PMID- 37928046
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231107
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 11
DP  - 2023 Nov
TI  - An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of 
      the factors influencing road accidents.
PG  - e21187
LID - 10.1016/j.heliyon.2023.e21187 [doi]
LID - e21187
AB  - The evaluation of the severity of the factors influencing road accidents with a 
      detailed severity distribution is critical to plan evidence-based road safety 
      improvements and strategies. However, currently available studies use statistical 
      and machine learning (ML) models to evaluate the severity of factors causing road 
      accidents without a detailed severity distribution. Further, most of these 
      available models require significant pre-data processing and have certain 
      data-centric limitations. However, the multi criteria decision-making (MCDM) 
      techniques have the potential to combine expert opinions for robust analysis 
      without any pre-data processing calculations. Thus, this study uses a hybrid 
      analytic hierarchy process (AHP) and the preference ranking organisation method 
      for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors 
      and characteristics that influence road accidents within the Gujarat state, using 
      injury types as criteria and minor road accident influencing factors as 
      alternatives. These 82 minor factors have been further characterised into 18 
      characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to 
      determine criterion weights, while PROMETHEE ranked all minor variables. Then, 
      after applying k-mean clustering, each ranked factor has been classified as very 
      severe, moderately severe, or severe. The result clearly highlights that 
      overspeeding, male gender, and clear weather conditions have been concluded to be 
      the highly severe factors for Gujarat state. Thus, by providing a clear severity 
      analysis and distribution of factors influencing road accidents, the proposed 
      research may help government stakeholders, researchers, and politicians build 
      severity-based road safety reforms and strategies with clarity.
CI  - (c) 2023 The Author(s).
FAU - Trivedi, Priyank
AU  - Trivedi P
AD  - Civil Engineering Department, Institute of Infrastructure Technology Research and 
      Management, [IITRAM], Ahmedabad, India.
FAU - Shah, Jiten
AU  - Shah J
AD  - Civil Engineering Department, Institute of Infrastructure Technology Research and 
      Management, [IITRAM], Ahmedabad, India.
FAU - Moslem, Sarbast
AU  - Moslem S
AD  - School of Architecture Planning and Environmental Policy, University College of 
      Dublin, D04 V1W8, Belfield, Dublin, Ireland.
FAU - Pilla, Francesco
AU  - Pilla F
AD  - School of Architecture Planning and Environmental Policy, University College of 
      Dublin, D04 V1W8, Belfield, Dublin, Ireland.
LA  - eng
PT  - Journal Article
DEP - 20231020
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10623276
OTO - NOTNLM
OT  - AHP
OT  - Factors influencing road accidents
OT  - PROMETHEE
OT  - Severity
COIS- The authors declare no conflict of interest.
EDAT- 2023/11/06 06:42
MHDA- 2023/11/06 06:43
PMCR- 2023/10/20
CRDT- 2023/11/06 04:27
PHST- 2023/04/09 00:00 [received]
PHST- 2023/10/15 00:00 [revised]
PHST- 2023/10/18 00:00 [accepted]
PHST- 2023/11/06 06:43 [medline]
PHST- 2023/11/06 06:42 [pubmed]
PHST- 2023/11/06 04:27 [entrez]
PHST- 2023/10/20 00:00 [pmc-release]
AID - S2405-8440(23)08395-0 [pii]
AID - e21187 [pii]
AID - 10.1016/j.heliyon.2023.e21187 [doi]
PST - epublish
SO  - Heliyon. 2023 Oct 20;9(11):e21187. doi: 10.1016/j.heliyon.2023.e21187. 
      eCollection 2023 Nov.