scholarly journals Statistical Analysis and Development of Accident Prediction Model of Road Safety Conditions in Hisar City

2021 ◽  
Vol 889 (1) ◽  
pp. 012034
Author(s):  
Keshav Bamel ◽  
Sachin Dass ◽  
Saurabh Jaglan ◽  
Manju Suthar
Keyword(s):  
Traffic Accidents ◽  
Linear Regression Analysis ◽  
Severity Index ◽  
Road Accident ◽  
P Value ◽  
Road Accidents ◽  
Accident Prediction ◽  
New Information ◽  
Margin Of Error ◽  
Accident Data

Abstract The severity of road accidents is a big problem around the world, particularly in developing countries. Recognizing the major contributing variables can help reduce the severity of traffic accidents. This research uncovered new information as well as the most substantial target-specific factors related to the severity of road accidents. T-stat, P-value, Significance and other test values are determined to check the dependency of dependent variable on independent variable in order to obtain the most significant road accident variables. In this research, a comparative analysis of accident data from Hisar and Haryana are compared. According to the findings, Haryana’s accident severity index (46.20) was higher in 2019 than Hisar’s (36.01), while Hisar had fewer accidents per lakh population (33.34) than Haryana (38.40). The outcomes of the study were used to develop an effective and precise accident predicting model is developed for Hisar city and state Haryana using a statistical method. Four models were created using linear regression analysis, two each for Hisar and Haryana. These models produce good results with a margin of error that is within acceptable bounds (0-5%), allowing them to be used to predict future traffic accidents and deaths.

scholarly journals Determining Hotspots of Road Accidents using Spatial Analysis

2018 ◽  
Vol 9 (1) ◽  
pp. 146 ◽  
Author(s):  
S.Sarifah Radiah Shariff ◽  
Hamdan Abdul Maad ◽  
Nursyaza Narsuha Abdul Halim ◽  
Zuraidah Derasit
Keyword(s):  
Spatial Analysis ◽  
Traffic Accidents ◽  
Nearest Neighbor ◽  
Hot Spot ◽  
Road Accident ◽  
Road Accidents ◽  
Counter Measures ◽  
Temporal Clustering ◽  
Accident Data ◽  
Surrounding Areas

<p>Road accidents continuously become a major problem in Malaysia and consequently cause loss of life or property. Due to that, many road accident data have been collected by highway concessionaries or build–operate–transfer operating companies in the country meant for coming up with proper counter measures. Several analyses can be done on the accumulated data in order to improve road safety. In this study the reported road accidents cases in North South Expressway (NSE) from Sungai Petani to Bukit Lanjan during 2011 to 2014 period is analyzed. The aim is to determine whether the pattern is clustered at certain area and to identify spatial pattern of hot spots across this longest controlled-access expressway in Malaysia as hotspot represents the location of the road which is considered high risk and the probability of traffic accidents in relation to the level of risk in the surrounding areas. As no methodology for identifying hotspot has been agreed globally yet; hence this study helped determining the suitable principles and techniques for determination of the hotspot on Malaysian highways. Two spatial analysis techniques were applied, Nearest Neighborhood Hierarchical (NNH) Clustering and Spatial Temporal Clustering, using CrimeStat® and visualizing in ArcGIS™ software to calculate the concentration of the incidents and the results are compared based on their accuracies. Results identified several hotspots and showed that they varied in number and locations, depending on their parameter values. Further analysis on selected hot spot location showed that Spatial Temporal Clustering (STAC) has a higher accuracy index compared to Nearest Neighbor Hierarchical Clustering (NNH). Several recommendations on counter measures have also been proposed based on the details results.</p>

scholarly journals Road Transport Accidents in Nigeria and the Role of Automotive Active Safety System

2016 ◽  
Vol 11 (1) ◽  
pp. 49-54
Author(s):  
Olasunkanmi Oriola Akinyemi ◽  
Hezekiah O Adeyemi ◽  
Olusegun Jinadu
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Transportation Network ◽  
Road Transport ◽  
Road Accident ◽  
Active Safety ◽  
Road Accidents ◽  
Road Transportation ◽  
Road Traffic Safety

Abstract Analysis of road traffic accidents revealed that most accidents are as a result of drivers’ errors. Over the years, active safety systems (ASS) were devised in vehicle to reduce the high level of road accidents, caused by human errors, leading to death and injuries. This study however evaluated the impacts of ASS inclusions into vehicles in Nigeria road transportation network. The objectives was to measure how ASS contributed to making driving safer and enhanced transport safety. Road accident data were collected, for a period of eleven years, from Lagos State Ministry of Economic Planning and Budget, Central Office of Statistics. Quantitative analysis of the retrospective accident was conducted by computing the proportion of yearly number of vehicles involved in road accident to the total number of vehicles for each year. Results of the analysis showed that the proportion of vehicles involved in road accidents decreased from 16 in 1996 to 0.89 in 2006, the injured persons reduced from 15.58 in 1998 to 0.3 in 2006 and the death rate diminished from 4.45 in 1998 to 0.1 in 2006. These represented 94.4 %, 95 % and 95 % improvement respectively on road traffic safety. It can therefore be concluded that the inclusions of ASS into design of modern vehicles had improved road safety in Nigeria automotive industry.

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

scholarly journals TRAFFIC-RELATED MORTALITY IN MOLDOVA

2020 ◽  
pp. 140-147
Keyword(s):  
Life Expectancy ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Road Accidents ◽  
Continuous Increase ◽  
Risk Of Death ◽  
Significant Difference ◽  
Related Mortality

This article analyses the mortality caused by road accidents in Moldova depending on the degree of involvement of pedestrians, cyclists, motorcyclists, drivers and passengers of transport units, depending on age and sex. Results suggest that traffic-related mortality in Moldova has shown an increased incidence among the young and working-age population, where a significant difference between males and females is observed. Among the youth, traffic-related deaths register between 10-27% of the overall mortality in both sexes. The risk exposure of dying in a traffic accident decreases with age and is less significant in the retired ages. During the years 1998-2015, avoidance of trafficrelated deaths would have assured an increase in life expectancy between 0.40-0.56 years in males, and 0.09-0.23 years in females. The continuous increase in the number of transport units on public roads, as well as in the number of hours spent in traffic, influences the degree of exposure to the risk of death or injury as a result of road traffic accidents. Trauma resulting from road accidents increases the incidence of premature mortality and disability among the population, which is reflected by the decrease of healthy life expectancy. It is ascertained that the road accident mortality requires a detailed and comprehensive analysis given the multitude of factors influencing deaths and injuries related to a traffic accident among the population. Thus, in order to improve road safety and reduce mortality incidence among traffic participants, a range of actions has to be implemented by the liable actors, including through the international experience.

Augmenting Classifiers Performance through Clustering

2018 ◽  
Vol 8 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Sachin Kumar ◽  
Prayag Tiwari ◽  
Kalitin Vladimirovich Denis
Keyword(s):  
Traffic Accident ◽  
Latent Class ◽  
Safety Concern ◽  
Health And Safety ◽  
Road Accident ◽  
Support Vector ◽  
Road Accidents ◽  
Real World Data ◽  
The Public ◽  
Accident Data

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

scholarly journals Road Traffic Accidents by 'Nasimon' and 'Karimon'-A Study in Faridpur

2012 ◽  
Vol 7 (1) ◽  
pp. 6-9
Author(s):  
ASMJ Chowdhury ◽  
MS Alam ◽  
SK Biswas ◽  
RK Saha ◽  
AR Mondal ◽  
...  
Keyword(s):  
Low Socioeconomic Status ◽  
Urban Areas ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Motor Vehicles ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
College Hospital ◽  
Medical College

Road traffic accidents in Bangladesh have been rapidly increasing with huge mortality through road accidents each year. There are many causes of road accidents in recent years; one important cause is running of locally made improvised three wheelers (flat bed tricycle) in the urban areas and also on the highways, popularly known as 'Nasimon' and 'Karimon'. This prospective study was carried out in Faridpur Medical College Hospital from January through June 2011, to study the accident patients caused by 'Nasimon' and 'Karimon'. Fifty six (12%) patients were of RTA by 'Nasimon' and 'Karimon' out of a total of 468 patients admitted into our hospital during this period. Most patients (41, 73.21%) were male, highest accidents (24, 42.86%) were observed among 21-30 years age group and most victims (33, 58.93%) were belonged to low socioeconomic status. Commonest (31, 55.36%) victims were passengers of 'Nasimon' and 'Karimon' while maximum number of accidents (46, 82.14%) took place in the urban areas and on the highways. Injury pattern of victims were similar to that found in any other road accident patients. These three wheelers 'Nasimon' and 'Karimon' are run in violating of Bangladesh Motor Vehicles Act (1983) as they are totally unfit for plying on the highways. Strict surveillance against these illegal and risky vehicles on the highways and in the urban areas by law enforcing agencies is required as a measure to reduce the burden of road accidents in our country.DOI: http://dx.doi.org/10.3329/fmcj.v7i1.10289Faridpur Med. Coll. J. 2012;7(1): 06-09

scholarly journals Road Traffic Accidents Analysis Using Data Mining Techniques

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
G. Janani ◽  
N. Ramya Devi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Accident ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
Rule Mining ◽  
Data Mining Techniques

Road Traffic Accidents (RTAs) are a major public concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury. Most of the analysis of road accident uses data mining techniques which provide productive results. The analysis of the accident locations can help in identifying certain road accident features that make a road accident to occur frequently in the locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. Data analysis has the capability to identify different reasons behind road accidents. In the existing system, k-means algorithm is applied to group the accident locations into three clusters. Then the association rule mining is used to characterize the locations. Most state of the art traffic management and information systems focus on data analysis and very few have been done in the sense of classification. So, the proposed system uses classification technique to predict the severity of the accident which will bring out the factors behind road accidents that occurred and a predictive model is constructed using fuzzy logic to predict the location wise accident frequency.

scholarly journals Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

2016 ◽  
Vol 28 (4) ◽  
pp. 415-424 ◽  
Author(s):  
Draženko Glavić ◽  
Miloš Mladenović ◽  
Aleksandar Stevanovic ◽  
Vladan Tubić ◽  
Marina Milenković ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Future Research ◽  
Practical Application ◽  
Accident Prediction ◽  
Accident Data ◽  
Road Characteristics ◽  
The Relationship

Over the last three decades numerous research efforts have been conducted worldwide to determine the relationship between traffic accidents and traffic and road characteristics. So far, the mentioned studies have not been carried out in Serbia and in the region. This paper represents one of the first attempts to develop accident prediction models in Serbia. The paper provides a comprehensive literature review, describes procedures for collection and analysis of the traffic accident data, as well as the methodology used to develop the accident prediction models. The paper presents models obtained by both univariate and multivariate regression analyses. The obtained results are compared to the results of other studies and comparisons are discussed. Finally, the paper presents conclusions and important points for future research. The results of this research can find theoretical as well as practical application.

scholarly journals A Mechanistic Model for Predicting Accident Potential of Vehicles Transiting in Nigerian Roads

2019 ◽  
pp. 1-10
Author(s):  
Ama Agwu ◽  
Nwogu Chukwunoso ◽  
Nwankwojike Bethrand
Keyword(s):  
Environmental Factors ◽  
Prediction Model ◽  
Road Safety ◽  
Mechanistic Model ◽  
Road Accidents ◽  
Percentage Contribution ◽  
Accident Prediction ◽  
Accident Probability ◽  
Accident Data ◽  
Mechanical Factors

An accident prediction model was developed for determining the accident potential of a vehicle while on transit. The model identifies the various factors responsible for vehicle crashes. With the help of accident data obtained from the database of the Nigerian Federal Road Safety Corps (FRSC), the percentage contribution of each factor is calculated. These accident-cause factors were further grouped into three distinct classes: Human factors (HF), Mechanical factors (MF) and Environmental factors (EF). Analysis of the accident data showed that HF is the chief cause of most road accidents recorded, followed by MF and EF with probabilities of 0.846, 0.138 and 0.016 respectively. Also, driver age, travel distance and maintenance frequency of the vehicle were considered in the development of the model. The model gives an output ranging from 0-1. Values close to 0 mean low accident probability while values close to 1 signify high accident probability. Application and adherence to this model will significantly reduce the frequency of road accidents. Finally, transport companies and fleet operators are therefore encouraged to embrace and use this innovation for safer operations.

scholarly journals Uncovering the behaviour of road accidents in urban areas

2020 ◽  
Vol 7 (4) ◽  
pp. 191739
Author(s):  
C. Cabrera-Arnau ◽  
R. Prieto Curiel ◽  
S. R. Bishop
Keyword(s):  
Time Series ◽  
Population Size ◽  
Urban Area ◽  
Rural Areas ◽  
Urban Areas ◽  
Road Accident ◽  
Road Accidents ◽  
Accident Data ◽  
The Relationship ◽  
Degrees Of Severity

Different patterns in the incidence of road accidents are revealed when considering areas with increased levels of urbanization. To understand these patterns, road accident data from England and Wales is explored. In particular, the data are used to (i) generate time series for comparison of the incidence of road accidents in urban as opposed to rural areas, (ii) analyse the relationship between the number of road accidents and the population size of a set of urban areas, and (iii) model the likelihood of suffering an accident in an urban area and its dependence with population size. It is observed that minor and serious accidents are more frequent in urban areas, whereas fatal accidents are more likely in rural areas. It is also shown that, generally, the number of accidents in an urban area depends on population size superlinearly, with this superlinear behaviour becoming stronger for lower degrees of severity. Finally, given an accident in an urban area, the probability that the accident is fatal or serious decreases with population size and the probability that it is minor, increases sublinearly. These findings promote the question as to why such behaviours exist, the answer to which will lead to more sustainable urban policies.

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