Statistical analysis of factors associated with recent traffic accidents dataset: a practical study (original) (raw)
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Using logistic regression in determining the effective variables in traffic accidents
Applied Mathematical Sciences
This research was conducted to determine the important influential variables upon the deaths from road traffic accidents and effect of each of those upon the studied phenomenon through applying logistic regression model. The maximum likelihood method was used to estimate parameters to determine the explanatory variables effect. Wald test was used to determine the significance of the explanatory variables. The data set used in this research consists of a sample of (212) observations and was obtained from the records of the directorate traffic-Garmian. The accident victims is response variable in this study and it is a dichotomous variable with two categories. The study led to a number of conclusions, among them; logistic regression models fit such data, three explanatory variables were found most significantly associated to accident victims response variable namely; high speed, car type, and location.
2020
The present paper purposes to develop the relationship model between the factors of accidents and severity level of traffic accidents by using multinomial logistic regression model approach, for a case study the traffic accident in Makassar City, Indonesia. In further, the study evaluates the traffic accident factors which significantly influence the traffic accident severity level. In this regard, the outcome variable is the severity level of the traffic accident which has three attributes, i.e., death, serious injury, and minor injury. The explanatory variables involve victim characteristics and traffic accident characteristics. The present study used the traffic accident database during 2012–2015 which recorded by the traffic police agency in the city. The model calibration results show that the relationship model has a good accuracy level. The victim position and the collision types significantly influence the severity accident level. The results provide basic information for ef...
2018
There are various factors which are related to Road Traffic Crashes (RTCs). In this study, Logistic Regression is used to estimate the severity of factors related to RTCs in Kathmandu Valley. The dependent variable is the Crash Severity (Fatal or Non-Fatal). The independent variables are crash cause, vehicle type, age & sex of the driver at fault, age of the injured personnel, time of crash, collision type, location of the crash and injured type. Data are obtained from Nepal Traffic Police records for the past five years. Because of the binary nature of the dependent variable, logistic regression was found suitable. Of the nine independent variables, three variables were found to be significantly associated with the outcome of the dependent variable namely age of the driver at fault, age of the injured personnel and time of the crash. A statistical interpretation of these significant variables in terms of odds and odd ratio concept is done in the analysis part. Further the associati...
Analysing the Severity and Frequency of Traffic Crashes in Riyadh City Using Statistical Models
International journal of transportation science and technology, 2012
Traffic crashes in Riyadh city cause losses in the form of deaths, injuries and property damages, in addition to the pain and social tragedy affecting families of the victims. In 2005, there were a total of 47,341 injury traffic crashes occurred in Riyadh city (19% of the total KSA crashes) and 9% of those crashes were severe. Road safety in Riyadh city may have been adversely affected by: high car ownership, migration of people to Riyadh city, high daily trips reached about 6 million, high rate of income, low-cost of petrol, drivers from different nationalities, young drivers and tremendous growth in population which creates a high level of mobility and transport activities in the city. The primary objective of this paper is therefore to explore factors affecting the severity and frequency of road crashes in Riyadh city using appropriate statistical models aiming to establish effective safety policies ready to be implemented to reduce the severity and frequency of road crashes in Riyadh city. Crash data for Riyadh city were collected from the Higher Commission for the Development of Riyadh (HCDR) for a period of five years from 1425H to 1429H (roughly corresponding to 2004-2008). Crash data were classified into three categories: fatal, serious-injury and slight-injury. Two nominal response models have been developed: a standard multinomial logit model (MNL) and a mixed logit model to injury-related crash data. Due to a severe underreporting problem on the slight injury crashes binary and mixed binary logistic regression models were also estimated for two categories of severity: fatal and serious crashes. For frequency, two count models such as Negative Binomial (NB) models were employed and the unit of analysis was 168 HAIs (wards) in Riyadh city. Ward-level crash data are disaggregated by severity of the crash (such as fatal and serious injury crashes). The results from both multinomial and binary response models are found to be fairly consistent but the results from the random parameters model seem more reasonable. Age and nationality of the driver, excessive speed, wet road surface and dark lighting conditions and single vehicle crashes
Analyzing the Factors Influencing Road Traffic Accident Severity: A Case Study of Khulna City
PLAN PLUS
Road traffic accident occurrences are terrible global phenomena all over the world. With more and different motorized vehicles, Bangladesh and Khulna city face a more significant number of fatalities and injuries. The metropolitan city is one of the riskiest places for road accidents because of its higher population density than in any other location. Road accidents in Khulna city are a significant problem, and they are increasing day by day. So, to reduce the severity of road accidents, the factors influencing road accident severity in Khulna city must be studied and analyzed. Binomial logistic regression is selected and applied to predict the risk or severity of 266 accidents from 2010 to 2019, collected by the Traffic Branch of the Deputy Police Commissioner of Khulna Metropolitan Police. The response variable for this study is accident severity (fatal and non-fatal). According to the study's findings, the number of injuries, number of fatalities, vehicle velocity, accident t...
Applied Sciences
Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and...
Trends and Modeling of Traffic Accidents in Jordan
International Journal of Engineering and Technology, 2019
Road traffic accidents globally responsible for the death of 1.35 million people in 2016, it considered the 9 th leading cause of death for people of all ages in 2012-2016. The road traffic accidents considered the third cause of death in Jordan during the year 2010. According to this study, the traffic accident data in Jordan from 1981 to 2017 were analyzed. The traffic accidents in Jordan were increasing due to the increase in population and auto ownership, which increased from 15 in 1981 to 6.3 person/vehicle in 2017. The general trend of accidents was increasing from 13567 in 1981 to 150226 in 2017; resulted in 685 deaths and injuries of 16246 injuries, with an average of 104 thousand accidents /year for the last 17 years. In spite of growing the motorization level (# registered vehicles/1000 population) from 68 in 1981 to 157.5 in 2017, the severity rate (the total number of fatal and injury in the total accidents) decreasing from 0.718 in 1981 to 0.124 in 2017. According to this study, the relationships between traffic accidents and their caused factors seemed strength with R 2 ˃ 0.93, where the rate of casualty accidents decreasing with R 2 ˃ 0.80. The time factors considered the significant essential variable in most models, then the growth of auto ownership. The traffic accident rate was analyzed considering several indexes such as motorization and severity levels. Despite that the motorization index is increasing with time in a similar trend as the accident rate, the severity level is decreasing due to the reduction of casualty accidents. Most of the regression models (R 2 ≥ 0.900) obtained from these accidents data could be used to predict the accidents and other related variables in the future.
2019
Background : The car accident injury level is known to be a result of a complex interaction of factors to drivers’ behavior, vehicle characteristics and environmental condition. Therefore it is obvious that identifying the contribution of the factors to the accident injury is very critical. The objective of study was to perform descriptive analysis to see the characteristics of car accident, to assess the prevalence and determinants of road safety practices in Mekelle City, Tigray, Ethiopia. Methods : A random sample of data was extracted from traffic police office from September 2014- July 2017. An ordered logistic regression model was used to examine factors that worsen the car accident level. Result : A total sample of 385 car accidents were considered in the study of which 56.7% were fatal, 28.6% serious and 14.7% slight injury. The model estimation result showed that, being experienced drivers (Coef. = 0.686; p-value< = 0.050) were found to increase the level of injury. On t...
Using Binary Logistic Regression to Explain the Impact of Accident Factors on Work Zone Crashes
RSS 2017 - Road Safety & Simulation, 2017
For consolidated road networks, the identification, programming, and implementation of maintenance actions enables addressing the deficiencies identified in the infrastructure, ensuring the provision of an adequate service to users. The performance of such actions along the infrastructure lifetime makes it necessary to study the impact that road work zones may have on road crashes since these areas change locally and temporarily the traffic conditions offered to users (lower speeds, the presence of work equipment and workers, narrow lanes, changes in vertical and horizontal signs, etc.). This study aims to analyze the Portuguese official road work zones crash data from 2013-2015 period by using binary logistic regression models to identify the most significant factors influencing work zone crashes. Official data was processed in order to be used in a statistical analysis software and the binary logistic regressions were performed for the analysis of Portuguese work zone crashes by the type of crash (pedestrian, angle, rear-end and runoff road), driver age groups (under 25 years, 25 to 64 and over 65 years) and a predominant contributing factor as speeding, unexpected obstacle on the road and the disregard for vertical road signs and safety distance (main contributing factors identified in this study). Results obtained shows that factors as "urban environment", "one driver involved is running straightly", "clean and dry pavement" and "daylight" have positive impact in a large number of models. The identification of these factors allows supporting the definition of strategies aimed at the reduction of the number and severity of crashes in road work areas.