Decoding the Factors behind Rising Fatal Roadway Crashes in Iran: an In-Depth Analysis and Predictive Modelling Study

Document Type : Original Article

Authors

1 Department of Epidemiology and Biostatistics, University of Medical Sciences, Tehran, Iran

2 Department of Civil Engineering, Imam Khomeini International University, Qazvin, Iran

3 Department of Civil and Transportation Engineering, University of Isfahan, Isfahan, Iran

4 Department of Transportation Engineering, Suranaree University of Technology, Nakhon Thailand

5 Department of Security Studies, Polish Air Force University, Dywizjonu 303 Street, No. 35, 08-521 Deblin, Poland

6 Clinical Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

7 Department of Civil Engineering, Yazd University, Yazd, Iran

8 Department of Civil & Environmental Engineering, College of Engineering, Florida A&M University-Florida State University (FAMU-FSU), 2035 E Paul Dirac Dr., Sliger Building, Tallahassee, FL 32310, USA

10.22115/cepm.2024.452006.1299

Abstract

Isfahan Province, situated at the heart of Iran, is known for its bustling roadway systems, largely due to its popular tourist sites and strategic location. Tragically, this province has witnessed a consistent rise in death rates attributed to traffic crashes from 2012 to 2017. This study's central objective was to delve into the underlying factors precipitating such fatal traffic incidents in Isfahan Province during this time frame. This rigorous, cross-sectional analytical research was executed in 2022 and encompassed an evaluation of 9,683 fatality incidents related to traffic accidents in Isfahan Province from 2012 to 2017. The dataset for our analysis comprised all death reports collated during this period by the Forensic Medicine Department of Isfahan Province. We leveraged ArcGIS software to elucidate the geographical distribution of road crashes, and a Chi-square test was employed to identify the associated risk factors. To predict these risk factors leading to death, we constructed a random parameter binary logit model incorporating heterogeneity in means and variances. The model was thoroughly analyzed using Nlogit 6 software. The logistic regression results highlighted significant correlations between seasonality, the deceased's role in the crash, type of vehicle, and city location with crash-related mortality. The provincial capital registered a higher count of fatal crashes compared to other cities. We recommend enhanced public education to foster adherence to road safety measures, curtailment of irresponsible and dangerous behaviors, and elevation in vehicle safety standards as key steps toward enforcing driving regulations.

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