Distracted driving is an adverse driving behavior that is widely known to impair the safety of all roadway users and traffic flow. Despite the extensive research efforts on the prevalence and effects of distracted driving on roadway safety and performance, the number of vehicular crashes and fatalities resulting from distracted driving have continued to rise in recent years. This increasing trend may indicate that traditional distracted driving research efforts fail to yield effective solutions that reduce its presence on roadways. Alternatively, understanding the influential factors on the likelihood that drivers would engage in distracted driving behavior has the potential to develop effective distracted driving mitigation strategies. Recently, many studies have identified these factors that influence distracted driving behavior among passenger car drivers and few have focused on such factors affecting distracted driving behavior among drivers of large trucks. Because large truck involved crashes tend to result in more severe injury crashes and distracted driving among drivers of large trucks significantly increases crash risk, it is important to understand the factors that influence the likelihood that truck drivers would engage in distracted driving. Therefore, the objective of this thesis is to identify the factors that influence the likelihood that drivers of large trucks would engage in distracted driving behavior to aid interested stakeholders mitigate distracted driving among drivers of large trucks. This thesis applies econometric methods on stated-preference survey data distributed to drivers of large trucks to determine the factors that affect the likelihood of self-reported distracted driving behavior. Results from this analysis indicate that policies tailored to improving trucking parking, certain fatigue management strategies, and encouraging short-haul deliveries have the potential to reduce distracted driving among drivers of large trucks.
This thesis is presented in two manuscripts that expands existing distracted driving literature by identifying influential factors of distracted driving behavior among drivers of large trucks. In Chapter 2, a random parameters binary logit model is used to determine the factors influencing cell phone use while driving among truck drivers. In Chapter 3, a random parameters bivariate binary probit model is applied to determine the factors that influence truck driver engagement with driver internal and driver external sources of distractions. With the continuous technological advancements and the inherent job responsibilities of truck drivers that require such devices, it is imperative to understand these factors so that effective distracted driving countermeasures can be developed.
Key Words: Distracted Driving, Cell Phone, Large Trucks, Random Parameters, Binary Logit, Driver Inattention
Funding Statement (additional comments about funding)
This work has been funded by the US Department of Transportation’s University Transportation Center program, Grant #DTRT13-G-UTC40 through the Pacific Northwest Regional University Transportation Center (PacTrans). The authors would like to thank PacTrans for their support. Additionally, the authors would like to thank the Oregon Department of Transportation (ODOT) for their contributions through SPR 810. The findings of this study do not necessarily reflect the views of PACTRANS or ODOT.