Evaluation of safety interventions on risky driving behavior using data from a novel naturalistic driving experiment

This paper aims to evaluate the H2020 project i-DREAMS safety interventions impact on risky driving with a specific focus on speeding events. In this framework, a negative binomial model is developed to examine the correlations between ‘high’ severity speeding events per 100 km where the driver exceeds the proposed speed limit, the safety intervention schemes, and other risky driving factors. Additionally, a Friedman test is conducted to further explore the differences in risky driving behavior among the different intervention schemes. The findings highlight the positive impact of combining real-time and post-trip interventions, in reducing ‘high’ speeding events. Moreover, it is revealed that the presence of harsh acceleration, deceleration, and steering, and fatigue events amplifies the frequency of speeding. Overall, these findings emphasize the efficacy of specific intervention schemes and highlight the importance of addressing multiple risk factors simultaneously to enhance driver behavior and ensure road safety.</p

Evaluation of safety interventions on risky driving behavior using data from a novel naturalistic driving experiment

This paper aims to evaluate the H2020 project i-DREAMS safety interventions impact on risky driving with a specific focus on speeding events. In this framework, a negative binomial model is developed to examine the correlations between ‘high’ severity speeding events per 100 km where the driver exceeds the proposed speed limit, the safety intervention schemes, and other risky driving factors. Additionally, a Friedman test is conducted to further explore the differences in risky driving behavior among the different intervention schemes. The findings highlight the positive impact of combining real-time and post-trip interventions, in reducing ‘high’ speeding events. Moreover, it is revealed that the presence of harsh acceleration, deceleration, and steering, and fatigue events amplifies the frequency of speeding. Overall, these findings emphasize the efficacy of specific intervention schemes and highlight the importance of addressing multiple risk factors simultaneously to enhance driver behavior and ensure road safety.</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

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

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

Effectiveness evaluation of the interventions. Deliverable 7.2 of the EC H2020 project i-DREAMS

The overall objective of the i-DREAMS project is to set up a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment was made to monitor and determine if a driver is within acceptable boundaries of safe operation (i.e., Safety Tolerance Zone). Moreover, the i-DREAMS platform offers a series of in-vehicle interventions, meant to prevent drivers from getting too close to the boundaries of unsafe operation and to bring them back into the safety tolerance zone while driving. This deliverable focusses on evaluating the effectiveness of the i-DREAMS interventions in improving drivers’ safety outcomes. The work here will evaluate the impact of the real-time driver interventions, post-trip driver feedback, and gamification interventions, in order to assess their impact on driving behaviour and driver state. Comparisons will be made between the different countries for which data are available, between the different interventions, and between the different outcome variables. The data collected in on-road field trials are analysed for private drivers (passenger cars) and professional drivers (trucks and busses). The analysis of the interventions is formed of two main areas: outcome evaluation and process evaluation. Outcome evaluation, also known as effect evaluation, measures the effectiveness of the intervention. More specifically, it assesses whether the targeted factors of the on-road trials changed as a result of the intervention or not. The outcome evaluation of the on-road trials will examine whether the i-DREAMS interventions influenced the following four areas: safety outcomes, safety promoting goals, performance objectives, and change objectives. These four areas are part of the logic model of change behind the i-DREAMS interventions. Process evaluation assesses which parts of the intervention were implemented as intended, and which were not. For private drivers, the results show that there was a statistically significant decrease in events from Phase 1 to Phase 4. This suggests that the i-DREAMS system had a positive impact on the measured safety outcomes and succeeded in keeping drivers in the first level of the STZ for more of their journey. When individual phase changes are considered, the most significant results were seen from Phase 3 to Phase 4. This suggest that the addition of the gamification elements had a significant impact on safety outcomes, and further supports the conclusion that the full system provides the most effective results. However, differences were found when each country was analysed individually, which were statistically significant, though there is not a clear reason why this would be so. Furthermore, differences were also found between drivers within countries. In each country, between two thirds to three quarters of drivers showed improved outcomes (i.e., a reduction in events), but the remainder had worse outcomes (an increase in events). It’s not clear from the data why some individuals responded positively to the technology and others did not, and further work is needed to understand why the system has such varied effects on different drivers. For all countries, drivers engaged more with the app in Phase 4 of the trial compared with Phase 3, after the introduction of the gamification features. Although the ‘trips’ and ‘scores’ menu were the functions most used by drivers (functions that were available in both phases), the data suggests that the gamification functions were more engaging and held attention more consistently. The generic information in the app (hints, tips etc.) was less appealing to users. They found more interest in personalised feedback such as their trip information, goals, and position on the leader board. The data also suggests a link between app usage and performance outcome; nearly all the drivers who used the app heavily showed improved outcomes. It would be interesting to investigate this further to determine whether there is a causal effect between these results. Generally, the i-DREAMS system showed less positive impact with professional drivers compared to private drivers. Specifically, a lower proportion of the professional drivers showed improved outcomes, and little significant change was seen in terms of safety outcomes. Where there were significant results, these were most often increases in events, i.e., worse outcome. Again, it is not obvious why this result is observed. The only statistically significant improved outcome was for truck drivers, which was for ‘total’ high severity events specifically between Phase 1 and Phase 2. Therefore, it can tentatively be concluded that the system had a positive impact on the most severe events. Process evaluation results were only available for Truck drivers, but showed similar results to private drivers, with more app engagement in Phase 4 compared to Phase 3, after the introduction of gamification features. This further supports the value of gamification features. The intention was to use the results to inform the ranking of interventions and provide an assessment of which intervention schemes are most effective. However, given the varied results between countries and transport modes, it is difficult to conclude a definitive ranking of the different interventions. The results indicate that the full system (real-time warnings plus app feedback plus gamification features in the app) provides the most significant positive impact on driver outcome. For private drivers, the analysis showed that most significant positive change was seen in Phase 4 of the trial, i.e., the gamification features, however it cannot be said that those alone were the most effective, as they were tested in combination with the other interventions. However, the data does suggest that app feedback on its own is less effective than when the app also includes gamification features. For truck drivers, we can tentatively conclude that the real-time interventions had the most impact, however more data is needed to support this. Lastly, the rail mode was included in i-DREAMS to broaden the application of the i-DREAMS platform which was originally designed for use in road vehicles. Trams operate within a mixed-traffic environment, driving on both segregated track, and shared, multi-user road. Therefore, aspects of the i-DREAMS platform can be applied to trams and may be beneficial to tram driving safety and risk mitigation. Two main studies were carried out to assess the use of the i-DREAMS platform in trams. The first was a simulator study to test the real-time element of the platform and the second was a focus group study to assess the potential use of the post-trip feedback app in the tram context. The tram simulator study suggests that the i-DREAMS system and associated warnings offer several benefits for tram driving operations. Firstly, as instances of speeding are rare, the speed alert would be more helpful as a warning before the occurrence of speeding, alerting the drivers they are approaching the limit, or more effective as a constant in-cab reminder of the current speed limit. The concept of a vulnerable road user (VRU) warning could be beneficial to tram drivers operating in mixed traffic environments, however, it was clear that the VRU warning needs to be developed to take into account specific aspects of tram driving and there is a concern about it being triggered too often. The fatigue warning could also potentially be beneficial as a warning before the existing fatigue monitoring device alerts, as a prompt to drivers to consider their alertness or take a break. Tram drivers suggested that the app would be most useful in identifying issues that were common to drivers and as a self-evaluation tool. They were more sceptical about the gamification elements, in particular the leader board, and expressed views that competition could have a negative impact on safety and is therefore not desired.</p