Examining the relationship between impaired driving and past crash involvement in Europe: Insights from the ESRA study

Driving under the influence of alcohol, drugs and fatigue are all important factors of crash causation. Exploring the link between driver attitudes and crash involvement provides understanding on these important issues. To that end, questionnaire answers of car drivers disclosing their attitudes on the impacts of driving under the influence of alcohol, drugs and fatigue, and their relationship with past crash involvement as car drivers were analysed. A two-step approach is adopted: Principal Component Analysis (PCA) was employed to consolidate relative questions in numeric factor quantities. Afterwards, binary logistic regression was implemented on the calculated component scores to determine the impact of perspectives of road users for each factor on past crash involvement of car drivers. Data from the international ESRA2015 survey were utilized. PCA indicated that it is possible to meaningfully merge 29 ESRA2015 questions relevant to driving under the influence of alcohol, drugs and fatigue into 8 informative components accounting for an adequate percentage of variance. Binary logistic analysis indicated that components involving overall personal and communal acceptance of impaired driving, overall and past year personal behaviour towards impaired driving and frequency of typical journey checks by traffic police were all quantities positively correlated with past crash involvement

D6.3 An integrated model of driver-vehicle-environment interaction and risk

This Deliverable aims at developing an integrated model of driver-vehicle-environment interaction and risk by: (i) identifying the most critical precursors of risk from both the task complexity and the coping capacity side, (ii) implementing an integrated model for understanding the effect of the inter-relationship of task complexity and coping capacity with risk, and (iii) comparing the performance of such models on different countries.</p

D6.2 Analysis of coping capacity factors: vehicle and operator state

The current Deliverable aimed to provide the analysis results for the coping capacity factors, both for the vehicle as well as the operator state and the effect these have on risk. This aim was pursued by: (i) identifying the most critical factors of coping capacity, (ii) developing SEM and GLM model in order to investigate the effect of ‘vehicle state’ and ‘operator state’ on the STZ level and (iii) comparing the differences between different countries and transport modes.  After making a short summary of the project’s aims and objective, the naturalistic driving experiment procedure in all of the countries involved was described along with the data acquisition, data cleaning and data aggregation procedures followed to extract the datasets that were used in the analyses. These strategies aimed to comprehend how the data were stored in the back-end database, how to deal with missing values, how to impute missing values taking into account the natural meaning of the recorded variables and how to best exploit the data for developing the Structural Equation Model (SEM). The volume, diversity and noise included in the dataset, due to the different experimental difficulties faced in each of the countries led to extensive efforts to acquire clean data.  The next section of the Deliverable describes in detail, the methodologies followed throughout the analyses. Apart from SEMs, Generalized Linear Models (GLMs) were also used and the goodness-of-fit-metrics for the models were explained.  The main results of those analyses are thoroughly described in Chapter 4 of the current Deliverable. The analysis found that age, confidence, and driving style were the strongest indicators for operator state, while vehicle age and gearbox were significant for vehicle state. Mixed results were found when looking at the correlation between coping capacity and risk in different countries and transport modes, however the majority of the modes point towards a negative correlation between coping capacity and risk (i.e. higher operator capacity leads to lower risk).  The lack of objective coping capacity indicators in the study may have contributed to the lack of coherence between all the developed models over all countries and modes. However, there was consistency in the increase of coping capacity's effect on risk throughout the phases of the experiment. Despite efforts to clean and homogenize the data, an overall "coping capacity against risk" model for a specific mode was not possible due to the volume and diversity of the data. Future trials may provide additional data to help address these limitations and produce more conclusive results.  Finally in the last chapter, conclusions are drawn for the relationship between coping capacity and risk, while explanations for the model drawbacks are given.</p

D6.1 Analysis of task complexity factors

The main goal of the i-DREAMS project was to establish a framework that enables the definition, development, testing and validation of a context-aware safety envelope for driving called the ‘Safety Tolerance Zone’ (STZ). This could be accomplished through the implementation of a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). With the i-DREAMS project, data was collected from car, truck and bus drivers during on-road trials conducted in Belgium, Germany, Greece, Portugal and the United Kingdom.  The aim of this deliverable is to analyse the impact of task complexity on risk within the context of a four-phase on-road trial. The study consisted of four consecutive phases; Phase 1 involved observing driving behaviour without intervention following the installation of the i-DREAMS system. In Phase 2, in-vehicle real-time warnings were given using adaptive Advanced Driver Assistance Systems (ADAS) while monitoring continued. Phase 3 combined in-vehicle warnings with feedback via an app, and in Phase 4, gamification features were added to the app with the added support of a web dashboard.  The aim of this report is to examine the impact of task complexity factors, such as road layout, traffic, time of day, weather, etc., on risk. The objectives are to determine which task complexity factors have the most significant impact on risk, create Structural Equation Models (SEM) to understand how task complexity affects the Safety Tolerance Zone (STZ) and compare the effects of task complexity on risk for different countries and transport modes during the four phases of the i-DREAMS road-trial.  Task complexity relates to the current status of the real-world context in which a vehicle is being operated. Since this context is consistent of various individual elements which, together, determine the complexity of the task imposed on the vehicle operator, a multi-dimensional approach in further operationalizing this concept is adopted. In particular, task complexity context is monitored via registration of road layout (i.e., highway, rural, urban), time and location, traffic volumes (i.e., high, medium, low) and weather.  In terms of the methodology, generalized linear and structural equation modelling techniques were utilized to investigate the factors that define task complexity and how it relates to risk. Both task complexity and risk were treated as latent variables, which are not directly observable. Despite a unified data collection design, technical issues such as sensor failures and driver availability arose during the data collection process in different countries. As a result, different datasets were obtained, and different variables were selected for the models to ensure their validity.  The SEM analysis involved the development of four models per risk factor (e.g., speeding and headway), one for each phase, to identify any differences in the way task complexity impacts risk. However, due to the issues mentioned earlier, it was not possible to make a direct comparison between countries or transport modes. In some cases, not only the variables that represent task complexity vary, but also the variables that represent risk differ. Thus, the results could only be interpreted on a country and transport mode basis. It is noteworthy that age and gender were not significant factors in any of the models across different countries and transport modes.  Measuring task complexity and relating this to risk was a challenging task as the number of variables that were collected and could be used was restricted and therefore, proxies were utilised. For instance, weather conditions were indicated by the use of the wipers and lighting conditions, or night-time driving was assessed by the use (or not) of the high beams.  In general, the collection of the initially planned variables was proven to be trickier than anticipated. Future research should consider these challenges and attempt to incorporate information on factors like road configuration, traffic density, and other relevant metrics that would be very useful for establishing the complexity of the driving task and its association with risk.</p

Investigating the effect of driver-vehicle-environment interaction with risk through naturalistic driving data

While mobility and safety of drivers are challenged by behavioural changes, the increasingly complex road environment has placed a higher demand on their adaptability. The ultimate goal of this paper was to identify the impact that the balance between task complexity and coping capacity had on crash risk. Towards that aim, an integrated model for understanding the effect of the inter-relationship of task complexity and coping capacity with risk was developed. A vast library of data from a naturalistic driving experiment was created in three countries (i.e. Belgium, UK and Germany) to investigate the most prominent driving behaviour indicators available, including speeding, headway, overtaking, duration, distance and harsh events. In order to fulfil the aforementioned objectives, exploratory analysis, such as Generalized Linear Models (GLMs) were developed and the most appropriate variables associated to the latent variable “task complexity” and “coping capacity” were estimated from the various indicators. Additionally, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modelled. The analyses revealed that higher task complexity levels lead to higher coping capacity by drivers. Additionally, the effect of task complexity on risk was greater than the impact of coping capacity in Belgium and Germany, while mixed results were observed in the UK.</p

Modelling the inter-relationship among task complexity, coping capacity and crash risk

Considering the significant influence of the human factor on safe driving behavior, the i-DREAMS project developed a ‘Safety Tolerance Zone (STZ)’ to define the precise boundary where self-regulated control can be maintained safely. Taking to account the framework of the i-DREAMS project, this paper endeavors to model the inter-relationship among task complexity, coping capacity (i.e. vehicle and operator state) and crash risk. A complete Structural Equation Model (SEM) was developed for each country of analysis (i.e., Belgium, United Kingdom, Germany) to describe the interactions between task complexity and coping capacity (i.e., related to both vehicle state and operator state factors). Results showed positive correlation of task complexity and coping capacity that implies that driver’s coping capacity increased as the complexity of driving task increases.</p

Framework for behaviour change implemented in real-time and post-trip interventions of the H2020 i-DREAMS naturalistic driving project

The aim of the European funded Horizon 2020 project i-DREAMS† is to develop, implement and evaluate a cluster of innovative real-time and post-trip interventions to improve road safety among both private and professional drivers operating different modes in road (i.e., car, bus, truck) and rail by means of a field trial with 600 participants in five different countries (i.e., Belgium, UK, Germany, Greece, and Portugal). This paper presents the theoretical paradigms of behavioural change adopted in the i-DREAMS interventions, and elaborates on the methodological framework used to determine intervention objectives, select suitable techniques for behavioural change, and further translate these into a series of gamification features to be integrated into an in-vehicle feedback display, and a smartphone app supported by an online web-platform. </p

State-of-the-art technologies for post-trip safety interventions

Several systems and methods have been investigated in order to develop an appropriate driver monitoring and mentoring strategy after each trip. The i-DREAMS0F 1 platform allows the implementation of post-trip interventions, meant to motivate and enable human operators to develop the appropriate safety-oriented attitude in the future. Hence, the scope of this paper is to investigate the state-of-the-art technologies utilized in four different transport modes (car, truck, bus and rail) for post-trip interventions associated with risk prevention and mitigation. Overall, several smartphone applications and web-based platforms have been explicitly designed for providing post-trip feedback to drivers, in order to identify risky driving performance, improve their behaviour and promote road safety. With regards to car-specific interventions, driver systems with gamification features and visual notifications enabled drivers to achieve a better performance. In addition, smartphone applications were found to offer a scalable, and easily implementable alternative to current road monitoring methods, although methodological challenges still remain. In trucks and buses, interventions were usually part of a broader framework (e.g. including driver coaching and management commitment) and the effects of such interventions was not be taken into account in isolation for accomplishing a sufficient safety culture change. Lastly, as of yet no post-trip interventions to improve rail drivers’ safety appear were identified in the literature

Investigating the effect of driver-vehicle-environment interaction with risk through naturalistic driving data

While mobility and safety of drivers are challenged by behavioural changes, the increasingly complex road environment has placed a higher demand on their adaptability. The ultimate goal of this paper was to identify the impact that the balance between task complexity and coping capacity had on crash risk. Towards that aim, an integrated model for understanding the effect of the inter-relationship of task complexity and coping capacity with risk was developed. A vast library of data from a naturalistic driving experiment was created in three countries (i.e. Belgium, UK and Germany) to investigate the most prominent driving behaviour indicators available, including speeding, headway, overtaking, duration, distance and harsh events. In order to fulfil the aforementioned objectives, exploratory analysis, such as Generalized Linear Models (GLMs) were developed and the most appropriate variables associated to the latent variable “task complexity” and “coping capacity” were estimated from the various indicators. Additionally, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modelled. The analyses revealed that higher task complexity levels lead to higher coping capacity by drivers. Additionally, the effect of task complexity on risk was greater than the impact of coping capacity in Belgium and Germany, while mixed results were observed in the UK.</p

Effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project: a sneak preview

This paper addresses the effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project. The project aims to setup a framework for the definition, development and validation of a context-aware ‘safety tolerance zone (STZ)’ for driving. A range of sensors are used to collect a large variety of data, which enables assessment of the STZ, and help designing interventions. Effectiveness evaluation is based on the outcome and process evaluation using the COM-B and REAIM frameworks, respectively. Preliminary results for a sample of 27 car drivers from Belgium and 26 car drivers from UK are presented. Overall, the interventions were found effective in improving driving behaviour. UK drivers have performed significantly better, while Belgian car drivers showed mixed results, mainly due to the changing mobility patterns during COVID19 pandemic. </p