Objective: Road traffic accident (RTA) and its related injuries contribute to a significant portion of the burden of diseases in Iran. This paper explores the association between driver-related factors and RTA in the country.
Methods: This cross-sectional study was conducted in Iran and all data regarding RTAs from March 20, 2010 to June 10, 2010 were obtained from the Traffic Police Department. We included 538 588 RTA records, which were classified to control for the main confounders: accident type, final cause of accident, time of accident and driver-related factors. Driver-related factors included sex, educational level, license type, type of injury, duration between accident and getting the driving license and driver's error type.
Results: A total of 538 588 drivers (91.83% male, sex ratio of almost 13:1) were involved in the RTAs. Among them 423 932 (78.71%) were uninjured; 224 818 (41.74%) had a diploma degree. Grade 2 driving license represented the highest proportion of all driving licenses (290 811, 54.00%). The greatest number of accidents took place at 12:00-13:59 (75 024, 13.93%). The proportion of drivers involved in RTAs decreased from 15.90% in the first year of getting a driving license to 3.13% after 10 years'of driving experience. Neglect of regulations was the commonest cause of traffic crashes (345 589, 64.17%). Non-observance of priority and inattention to the front were the most frequent final causes of death (138 175, 25.66% and 129 352, 24.02%, respectively). We found significant association between type of accident and sex, education, license type, time of accident, final cause of accident, driver's error as well as duration between accident and getting the driving license (all P less than 0.001).
Conclusion: Our results will improve the traffic law enforcement measures, which will change inappropriate behavior of drivers and protect the least experienced road users.
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Inj Epidemiol
December 2024
Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain.
J Safety Res
June 2024
Department of Civil, Environmental, Land, Building Engineering and Chemistry Polytechnic University of Bari, Bari 70125, Italy. Electronic address:
Introduction: Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.
Method: In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions).
Inj Epidemiol
April 2024
Pacific Institute for Research and Evaluation, 4061 Powder Mill Road, Suite 350, Beltsville, MD, 20705, USA.
Background: Pedestrians and cyclists are often referred to as "vulnerable road users," yet most research is focused on fatal crashes. We used fatal and nonfatal crash data to examine risk factors (i.e.
View Article and Find Full Text PDFJ Safety Res
February 2024
3255 Patrick Taylor Hall, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, USA. Electronic address:
Introduction: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions.
View Article and Find Full Text PDFPLoS One
February 2023
Civil Engineering Department, Sharif University of Technology, Tehran, Iran.
The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!