Practical and statistical challenges in driving research

Abstract

Driving is an integral aspect of many modern societies, and motor vehicle safety is an important public health issue. With advances in sensor technology, more and more driving data are being collected by researchers, insurers, and automobile companies, which has increased the need and opportunities for statisticians to be involved in driving research. This report discusses several practical and statistical challenges in driver-level studies, including the process of defining meaningful driving metrics, issues related to "Big Data" aspects of driving research, and the principle of reproducible research.

Assessing the impact of sleep time on truck driver performance using a recurrent event model

“…Traffic safety modeling is a challenging research area due to complex nature of crashes . Many factors contribute to crash risk including driver behavior, traffic condition, environmental factors, and vehicle factors.…”

Driving the analysis: An exciting opportunity for statistical innovation in driving research

“…For example, the data structure of longitudinal driving studies is often milliseconds of data collected for extended follow‐up periods, resulting in challenging analyses that include unevenly spaced time intervals, varying environmental situations, and randomness within the driver, vehicle, and trip. It is these unique challenges that have motivated the collection of featured papers that are published in this issue and in a previous issue . This set of articles is meant to provide research and practicing biostatisticians an introduction to some of the common and complex issues in both designing and analyzing data from driving studies.…”