Abstract
Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.
Highlights
Driving is a complex task influenced by many circumstances, some of that are directly related to driver behavior and personality and others are more concerned with the driving environment.Almost all the traffic crashes and/or traffic conflicts occur from the conjugation of these two factors: behavior and environment
The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers
Given that the aim of this study is to explore driver monitoring systems (DMSs) data to analyze risk factors contributing to driver inattention, the literature overview presented is devoted to previous naturalistic driving studies (NDSs) focused on this topic
Summary
Driving is a complex task influenced by many circumstances, some of that are directly related to driver behavior and personality and others are more concerned with the driving environment. NDSs provide the environmental conditions required to engage drivers to distraction and drowsiness, as well as the type of pre-crash driver behavior data that is necessary to research the relative near crash/crash risk associated to inattention This method traditionally requires that reliable and unobtrusive instrumented packages be installed on vehicles to monitor the driver, the vehicle, and the surrounding environment. Data was gathered by two types of DMS that monitor and alert the driver when he/she gets distracted or drowsy: one type consisted in dedicated devices installed on commercial fleet vehicles, and the other was a smartphone application available to the general public Both DMSs have been developed by HealthyRoad Biometric Systems (Porto, Portugal), a private company that develops facial biometric technology for the automotive sector. Due to the retrospective nature of the used data, the limitations of the study are debated in the last section, alongside with some closing remarks
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