Severity Of Road Accidents Research Articles

Factors associated with urban non-fatal road-accident severity

ABSTRACTThis paper reports on the factors associated with non-fatal urban-road accident severity. Data on accidents were gathered from the local traffic police in the City of Palermo, one of the six most populated cities in Italy.Findings from a mixed-effects logistic-regression model suggest that accident severity increases when two young drivers are involved, road traffic conditions are light/normal and when vehicles crash on a two-way road or carriageway. Speeding is more likely to cause slight or serious injury even when compared to a vehicle moving towards the opposite direction of traffic. An accident during the summer is more likely to result in a slight or serious injury than an accident during the winter, which is in line with evidence from Southern Europe and the Middle East.Finally, the severity of non-fatal accident injuries in an urban area of Southern Europe was significantly associated with speeding, the age of the driver and seasonality.

Study of the Economic Cost of Road Accidents in Jordan

Traffic accidents in Jordan represent a serious problem, where every day about 2 persons die by road accidents, and the country loses between 2-3 percent of its gross domestic products (GDP) due to this problem. This research aims to estimate the total and unit costs (per crash severity and vehicle type) of road accidents in Jordan during three years (2011 through 2013) using human capital approach, and also to estimate the value of statistical life in Jordan for risk reduction of person’s death by road accident using willingness-to-pay approach. The results revealed that Jordan is still suffering from a continuous rise in its road accidents’ costs without a sufficient improvement accompanied with the dramatic increases in its number of vehicles and population (during the study period), especially after the crisis of Syrian refugees who enter the Jordan during this period. The estimated total costs of accident in the years 2011, 2012 and 2013 were about US $ (3814, 4718 and 5146, respectively, which constituted 2.5%, 2.3% and 2.25% of the total country’s GDP for the same years.

Influence of regional features of psycho- emotional state of the Russian society on road accidents severity

The article considers issues of assessment of the relation between road accidents severity and psychoemotional state of Russian society. In the context of the hypothesis about the significant impact of people’s awareness of the value of life on their ordinary behavior, four subtasks were solved. Firstly, the difference in road accidents severity between all road accidents and road accidents that happened in alcohol intoxication state was established. Secondly, indicators of the psychoemotional state of people from different Russian regions were identified. Thirdly, dependencies «Road accidents severity = f(Psychoemotional state of people from Russian region)» for two cases - «All road accidents» and «Road accidents that happened in alcohol intoxication state» were defined. Finally, it was found that degree of impact of alcohol intoxication on road accidents severity depends on the psychoemotional state of people from different Russian regions. Produced results can be used in the development of regional programs of road safety management taking into account features of the psychoemotional background in different subjects of Russia.

Augmenting Classifiers Performance through Clustering

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.

Modernizing the Transportation Infrastructure of Hyderabad using ITS in Relation to Capacity Levels and Road Accident Severity

Modernizing the Transportation Infrastructure of Hyderabad using ITS in Relation to Capacity Levels and Road Accident Severity

Severity analysis of powered two wheeler traffic accidents in Uttarakhand, India

ObjectivePowered Two Wheeler (PTW) vehicles are one of the preferred modes of transport used in India. Also, PTWs accidents are comparatively more frequent than other type of accidents on road. The influencing factors of PTW accidents are also differ from factors that affect other accident types. The objective of this study is to analyze newly available PTWs road accident data from Uttarakhand state in India and revealing the factors that affect the severity of these accidents in various districts of Uttarakhand..MethodologyTo analyze the factors that affect the severity of road accidents in Uttarakhand, initially we have compared three popular classification algorithms i.e. decision tree (CART), Naïve Bayes and Support vector machine on PTW accident data set. The decision tree algorithm’s (CART) classification accuracy was found better than other two techniques. Hence we have preferred CART algorithm to extract the factors that affect the severity of PTWVs accidents in whole Uttarakhand state and its 13 districts separately.Experimental ResultsThe analysis of PTWVs accident data using CART for 13 districts of Uttarakhand and the whole state reveals that every districts have different factors associated with PTW accidents severity. There are some districts in Uttarakhand state which have similar PTW accident patterns, whereas few districts are found to have different PTW accident patterns. These results are very useful to understand the pattern of PTW accidents in Uttarakhand state. These results can certainly be helpful to overcome the PTWs accident rate in Uttarakhand state.

Realised safety impacts of electronic stability control in Finland

The objective of the study is to estimate the realised safety effects of electronic stability control (ESC) in Finland in the current context. The modelling of impacts was based on a framework in which the outcomes of accidents are determined by exposure of road users, accident risk and accident severity. Exposure to risk was modelled in three ways: (i) by calculating the share of passenger cars equipped with ESC with a mathematical model, (ii) on the basis of an ESC fleet penetration estimate obtained from a survey targeted to car importers and (iii) with ESC fleet penetration taking into account the penetration estimate obtained from the survey weighted with the annual kilometrage of passenger cars. The results of the study suggest that around 37 fatalities and 747 injuries were avoided in Finland in 2014 because of ESC.

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials

IntroductionThe effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece. MethodRandom Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively. ResultsRegarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity. ConclusionsThe study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms. Practical applicationThe identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials.

Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models.

Investigation of road accident severity per vehicle type

Abstract The aim of the present study is the investigation of road accident severity per vehicle type. For that reason, a dataset consisting of 59,316 recorded accidents in Greece was analyzed and mathematical models were developed by applying lognormal regression. Three expressions of accident severity were examined: i) the number of fatalities divided by the total number of involved vehicles, ii) the number of severe injuries divided by the total number of involved vehicles and iii) the number of slight injuries divided by the total number of involved vehicles. Furthermore, separate accident severity models were developed for each type of vehicle. The effect of various parameters, such as crash type and weather conditions on accident severity was identified for each type of vehicle (car, moped, motorcycle, bus and truck). Cars involve both private vehicles as well as vehicles used for commercial purposes (like taxis). In general, good weather conditions and night accidents increase severity. Moreover, crash types are consistently affecting accident severity.

Assessment of Spatial Unevenness of Road Accidents Severity as Instrument of Preventive Protection from Emergency Situations in Road Complex

Emergency situations in road complex are road traffic accidents (RA) with severe consequences. These are incidents connected with the death and injury of large number of people. The most common reasons for this are the collision of three or more cars, the collision of buses with trains at railroad crossings, the fall of the buses in the mountain gorge, and other similar cases. Is it possible to predict such events? How to build a preventive protection against such emergencies? We have to understand that emergencies in a road complex are qualitative expression of the quantitative processes that characterize the general state of road safety in the region. In this regard, at the level of state monitoring of emergency situations it is important to understand in general - in which region the situation is more complicated and in which is more favorable. This knowledge helps to more efficiently reallocate resources intended to solve the problems of road safety provision. The consequence of this is improvement of the quality of preventive protection from the emergencies in the road complex. The article presents quantitative values of severity of accidents in the Russian Federation regions and the Pareto chart distribution of cumulates of the accident severity for the Russian Federation. On the basis of the complex assessment of the spatial non-uniformity of the accident severity results it offers two important recommendations, implementation of which will alleviate the issue of formation of emergency situations in the road of the Russian Federation on the basis of the complex assessment of the spatial nonuniformity of the accident severity results.

ADMINISTRATIVE SANCTIONS FOR DRIVING UNDER THE ALCOHOL INFLUENCE

The relative indicators of road traffic safety in Latvia are still amongst the worst in the EU Member States, despite the fact that the road safety level improvement rate in Latvia is one of the best in the European Union. Despite the reduction of severity of road accidents, it is still higher than the best national figures of EU. The publication examines the sanctions for driving under the influence of alcohol or narcotic or other intoxicating substances and their impact on traffic safety in Latvia, court statistics is analysed. The authors also provide their proposals for improving the situation.

Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates

ABSTRACTRoad traffic accidents are among the leading causes of death and injury worldwide. In Abu Dhabi, in 2014, 971 traffic accidents were recorded, which contributed to 121 fatalities and 135 severe injuries. Several factors contribute to injury severity, including driver-related factors, road-related factors, and accident-related factors. In this article, data-mining techniques were employed to establish models (classifiers) to predict the injury severity of any new accident with reasonable accuracy, based on 5,973 traffic accident records in Abu Dhabi over a 6-year period from 2008 to 2013. Additionally, the research aimed to establish a set of rules that can be used by the United Arab Emirates (UAE) Traffic Agencies to identify the main factors that contribute to accident severity. Using Waikato Environment for Knowledge Analysis (WEKA) data mining software, four well-known classification algorithms were employed to model the severity of injury. These algorithms included: Decision Tree (DT) (J48), Rule Induction (PART), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each method in predicting accident severity was evaluated in three different ways. First, the entire data set was used as a training set for the algorithm. Second, accuracy was evaluated using cross-validation with 10-fold. Third, to overcome the problems that resulted from the imbalanced distribution of accident severity in our data set, the data set was resampled to bias the accident severity distribution toward a uniform distribution, and then cross-validation with 10-fold was used again to evaluate the performance. Furthermore, to establish the main contributing factors for road accidents severity, rules generated by the DT J48 algorithm were further explored. The results showed that the overall accuracy of the DT J48 classifier, the PART classifier, and the MLP classifier in predicting the severity of injury resulting from traffic accidents, using 10-fold cross-validation, were similar. The NB classifier exhibited less accuracy. Additionally, the prediction accuracy of the classifiers was enhanced after resampling the training set. The results indicated that the most important factors associated with fatal severity were age, gender, nationality, year of accident, casualty status, and collision type. 18- to 30-year-olds were the most vulnerable age group to traffic accidents. There was a clear trend in accident reduction over the period of the study. Drivers were involved more frequently in traffic accidents than passengers and pedestrians. Male drivers were involved more frequently in traffic accidents than female drivers. UAE, Asian, and Arab nationalities had the highest traffic accident frequency; Gulf and other nationalities had lower traffic accident frequency. The highest number of traffic accidents occurred at right angles. Pedestrian–vehicle type collisions had the next highest number of traffic accidents, followed by rear-end collisions and sideswipe collisions.

The Identification of Patterns of Interurban Road Accident Frequency and Severity Using Road Geometry and Traffic Indicators

This paper is focused on the effect of road geometry, and other accident causing conditions, on the binary response variable road accident severity. The data is collected from two interurban routes in Spain (Madrid-Irún and Barcelona-Almeria) and covers a 3 year period (2010-2012). Data mining techniques were applied for the treatment and combination of two databases for road accident associated factors and geometry standards respectively. The effect of the influential factors on road accident severity was estimated through a non-parametric statistical methodology, random forests. Several standards of the road geometry design were found to have a significant effect on the road accident severity.

Analysing the Relationship Among Accident Severity, Drivers’ Behaviour and Their Socio-economic Characteristics in Different Territorial Contexts

A relevant aspect of traffic accident analysis is to identify the key factors affecting road users’ driving behaviour and attitudes. Traditional considerations of traffic safety focus on physical environment, vehicle and road user. Among these, road users’ driving behaviour plays an important role. The aim of our work is to investigate the relationship among road accident severity, users’ driving behaviour and socio-economic characteristics, by comparing data from two urban areas having different characteristics: Cosenza, a medium-small city of the southern Italy, and Granada, a medium-large city of the southern Spain. For this purpose, two surveys were conducted by face-to-face interviews. The questionnaire included an experiment based on the Stated Preferences according to which respondents had to express a ranking of hypothetical driving scenarios characterized by different levels of attributes describing driving attitudes. The data collected through the surveys were analysed in order to characterize road users’ driving behaviour in the two different backgrounds, and to emphasise the main differences between them. The results obtained from the two samples conducted to very different interpretations and showed how road accident severity, users’ driving behaviour and drivers’ perception of the risk of road accident depend on the territorial context where the road users live.

Identifying the Factors Affecting the Probability of Accident Severity in Community Road - Focusing on Seoul City

Since a variety of factors are associated with crash occurrence, the analysis of causes of crash is a hard task for traffic researchers and engineers. This study was attempted to identify factors affecting severity of the community road accidents. In particular, our analyses were focused on the community road accidents. A binary logistic regression technique was adopted for the analyses. The results showed that pedestrians of 65 years or older, cloudy, fence (sidewalk/driveway barrier), drivers of 24 years or younger, left/right turning, female pedestrian, non-business vehicle were dominant factors for the severity.

A Pragmatic Approach for Refined Feature Selection for the Prediction of Road Accident Severity

A Pragmatic Approach for Refined Feature Selection for the Prediction of Road Accident Severity

Prediction of Road Accident Severity Using the Ordered Probit Model

The ordered probit model is used to examine the contribution of several factors to the injury severity faced by motor-vehicle occupants involved in road accidents. The estimated results suggest that motor-vehicle occupants travelling in light-vehicles, at two-way roads, and on dry road surfaces tend to suffer more severe injuries than those who travel in heavy-vehicles, at one-way roads, and on wet road surfaces. Additionally, the driver's seat is clearly the safest seating position, urban areas seem to originate less serious accidents than rural areas, and women tend to be more likely to suffer serious or fatal injuries than men.

A Methodology for Ranking Road Safety Hazardous Locations Using Analytical Hierarchy Process

This study presents a methodology for ranking road safety hazardous locations using analytical hierarchy process (AHP). Road accident causes huge losses to the economy in terms of the cost incurred in hospitalization and treatment and damages to vehicles and property etc. There is an urgent need to reduce the number and severity of road accidents by implementing remedial measures at hazardous locations in the road network. Further, it is generally not possible to implement all remedial measures identified due to limited budget available for road safety improvement. Hence, it is needed to rank the hazardous locations so that depending on the available budget, the hazardous locations can be treated. However, literature review indicated that most of the methodologies require accident data for this purpose. Comprehensive road accident data is rarely available. Hence, this study presents a four stage methodology for ranking road safety hazardous locations which do not require accident data. The methodology presents a hierarchical structural for identification of safety factors. Road safety hazardous conditions are decomposed into safety hazardous condition at straight sections, safety hazardous condition at curve sections and safety hazardous condition at intersections. Analytical hierarchy process (AHP) is used to determine the weight of the different identified safety factors. The Safety Hazardous Index is developed using weight of safety factors and condition rating of safety factors. The Safety Hazardous Index is developed separately to evaluate safety at straight section, safety at curve section and safety at intersection. It is expected that this study will be useful in treating more hazardous locations depending on the available budget for road safety improvement.here and insert your abstract text.

AFFECTIVE-SOUND EFFECTS ON DRIVING BEHAVIOUR

Recent technological developments in active advanced driver assistance systems and in-car infotainment devices have contributed to reducing the number and severity of road accidents as well as improving and simplifying driver experience. However, these systems may impact driving performance in undesired ways, especially when emotionally-charged stimuli are used as warning signals. Emotional distraction can be a serious danger, causing delays in information processing, and reducing driving safety below minimal acceptable levels. Here we study the effect of emotionally-laden auditory signals on the speed of concurrent driving decisions. We distinguished two categories of behavioural responses: ‘urgent’ vs ‘evaluative’. In the experiments reported here participants were quicker to evaluate whether a traffic scene was risky or not after hearing an emotionally-charged auditory stimulus than after a neutral one. However, urgent (braking) responses to the same scenes were not affected by the emotional quality of the auditory signal. Based on these results, we give preliminary advice on the design of guidelines for in-car interfaces particularly in the field of affective in-car computing.