Citation

Thibenda M, Wedagama DMP, Dissanayake D. Safety Sci. 2022; 155: e105869.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2022.105869

PMID

unavailable

Abstract

This study investigates drivers' attitudes towards road safety in Jakarta and Hanoi. A comprehensive analysis of two datasets was achieved by multi-faceted analytical approaches of Multiple Correspondence Analysis (MCA), Cluster analysis (Hierarchical and Two-step) (CA), Principal Component Analysis (PCA) and Multinomial Logistic Regression (MLR). MCA and CA were used to segment the drivers based on their sociodemographic characteristics and driving related information where three clusters were identified from each dataset. There were 60+ attitudinal statements in the datasets, therefore dimension reduction process was conducted using PCA. Five Principal Components were generated from each dataset, four of them common to Jakarta and Hanoi ("Safe driving practices and behaviour", "Road safety enforcement and education", "Driver behaviour at signalised junctions", "Road infrastructure and roadside facilities"), the fifth component being unique to each city ("Road infrastructure design issues" for Hanoi and "Type of motorised vehicles" for Jakarta). Finally, MLR was used to investigate how the level of perception on the identified components varied across sociodemographic clusters in each city. Employed young adults in Jakarta perceive that "Driver behaviour at signalised junctions" directly influences road safety compared to teenage and young adults who are in education. The reverse is true for Hanoi. Employed young drivers in both cities are less likely to recognise the importance of "Road safety enforcement and education" compared to teenage and young drivers in education. The study shows that the perception of road safety among drivers varies based on various factors including their sociodemographic traits and driving experience.

Language: en

Keywords

Cluster analysis; Driver perception; Logistic regression; Multiple correspondence analysis; Principal component analysis; Road safety