scholarly journals Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

2020 ◽  
Vol 12 (6) ◽  
pp. 2237 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Maria Attard
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Fatal Outcome ◽  
Policy Support ◽  
Health Concern ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Traffic Volumes ◽  
New Vision

Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.

Analysis of Driver's Characteristics Based on Internet of Vehicles

2014 ◽  
Vol 505-506 ◽  
pp. 1148-1152
Author(s):  
Jian Qun Wang ◽  
Xiao Qing Xue ◽  
Ning Cao
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Classification Model ◽  
Economic Losses ◽  
Lane Changing ◽  
Internet Of Vehicles ◽  
Factors Affecting ◽  
The Road ◽  
The University ◽  
Driver’S Characteristics

The road traffic accidents caused huge economic losses and casualties, so it had been focused by the researchers. Lane changing characteristic is the most relevant characteristic with safety. The intent of lane changing was discussed. Firstly, the factors affecting the intent were analyzed, the speed satisfaction value and the space satisfaction value were proposed; then the data from the University of California, Berkeley was extracted and the number of vehicles changed lane more often and the vehicle ID were obtained; the BP neural network classification model was established, it was trained and testified by actual data. The results shown the method could predict the intent accurately.

scholarly journals Improving the timeliness and accuracy of injury severity data in road traffic accidents in an emerging economy setting

2018 ◽  
Vol 31 (2) ◽  
pp. 140-146
Author(s):  
Carlos Lam ◽  
Chang-I Chen ◽  
Chia-Chang Chuang ◽  
Chia-Chieh Wu ◽  
Shih-Hsiang Yu ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Emerging Economy ◽  
Road Traffic Accidents

Children and Terror Casualties Receive Preference in ICU Admissions

2012 ◽  
Vol 6 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Kobi Peleg ◽  
Michael Rozenfeld ◽  
Eran Dolev ◽  
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Age Groups ◽  
Trauma Patients ◽  
Trauma Centers ◽  
Severe Injuries ◽  
Level I ◽  
Hospital Emergency Departments ◽  
Trauma Type

ABSTRACTObjective: Trauma casualties caused by terror-related events and children injured as a result of trauma may be given preference in hospital emergency departments (EDs) due to their perceived importance. We investigated whether there are differences in the treatment and hospitalization of terror-related casualties compared to other types of injury events and between children and adults injured in terror-related events.Methods: Retrospective study of 121 608 trauma patients from the Israel Trauma Registry during the period of October 2000-December 2005. Of the 10 hospitals included in the registry, 6 were level I trauma centers and 4 were regional trauma centers. Patients who were hospitalized or died in the ED or were transferred between hospitals were included in the registry.Results: All analyses were controlled for Injury Severity Score (ISS). All patients with ISS 1-24 terror casualties had the highest frequency of intensive care unit (ICU) admissions when compared with patients after road traffic accidents (RTA) and other trauma. Among patients with terror-related casualties, children were admitted to ICU disproportionally to the severity of their injury. Logistic regression adjusted for injury severity and trauma type showed that both terror casualties and children have a higher probability of being admitted to the ICU.Conclusions: Injured children are admitted to ICU more often than other age groups. Also, terror-related casualties are more frequently admitted to the ICU compared to those from other types of injury events. These differences were not directly related to a higher proportion of severe injuries among the preferred groups.(Disaster Med Public Health Preparedness. 2012;6:14–19)

Environmental factors affecting the frequency of road traffic accidents: a case study of sub-urban area of Pakistan

2019 ◽  
Vol 26 (12) ◽  
pp. 11674-11685 ◽  
Author(s):  
Hafiz Mohkum Hammad ◽  
Muhammad Ashraf ◽  
Farhat Abbas ◽  
Hafiz Faiq Bakhat ◽  
Saeed A. Qaisrani ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Urban Area ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Factors Affecting

scholarly journals High Mortality from Major Vascular Trauma in Traffic Accidents: A Population-Based Study

2019 ◽  
Vol 109 (4) ◽  
pp. 328-335
Author(s):  
B. K. Johannesdottir ◽  
U. Johannesdottir ◽  
T. Jonsson ◽  
S. H. Lund ◽  
B. Mogensen ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Population Based ◽  
Vascular Trauma ◽  
Term Survival ◽  
Mortality And Morbidity ◽  
New Injury Severity Score ◽  
Discharged Patients ◽  
Discharge From Hospital

Background and Aims: Injuries involving major arteries are an important cause of mortality and morbidity, most often from road traffic accidents. Our aim was to study the outcome of major vascular trauma from traffic accidents in an entire population, including patients who die at the scene and those who reach hospital alive. Materials and Methods: This was a retrospective analysis of all patients who sustained major vascular trauma in traffic accidents in Iceland from 2000 to 2011. Patient demographics, mechanism, and location of vascular injury and treatment were registered. Injury scores were calculated and overall survival estimated. Results: There were 62 individuals (mean age 44 years, 79% males) with 95 major vascular traumas, giving an incidence of 1.69/100,000 inhabitants (95% confidence interval: 1.27–2.21). A total of 33 died at the scene and 8 during transportation to hospital but 21 (34%) reached hospital alive. Most patients who succumbed had thoracic major vascular traumas (76%) or abdominal major vascular traumas (23%). Mean new injury severity score for the 21 admitted patients was 44. A total of 18 were operated with vascular repair, 3 with endovascular stent graft insertion. The mean hospital stay for discharged patients was 34 days. Altogether, 15 of the 62 patients (24%) survived to discharge from hospital, with a 5-year survival of 86% for discharged patients. Conclusion: Every other patient with major vascular trauma following traffic accidents died at the scene and a further 13% died during transportation to hospital, most of whom sustained major vascular trauma to the thoracic aorta. However, one-third of the patients reached hospital alive and 71% of them survived to discharge, with excellent long-term survival.

scholarly journals Road traffic accidents on Senai-Desaru expressway

2018 ◽  
Vol 250 ◽  
pp. 02002 ◽  
Author(s):  
Nordiana Mashros ◽  
SittiAsmah Hassan ◽  
Yaacob Haryati ◽  
Mohd Shahrir Amin Ahmad ◽  
Ismail Samat ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Contributing Factors ◽  
Property Damage ◽  
Crash Frequency ◽  
Crash Data ◽  
Run Off ◽  
Accident Data ◽  
Road Segments

Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.

scholarly journals Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

2020 ◽  
Vol 17 (11) ◽  
pp. 4111
Author(s):  
Ali J. Ghandour ◽  
Huda Hammoud ◽  
Samar Al-Hajj
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Injury Severity ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Outcome Variable ◽  
Contributing Factors ◽  
Road Crashes ◽  
Factors Associated ◽  
Ensemble Machine Learning

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

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