Definition and improvements of real-time interventions to implement the Safety Tolerance Zone concept of H2020 i-DREAMS project

The H2020 i-DREAMS project aims to establish a framework for the definition, development, testing, and validation of a context-aware safety envelope for driving called the "Safety Tolerance Zone" (STZ). Main goal of i-DREAMS project is to create a platform that, considering driver background factors and real-time risk indicators related to driving performance, continuously monitors and assess in real-time the driver state and driving task complexity, to determine whether the driver is within the acceptable boundaries of safe operation. In addition, safety-oriented interventions will be developed to inform or warn the driver in real-time and at an aggregated level after the trip via an app- and web-based gamification coaching platform (post-trip intervention).The main purpose of this paper is to present the efforts and improvements made during the i-DREAMS project in relation to real-time intervention: it highlights the progress made in the selection of indicators, the definition of real-time interventions and the thresholds of the indicators used. All these elements have been updated and finalised in the last months in view of the real needs for the implementation of the i-DREAMS Safety Tolerance Zone (STZ).</div

Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored. This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations