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 COVID 19: Real-time Forecasts of confirmed cases, active cases, and health infrastructure requirements for India and its states using the ARIMA model

2020 ◽  
Author(s):  
Rishabh Tyagi ◽  
Mahadev Bramhankar ◽  
Mohit Pandey ◽  
M Kishore
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Care ◽  
World Health ◽  
Series Data ◽  
Health Infrastructure ◽  
Future Demands ◽  
Health Organization

AbstractBackgroundCOVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on 11th March 2020. The Indian public health care system is already overstretched, and this pandemic is making things even worse. That is why forecasting cases for India is necessary to meet the future demands of the health infrastructure caused due to COVID-19.ObjectiveOur study forecasts the confirmed and active cases for COVID-19 until July mid, using time series Autoregressive Integrated Moving Average (ARIMA) model. Additionally, we estimated the number of isolation beds, Intensive Care Unit (ICU) beds and ventilators required for the growing number of COVID-19 patients.MethodsWe used ARIMA model for forecasting confirmed and active cases till the 15th July. We used time-series data of COVID-19 cases in India from 14th March to 22nd May. We estimated the requirements for ICU beds as 10%, ventilators as 5% and isolation beds as 85% of the active cases forecasted using the ARIMA model.ResultsOur forecasts indicate that India will have an estimated 7,47,772 confirmed cases (95% CI: 493943, 1001601) and 296,472 active cases (95% CI:196820, 396125) by 15th July. While Maharashtra will be the most affected state, having the highest number of active and confirmed cases, Punjab is expected to have an estimated 115 active cases by 15th July. India needs to prepare 2,52,001 isolation beds (95% CI: 167297, 336706), 29,647 ICU beds (95% CI: 19682, 39612), and 14,824 ventilator beds (95% CI: 9841, 19806).ConclusionOur forecasts show an alarming situation for India, and Maharashtra in particular. The actual numbers can go higher than our estimated numbers as India has a limited testing facility and coverage.

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 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 Time series modeling of road traffic accidents in Amhara Region

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kidane Alemtsega Getahun
Keyword(s):  
Time Series ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Series Data ◽  
Close Monitoring ◽  
Road Traffic Accidents ◽  
Amhara Region

AbstractRoad traffic accidents (RTA) are commonly encountered incidents that can cause injuries, death, and property damage to members of society. Ethiopia is one of the highest incident rates of road traffic accidents. Report of Transport and Communication from 2012 to 2014, shows an increment in the number of traffic accidents in Ethiopia. Amhara region accounted for 27.3% of the total road traffic accident-related deaths in Ethiopia during the year 2008/9, which is the highest share among all regions in Ethiopia. The current research aims to model the trend of injury, fatal and total road traffic accidents in the Amhara region from September 2013 to May 2017. Monthly reported traffic accidents were obtained from the traffic department of the Amhara region police commission. The most universal class of models for forecasting time series data called Auto-regressive Integrated Moving Averages (ARIMA) models were applied to model the trends and patterns of road traffic accident cases in the Amhara region. The average number of observed injury RTA, fatal RTA, and total RTA were 27.2, 14, and 78.2 per month respectively. It was observed that a relatively large number of RTA’s are reported on Tuesday, Thursday, and Saturday relative to other days of the week. The data also reveals that more than 60% of accidents involve drivers between the ages of 18–30 years. ARIMA (2,0,0) (1,0,0) ARIMA (2,0,0) and ARIMA (2,0,0) (1,1,0) were fitted as the best model for total injury accidents, fatal RTA and total RTA data respectively. A 48 months forecast was made based on the fitted models and it can be concluded that road traffic accident cases would continue at the non-decreasing rate in the Amhara region for the predicted periods. Therefore, the findings of this study draw attention to the importance of implementing improved better policies and close monitoring of road trafficking to change the existing non-decreasing trend of road traffic accidents in the region.

scholarly journals Time Series Modeling of Road Traffic Accidents in Amhara Region

2021 ◽  
Author(s):  
Kidane Alemtsega
Keyword(s):  
Time Series ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Series Data ◽  
Close Monitoring ◽  
Road Traffic Accidents ◽  
Amhara Region

Abstract Road traffic accidents are commonly encountered incidents that can cause injuries, death, and property damage to members of society. Ethiopia is one of the highest incident rates of road traffic accidents. Report of Transport and Communication from 2012–2014, shows an increment in the number of traffic accidents in Ethiopia. Amhara region accounted for 27.3% of the total road traffic accident-related deaths in Ethiopia during the year 2008/9, which is the highest share among all regions. The current research aims to model the trend of injury, fatal and total road traffic accidents in the Amhara region from September 2013 to May 2017. Monthly reported traffic accidents were obtained from traffic police offices and other concerned governmental organizations at the zonal and regional levels. The most universal class of models for forecasting time series data called Auto-regressive Integrated Moving Averages models are applied to model the trends and patterns of road traffic accident cases in the Amhara region. The average number of observed injury RTA, fatal RTA, and total RTA were 27.2, 14, and 78.2 per month respectively. It was observed that a relatively large number of RTA’S are reported on Tuesday, Thursday, and Saturday relative to other days of the week. The data also reveals that more than 60% of accidents involve drivers between the ages of 18–30 years. ARIMA (2,0,0) (1,0,0) ARIMA (2,0,0) and ARIMA (2,0,0) (1,1,0) were fitted as the best model for total injury accidents, fatal RTA and total RTA data respectively. A 48 months forecast was made based on the fitted models and it can be concluded that road traffic accident cases would continue at the non-decreasing rate in the Amhara region for the predicted periods. Therefore, the findings of this study draw attention to the importance of implementing improved better policies and close monitoring of road trafficking to change the existing trend of road traffic accidents in the region.

scholarly journals Situational Analysis of Road Traffic Accidents- A case of Madurai District rural areas, Tamil Nadu

2020 ◽  
Vol 32 (3) ◽  
pp. 554-558
Author(s):  
Vigneshwaran Subbiah Akkayasamy ◽  
Sigamani Panneer
Keyword(s):  
Rural Areas ◽  
Tamil Nadu ◽  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Health Concern ◽  
Series Data ◽  
Situational Analysis ◽  
Road Traffic Accidents ◽  
Road Accidents

Background: Road traffic accidents (RTAs) have emerged as a major public health concern due to the growing number of motorized vehicles all over the world. In India, the burden of road traffic accidents is increasing and from 1991 to 2011, the number of fatal deaths has more than doubled. Madurai district is among road accident-prone regions of the state of Tamil Nadu. Objective: This paper aims to examine and understand trends and patterns of RTAs in rural areas of Madurai District between 2014-2018. Methods: The researchers collected RTAs data from Madurai District Crime Records Bureau. we have considered time-series data from 2014 to 2018 and the number and percentage of deaths by the distribution of relevant factors such as timing, gender, road type, and vehicles to understand holistic patterns of RTAs. Results: Totally 9950 road accidents were reported by Madurai District Crime Records Bureau between 2014 and 2018 and on an average over 1990 accidents have occurred every year. Nearly 40 per cent fatal accidents occurred between 15-21 h. Men were the victims in 87 per cent of deaths and men died 6.8 times higher than females during 2014-2018. Over 57 per cent of fatalities have occurred in National Highways. The proportion of two-wheelers contributed to road deaths is 28 per cent and two-wheelers caused maximum road deaths than other vehicles. Conclusion: The study shows a decreasing trend in road accidents and fatalities in Madurai district. However, a significant number of men dying in road accidents highlights the difficulties of their families.

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.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

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