An integrated GIS–MCDM and statistical analysis approach for assessing heavy vehicle accident outcomes

Kaya Ö., KABAKUŞ N.

International Journal of Crashworthiness, 2026 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1080/13588265.2026.2679750
  • Dergi Adı: International Journal of Crashworthiness
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Spatial analysis, heavy vehicle accidents, binary logistic regression, risk assessment, traffic safety
  • Atatürk Üniversitesi Adresli: Evet

Özet

Analysis of heavy vehicle accidents is critical for traffic safety, as such crashes often result in serious injuries, fatalities, material damage, traffic disruptions, and economic losses. This study proposes an integrated approach combining geographic information systems (GIS), multi-criteria decision-making (MCDM), and statistical analysis to examine heavy vehicle accidents in Türkiye between 2015 and 2019. The methodology includes: (i) examining accident records and identifying 21 independent variables and 93 sub-variables, (ii) calculating their priority values using the fuzzy Logarithm Methodology of Additive Weights, (iii) conducting GIS-based spatial analyses, and (iv) applying binary logistic regression to evaluate accident outcomes as injury or fatality. The results indicate that road, environmental, accident-related, and driver-related factors significantly affect crash severity. Fuzzy LMAW results show that driver violations have the greatest negative impact. Spatial analyses reveal that severe crashes are mainly concentrated in western, north-western, south-western, and central Türkiye.