Abstract
Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.
Highlights
Every year, around 1.35 million people die as a consequence of road crashes worldwide [1]
We develop a driving behavior-based relative risk evaluation model, in which both the frequency and severity level of risky driving behaviors are considered, and the concept of relative risk instead of absolute risk is proposed
To assess the crash risk of a driver based on his/her driving behavior, in this study we develop a driving behavior-based relative risk evaluation model, using a nonparametric optimization method
Summary
Around 1.35 million people die as a consequence of road crashes worldwide [1]. The huge costs in health services, and the added burden on public finances due to road traffic injuries and fatalities, representing approximately 1–3% of GDP in most countries, have become increasingly socially unacceptable [2]. Road crashes, previously regarded as random, unavoidable ‘accidents’, have been increasingly identified as a preventable public health problem due to the development of a better understanding of the nature of crashes over the past decades [3,4,5,6]. Risky or abnormal behaviors are highly related to road crashes. It is of great importance to estimate each driver’s risk before his/her risky driving behaviors lead to a crash. Drivers can better understand their current driving ability, and various interventions can be taken to improve their driving performance. A dynamic adjustment of vehicle insurance premiums can be applied, and a “reward system” can be introduced to encourage safer driving [8,9]
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