While mobility and safety of drivers are challenged by behavioral 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 behavior 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 inter-related, 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.

The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

The year 2020 was an extraordinary year due to the COVID-19 pandemic. This pandemic resulted in lockdowns and confinements globally and emptier streets and roads. Traffic patterns and traffic composition (modal split) changed considerably during the pandemic and as a consequence the number of people killed and injured in road crashes. The aim of this research is to present the number of road fatalities and the fatality rates (fatalities per kilometer driven) in 2020 and to compare these numbers and rates with the previous period (2017–2019), a baseline. An online questionnaire was distributed among the forty countries that are members of the International Safety Data and Analysis Group (IRTAD) in the International Transport Forum and 24 were in a position to submit the requested information before the 1st of June 2021. The questionnaire requested information on the monthly number of fatalities on a national level for four years, (2017–2020) and on kilometres driven. The number of fatalities in 2020 was 17.3% lower in the 24 participating countries compared with the baseline period and the reduction is almost seven times higher than annually in these countries in the period 2010–2019. The reduction took place in spring 2020 and not so much in the remainder of the year. The highest reduction were measured among young (0–17) and elderly people (75 and older), with public transport and on motorways. With the exception of one country, reductions in fatalities have been measured in all countries, however we observe major differences between countries. Regarding fatality rates (fatalities per vehicle kilometer travelled), we also observe major differences between countries and not a stable pattern over 2020 and a remarkable increase in April 2020, the month with the largest fatality reduction. Countries with severe COVID-19 restrictions do not necessarily demonstrate the greatest reductions in road fatalities. It is recommended to carry our further analysis to find explanations for the results and for the differences between the countries.

Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating –for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.