurring in low and middle income countries. Trauma in Saudi Arabia is a major public health problem with increasing rates of mortality and morbidity.</p><p><strong>Objectives</strong>: To review the incidence of maxillofacial injuries due to road traffic accidents in Saudi Arabia and to highlight the etiological factors, the current preventive strategies and suggestions to reduce such injuries.</p><p><strong>Material and Methods</strong>: This review was conducted through literature search over a period of 25 years. The key words included in the search include road traffic injuries, maxillofacial trauma, Saudi Arabia. The search was conducted through search engines and which included Google, science direct, pub med. A total of 56 reference articles and web pages were reviewed. 31 of these references are cited in this review. The demographic factors involved in road traffic accidents, the existing legislation in the country as cited in the literature, primary care system and recent advances in management are highlighted in this review article.</p><p><strong>Conclusion</strong>: Trauma is a preventable cause of death, morbidity, depression and unemployment. Simple measures such as seat belt legislation, traffic monitoring, creation of awareness among youth will significantly bring down loss of lives. This will create a better society to live and enjoy life.</p>

scholarly journals A study to assess the knowledge and practice of road safety measures among undergraduate medical students of M. R. Medical College, Kalaburagi

2018 ◽  
Vol 5 (4) ◽  
pp. 1596 ◽  
Author(s):  
Lakshmi R. Kalbandkeri ◽  
Boramma G. ◽  
Shreeshail Ghooli
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
World Health ◽  
Road Traffic Accidents ◽  
Undergraduate Medical Students ◽  
Safety Measures ◽  
The Road ◽  
Medical College ◽  
Knowledge And Practice

Background: Road traffic injuries claim more than 1.25 million lives each year and have a huge impact on health and development. They are the leading cause of death among young people aged between 15 and 29 years globally. In the South East Asian region of the World Health Organization, India alone accounted for 73 percent of these Road traffic accidents (RTA) burden. The importance of road safety measures needs to be emphasized in the prevention of the road traffic accidents. The objectives of the study were to assess the knowledge and practice of road safety measures among undergraduate medical students.Methods: A cross-sectional study was conducted among 310 medical undergraduates of M.R. Medical College from 1st September to 1st October 2016. Data was collected using pre-tested, semi-structured, self-administered questionnaire. The data collected was analyzed using SPSS version 16. Statistical analysis was done using relevant statistical tests.Results: Out of the 310 students 54.19% were males and 45.81% were females. 90.9% of the participants had driving licence, 32.1% of the students had taken training for driving the car and 66% of the students did not wear helmet. Female students had high knowledge of the road safety measures when compared to male students.Conclusions: The overall knowledge of road safety measures was high among the study participants. Regarding practice behaviours they were not desirable like practice of wearing helmet and exceeding speed limit. Undertaking proper road safety measures are the best available interventions to curb the epidemic of RTA.

scholarly journals The association between meteorological variables and road traffic injuries: a study from Macao

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6438 ◽  
Author(s):  
Chon-Fu Lio ◽  
Hou-Hon Cheong ◽  
Chon-Hou Un ◽  
Iek-Long Lo ◽  
Shin-Yi Tsai
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Meteorological Factors ◽  
Linear Regression Analysis ◽  
World Health ◽  
Road Traffic Accidents ◽  
Road Traffic Injuries ◽  
Windy Weather

Objective Correlation analysis and multiple linear regression analysis were conducted to estimate the influence of meteorological factors on road traffic injuries stratified by severity. Crash rate was defined as mean monthly road traffic accidents per 1,000 vectors. Design Ecological time-series study. Setting Macao traffic accident registry database between January 1st, 2001 and November 31st, 2016. Participants In total, 393,176 traffic accidents and 72,501 cases of road traffic injuries (RTIs) were enrolled; patients’ severity was divided into mild injury, required hospitalisation, and death. Exposure Variation of monthly meteorological factors. Main outcome measure Weather-condition-related road traffic accidents, injuries, and deaths. Results Windy weather significantly correlated with increased number of traffic accidents among all transport vectors (r = .375 to .637; p < 0.001). Multiple linear regression showed temperature (B = 0.704; p < 0.05) and humidity (B =  − 0.537; p < 0.001) were independent factors for mild injury. The role of windy weather was relatively more obvious among patients with severe injuries (B = 0.304; p < 0.001) or those who died (B = 0.015; p < 0.001). A longer duration of sunshine was also associated to RTI-related deaths (B = 0.015; p < 0.001). In total, 13.4% of RTIs were attributable to meteorological factors and may be preventable. Conclusion The World Health Organization stated that RTIs are a major but neglected public health challenge. This study demonstrates meteorological factors have significant effects on any degree of RTIs. The results may not be generalized to other climates or populations while the findings may have implications in both preventing injuries and to announce safety precautions regarding trauma and motor vehicle collisions to the general public by public agencies.

scholarly journals Count time series models for road traffic accidents in Tanzania Mainland

1970 ◽  
Vol 47 (2) ◽  
pp. 495-506
Author(s):  
Amina S Msengwa ◽  
Florence D Ngari
Keyword(s):  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
Information Criterion ◽  
Dar Es Salaam ◽  
Autoregressive Models ◽  
Ratio Test ◽  
Time Varying ◽  
Road Traffic Accidents ◽  
Related Factors

A pairwise analysis was conducted to assess the trends and factors associated with road traffic accidents in Tanzania. The Poisson and Negative Binomial Autoregressive Models were used to extend log linear functions by accounting time-varying components. A total of 85,514 road traffic accidents in Tanzania mainland that occurred from 2012 to 2017 were extracted from Tanzania Police Office records. Eleven factors were grouped into a human, vehicle, physical/environmental and pedestrian-related factors. The Likelihood ratio test, Akaike Information Criterion, Bayesian Information Criterion and residual ACF plots were used to evaluate the performance of the models in Dar es Salaam and other combined regions. The trend analysis indicated a declining pattern in all factors and human-related factors appeared higher than the other three factors. The highest number of road traffic accidents was observed in Dar es Salaam Region compared to other combined regions. The models, including its past values and time-varying factors, were in favour-of other models. In both, Dar es Salaam and other combined regions, non-linear pattern and Negative Binomial Autoregressive Models fitted the data well. The implementation of collective actions in recent years seems positive on road traffic accidents. Nevertheless, more emphasis is needed to monitor trends on the number of accidents and related fatalities. Keywords: Road Traffic Accidents, Poisson, Negative binomial, Autoregressive Models, Tanzania.

scholarly journals A Comparison of Nowruz 2016 and Nowruz 2017 Accidents Data in Iran

2020 ◽  
Vol 5 (2) ◽  
pp. 71-78
Author(s):  
Abdolmajid Rahmani Daranjani ◽  
◽  
Mahmoud Rezaeizadeh ◽  
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Road Transport ◽  
Warning Signs ◽  
World Health ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
Factors Affecting ◽  
The World ◽  
On The Road

Background: Road traffic accidents are currently among the most essential public health issues. According to the World Health Organization, given the rapid growth of road transport globally, road traffic accidents could be the third leading cause of death and disability in the world by 2020. This article examined the role of the human factor in road accidents during the Nowruz holidays, as a major cultural event in Iran. Materials and Methods: We explored the data of road accidents that occurred in Nowruz in 2016 and 2017 in Iran. Traffic accident data concerning the Nowruz holidays of 2016 and 2017 were collected by census method of sampling and based on the report of highway police. Additionally, the frequency of these accidents was analyzed according to travel time, accident type, gender, age, education, and vehicle type in different provinces. Results: The present study findings suggested that among human factors affecting Nowruz accidents in 2016 and 2017, the highest frequency belonged to unnecessary speeding. As in 2016 and 2017, it was the main responsible characteristic for 56.42% and 55.01% of accidents, respectively. In Nowruz 2016, the provinces of Tehran, Khorasan Razavi, Isfahan, Fars, and Khuzestan; in Nowruz 2017, the provinces of Tehran, Isfahan, Khorasan Razavi, Fars, and Gilan encountered the highest rates of accidents leading to injuries and deaths. Conclusion: To control unnecessary speeding and regulations disregard, planning for culturizing and the community-level education are suggested. Besides, increasing the quality and intelligence of vehicles and the construction of sliders, vertical lines on the road, warning signs, and billboards could help reduce the rate of accidents. Creating a working group of experts in psychology, traffic, etc., to study the pathology of dangerous behaviors, useless haste, and disregard for regulations and providing solutions could also be effective.

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