Christos Katrakazas

National Technical University of Athens | NTUA · Department of Transportation Planning

Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, and post-trip interventions have gained significant importance in the past few years. This study aimed to analyze different classification techniques and examine their ability to identify dangerous driving behavior based on a dua...

The current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives...

Human behavior significantly contributes to severe road injuries, underscoring a critical road safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. The research...

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 Belgi...

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...

Higher levels of Automated driving (AD) vehicles require new allocations of functions among drivers, vehicles, and road infrastructure. The European Horizon 2020 project HADRIAN investigates how such reallocations could be practically achieved as part of Collaborative Connected and Automated Mobility (CCAM) to meet the benefit expectations of drive...

The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the interrelationship among driving task complexity, operator and vehicle coping capacity, and crash...

This paper tries to identify and investigate the most significant factors that influenced the relationship between COVID-19 pandemic metrics (i.e., COVID-19 cases, fatalities, and reproduction rate) and restrictions (i.e., stringency index and lockdown measures) with driving behavior in the entire year 2020. To that aim, naturalistic driving data f...

Driver distraction and inattention have been found to be major contributors to a large number of serious road crashes. It is evident that distraction reduces to a great extent driver perception levels as well as their decision making capability and the ability of drivers to control the vehicle. An effective way to mitigate the effects of distractio...

Introduction In the unprecedented year of 2020, the rapid spread of COVID-19 disrupted everyday activities worldwide, leading the majority of countries to impose lockdowns and confine citizens in order to minimize the exponential increase in cases and casualties. To date, very few studies have been concerned with the effect of the pandemic on drivi...

This paper tries to identify and investigate the most significant factors that influenced the relationship between COVID-19 pandemic metrics (i.e., COVID-19 cases, fatalities and reproduction rate) and restrictions (i.e., stringency index and lockdown measures) with driving behavior in the entire 2020. To that aim, naturalistic driving data for a 1...

The pandemic of COVID-19 has affected human patterns since December 2019. Since the very beginning, most countries imposed strict measures such as lockdowns and the suspension of all nonessential movements to reduce the spread of the pandemic. Therefore, mobility, road safety, and travel behavior were also significantly affected. At present, many s...

Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This i...

The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads. For this purpose, driving data were gathered through a driving simulator experiment with 37 young drivers. Additionally, a survey was conducted to collect their demographic characteristics and driving behav...

Since early 2020, strict restrictions on non-essential movements were imposed globally as countermeasures to the rapid spread of COVID-19. The various containment and closures strategies, taken by the majority of countries, have directly affected travel behavior. This paper aims to investigate and model the relationship between covid-19 restrictive...

This deliverable describes the first pool of simulation results covering nine representative pilot sites of SHOW. The initial aim of this deliverable was to provide the first pool of simulation results fed by pre-demo utilizing input from the pre-demo evaluation round and to revise the data inputs required from SHOW sites during real-life demo acti...

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 th...

The current study aims to investigate the impact of the COVID-19 pandemic on road traffic collisions, fatalities, and injuries using time series analyses. To that aim, a database containing road collisions, fatalities, and slight injuries data from Greece were derived from the Hellenic Statistical Authority (HSA) and covered a ten-year timeframe (f...

Driving simulators have become widely used tools for examining the impact of driver behaviour with respect to individual driver differences or road layout by offering a safe, realistic, and controlled environment. In this research, a driving simulator experimental design is provided for testing the main risk factors defined within the i-DREAMS proj...

Introduction: COVID-19 has disrupted daily life and societal flow globally since December 2019; it introduced measures such as lockdown and suspension of all non-essential movements. As a result, driving activity was also significantly affected. Still, to-date, a quantitative assessment of the effect of COVID-19 on driving behavior during the lock...

The conventional form of traffic interaction undergoes a notable change with the integration of automated driving systems as a new road user, into the public roads. This may be more challenging during the transition phase, while manual-driven vehicles are still on the road, and the road infrastructure is not fully ready for merging such vehicles in...

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, analys...

Introduction: Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews mo...

This document identifies all simulation tools which are used by all partners participating in Work Package 10 of the SHOW project. Their applications range from vehicle level of shared CCAVs up to mobility level, and they are used to enrich all field experiment results of the SHOW pilots. In addition, a relation of tools to application areas and to...

Road crashes are one of the critical issues in the transportation sector. Crash studies aim to establish the relationship of crash occurrences with driver, environment and traffic factors. Lack of a large disaggregate driving or accident data constrains the study of driver factors and driving maneuvers. Moreover, the usual outcome of these studies...

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psy...

In a rapidly changing world, transportation is a big determinant of quality of life, financial growth and progress. New challenges (such as the emergence of the COVID-19 pandemic) and opportunities (such as the three revolutions of shared, electric and automated mobility) are expected to drastically change the future mobility landscape. Researchers...

The spread of the new coronavirus COVID-19, has led to unparalleled global measures such as lockdown and suspension of all retail, recreation and religious activities during the first months of 2020. Nevertheless, no scientific evidence has been reported so far with regards to the impact on road safety and driving behavior. This paper investigates...

Connected and Autonomous Vehicles (CAVs) will progressively change the functionality of current transportation systems, promising enhanced safety for all traffic participants. Furthermore, there is a palpable connection between road safety and cyber security breaches, as malicious software could lead to unexpected behavior of CAVs triggering collis...

Highway safety has attracted significant research interest in recent years, especially as innovative technologies such as connected and autonomous vehicles (CAVs) are fast becoming a reality. Identification and prediction of driving intention are fundamental for avoiding collisions as it can provide useful information to drivers and vehicles in the...

Advanced vehicle automation and the incorporation of more digital technologies in the task of driving, bring about new challenges in terms of the operator/vehicle/environment framework, where human factors play a crucial role. This paper attempts to consolidate the state-of-the-art in driver state measuring, as well as the corresponding technologie...

Research on connected and automated vehicles (CAVs) has been gaining substantial momentum in recent years. However, the vast amount of literature sources results in a wide range of applied tools and datasets, assumed methodology to investigate the potential impacts of future CAVs traffic, and, consequently, differences in the obtained findings. Thi...

The interaction among pedestrians and human drivers is a complicated process, in which road users have to communicate their intentions, as well as understand and anticipate the actions of users in their vicinity. However, road users still ought to have a proper interpretation of each others’ behaviors, when approaching and crossing the road. Pedest...

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware moti...

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic databa...

This paper examines the theory and application of a recently developed machine learning technique namely Relevance Vector Machines (RVMs) in the task of traffic conditions classification. Traffic conditions are labelled as dangerous (i.e. probably leading to a collision) and safe (i.e. a normal driving) based on 15-minute measurements of average sp...

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-c...