Analysis of distracted driving crashes in New Jersey using mixed logit model

Ahmed Sajid Hasan, Muntahith Mehadil Orvin, Mohammad Jalayer, Eric Heitmann, Joseph Weiss

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables. Method: In this study, we analyzed data on five years (2015–2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity. Results: We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it. Practical Applications: These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity.

Original languageEnglish (US)
JournalJournal of Safety Research
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality

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