Understanding effect of traffic and driver related characteristics on seat belt usage in Mumbai city using random parameter logit approach and time series analysis

Shivam Khaddar, Vedagiri Perumal, Shivam Gupta

Research output: Contribution to journalArticlepeer-review

Abstract

Safety seat belt usage has been a great interest to the transportation community. Understanding factors that influence driver’s decision of wearing a safety seat belt or not is essential in determining ways to enhance safety seat belt usage rate. A modeling approach is made to observe the trend of seat belt usage in Mumbai city and to understand the effect of vehicle type, ownership type, driver’s sociodemographic, and environmental characteristics on safety seat belt usage in Mumbai City. Data were collected by roadside observational surveys at various locations in Mumbai during the years 2015 through 2018. The time series model estimate confirms declining trend of drivers not wearing safety seat belt. When vehicles are disaggregated into different build types, buses are found to be associated with no use of safety seat belt as compared to other type of vehicles, and even male drivers follow the same trend in the city. By using random parameter logit model unobserved heterogeneity was captured among individuals. Findings can be used by policymakers to develop intervention strategies to increase seat belt usage in Mumbai and other cities having similar traffic characteristics and social environment features.

Original languageEnglish (US)
Pages (from-to)458-464
Number of pages7
JournalInternational journal of injury control and safety promotion
Volume27
Issue number4
DOIs
StatePublished - 2020

Keywords

  • Time trend analysis
  • random parameter logit model
  • road safety
  • safety belt use

ASJC Scopus subject areas

  • Safety Research
  • Public Health, Environmental and Occupational Health

Fingerprint Dive into the research topics of 'Understanding effect of traffic and driver related characteristics on seat belt usage in Mumbai city using random parameter logit approach and time series analysis'. Together they form a unique fingerprint.

Cite this