A model of latent class multinomial logit to investigate motorcycle accident injuries

Dewa Made Priyantha Wedagama*, Dilum Dissanayake

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The analysis of road traffic accidents will be more complicated with the existence of heterogeneity in the raw data of traffic accident. This study conducts a specific attention to unobserved heterogeneity issues by classifying homogeneous attributes of two different accident data classes. A latent class approach was used to investigate the contributing factors and their influences of motorcycle accident injury outcomes. The data set from 2010 to 2015 consisting 1061 motorcycle accident injuries on Denpasar-Gilimanuk and Denpasar-Singaraja national road networks in Tabanan Regency, Bali were employed as the case study. This study found that male motorists and head on collisions significantly influencing fatal motorcycle injuries. In addition, collisions between motorcycle and the other types of motor vehicles, day time accidents, male motorists at fault, right angle and head on collisions significantly associated with serious motorcycle accident injuries. This result may represent many primary factors which considerably diverge across a traffic accident injury observation. The contributing factors identified in this study were further discussed and some countermeasures for reducing the motorcycle accident injuries were proposed.

Original languageEnglish
Pages (from-to)422-429
Number of pages8
JournalEngineering and Applied Science Research
Volume47
Issue number4
DOIs
Publication statusPublished - 2020

Bibliographical note

Funding Information:
Data collection for this study was supported by the University of Udayana, Bali, Indonesia.

Publisher Copyright:
© 2020, Paulus Editora. All rights reserved.

Keywords

  • Accident injuries
  • Latent class analysis
  • Motorcycle
  • National road network

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications

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