Affiliations 

  • 1 Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove, QLD 4059, Australia; Politeknik Sultan Mizan Zainal Abidin, Jln Paka, 23000 Dungun, Terengganu, Malaysia. Electronic address: rusdibin.rusli@hdr.qut.edu.au
  • 2 Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove, QLD 4059, Australia; Queensland University of Technology (QUT), Civil Engineering and Built Environment, Science and Engineering Faculty, 2 George St., S Block, Room 701, Brisbane, QLD 4000, Australia
  • 3 University of Queensland (UQ), School of Civil Engineering, Faculty of Engineering, Architecture, and Information Technology, St. Lucia, QLD 4072, Australia
  • 4 Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove, QLD 4059, Australia
Accid Anal Prev, 2018 Oct;119:80-90.
PMID: 30007211 DOI: 10.1016/j.aap.2018.07.006

Abstract

Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.