Citation

Marisamynathan, Perumal V, Gupta S. Transp. Res. Proc. 2020; 48: 3121-3131.

Copyright

(Copyright © 2020, Elsevier Publications)

DOI

10.1016/j.trpro.2020.08.177

PMID

unavailable

Abstract

Motorcycle crashes make up a large proportion of road casualties in all over the world, particularly in developing countries like India, where motorcycle ownership is high. Wearing helmet decreases the risk and severity of injuries. To increase helmet usage, government has introduced a mandatory helmet usage law for two-wheeler driver and pillion riders in Mumbai, India from April 2016 onwards. Thus, the study objectives are to examine the various factors affecting helmet usage behavior and develop the model for estimating helmet usage behavior of motorcycle rider's in cities. The data were collected in two different time frames, such as before and after helmet mandatory law at randomly selected ten study locations in Mumbai, India. From field survey, 28,209 and 37,245 samples were collected during 2015 and 2016 respectively. This study was performed the statistical analysis of the impact of the state strict law on helmet usage and it was found that helmet usage was increased from 62.81% to 83.53%. Further, Pearson's R, Kendall's tau, Spearman's rho correlation and analysis of variance tests were conducted for identifying the impact of each variable with helmet wearing behavior of motorcyclists. Finally, binary logistic regression model was developed to estimate helmet usage behavior of the motorcycle rider. The model was validated, and their statistical performance results denote that developed model predicts helmet usage behavior more preciously. These study findings and model outcomes can be useful for policy makers to understand about the actual conditions of helmet usage behavior in cities and increase helmet usage rate, and also create policy level decision making on reduction in traffic crashes in India.

Language: en

Keywords

Binary Logit Model; Correlation Tests; Helmet; Mandatory Law