Кибербезопасность в образовательных сетях

The paper discusses the possible impact of digital space on a human, as well as human-related directions in cyber-security analysis in the education: levels of cyber-security, social engineering role in cyber-security of education, “cognitive vaccination”. “A Human” is considered in general meaning, mainly as a learner. The analysis is provided on the basis of experience of hybrid war in Ukraine that have demonstrated the change of the target of military operations from military personnel and critical infrastructure to a human in general. Young people are the vulnerable group that can be the main goal of cognitive operations in long-term perspective, and they are the weakest link of the System.У статті обговорюється можливий вплив цифрового простору на людину, а також пов'язані з людиною напрямки кібербезпеки в освіті: рівні кібербезпеки, роль соціального інжинірингу в кібербезпеці освіти, «когнітивна вакцинація». «Людина» розглядається в загальному значенні, головним чином як та, що навчається. Аналіз надається на основі досвіду гібридної війни в Україні, яка продемонструвала зміну цілей військових операцій з військовослужбовців та критичної інфраструктури на людину загалом. Молодь - це вразлива група, яка може бути основною метою таких операцій в довгостроковій перспективі, і вони є найслабшою ланкою системи.В документе обсуждается возможное влияние цифрового пространства на человека, а также связанные с ним направления в анализе кибербезопасности в образовании: уровни кибербезопасности, роль социальной инженерии в кибербезопасности образования, «когнитивная вакцинация». «Человек» рассматривается в общем смысле, в основном как ученик. Анализ представлен на основе опыта гибридной войны в Украине, которая продемонстрировала изменение цели военных действий с военного персонала и критической инфраструктуры на человека в целом. Молодые люди являются уязвимой группой, которая может быть главной целью когнитивных операций в долгосрочной перспективе, и они являются самым слабым звеном Систем

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

The Comparison between IHSDM and NSM to Assess the Safety Performance of Two-Lane Rural Roads

The objectives of this research were to explore ways to assess the safety performance of two-lane rural roads in NRW (North Rein Westphalia, Germany), and in particular to identify road factors affecting accidents on rural roads. Following a wide-ranging literature review, the Interactive Highway Safety Design Model (IHSDM) was identified as worthy of further investigation for its adaptation to use. Initial investigations showed that IHSDM is a promising tool for safety and operational assessment of two-lane rural roads in Germany. Incorporating crash history data generally improves IHSDM's accuracy in crash numbers, and appears to provide a better level of "local calibration". A number of tasks were identified and undertaken to adapt IHSDM for general use here, including calibrating the Crash Prediction Module (CPM), developing a Design Policy file based on local agency standards for use within the program, and developing an importing routine for the highway geometry and accident data. This research aims to present and illustrate a comprehensive road safety method: Network Safety Management (NSM). NSM, based on the German Guidelines for Safety Analysis of Road Networks ESN, describes a methodology for analyzing road networks from the traffic safety point of view. It also helps the road administrations in detecting those sections within the network with the highest safety potential, i.e. where an improvement of the infrastructure is expected to be highly cost efficient. Suitable measures can then be derived from a comprehensive analysis of the accidents. The safety potential and the calculated cost of the measure together form the basis for an economic assessment, which is usually conducted as a cost–benefit analysis. A systematic algorithm to assess traffic accident risk in the study area was developed in this study. The algorithm helps to identify factors that have significant influence on accidents, and to identify the road sections that have high risk of accidents. This algorithm provides both geographical and statistical analysis on accident events, i.e. mapping "Safety Analysis of Road Networks ESN" and statistical techniques "cluster analysis".Diese Forschungsarbeit hat als Ziel, die Sicherheitseffizienz zweispuriger Landstraßen in Deutschland (NRW) zu berechnen und insbesondere die Unfallfaktoren auf diesen Straßen zu ermitteln. Nach intensiver Literaturrecherche wurde festgestellt, dass das in den USA entwickelte Interactive Highway Safety Design Model (IHSDM) für weitere Untersuchungen genutzt werden kann. Die anfänglichen Untersuchungen haben ergeben, dass IHSDM ein vielversprechendes Sicherheits- und Bewertungsinstrument für zweispurige Landstraßen in Deutschland ist. Unfalldaten aus den Jahren zuvor zeigen, dass das IHSDM Informationen bereitstellt, die es erlauben, Maßnahmen zu benennen, um die Unfallzahlen zu verringern. Zudem bietet es vom Ansatz her eine gute Basis für die „Kalibrierung vor Ort“. Die Untersuchungen haben ergeben, dass das IHSDM für den allgemeinen Gebrauch in Deutschland angepasst werden muss. Vor allem müssen das Crash Prediction Module (CPM) kalibriert werden, eine Design-Policy-Datei basierend auf den lokalen Richtlinien entwickelt werden und eine Import-Routine für die Straßengeometrie und die Unfalldaten entwickelt werden. Das Ziel dieser Forschungsarbeit ist, einen Vergleich der IHSDM -Methode mit der Methode„Network Safety Management NSM“herzustellen. NSM ist eine Methode, die auf den deutschen Richtlinien für Sicherheitsanalysen von Straßennetzen ESN basiert. Sie analysiert Straßennetze, wobei die Verkehrssicherheit im Blickpunkt steht, und hilft dem Straßenverkehrsamt die Abschnitte mit dem höchsten Sicherheitspotenzial innerhalb des Netzes zu erfassen, beispielsweise wenn zu erwarten ist, dass eine Verbesserung der Infrastruktur in hohem Maße kosteneffizient ist. Allerdings liefert NSM nur indirekt Ansatzpunkte für eignete Maßnahmen, die aus den umfassenden Analysen der Unfälle abgeleitet werden müssen. Somit bietet es sich an, ein an deutsche Verhältnisse angepasstes IHSDM für die Untersuchung von Unfällen und die Entwicklung von Reduktionsstrategien einzusetzen. In der vorliegenden Studie wurden beide Methoden ein systematischer Algorithmus zur Feststellung des Unfallrisikos im Untersuchungsgebiet entwickelt. Der Algorithmus hilft, ausschlaggebende Unfallfaktoren und Streckenabschnitte, die eine hohe Unfallgefahr besitzen, zu identifizieren. Er bietet sowohl geographische als auch statistische Analysen zu Unfallereignissen, wie zum Beispiel einer Kartierung der "Sicherheitsanalyse von Straßennetzen ESN" sowie statistischer Methoden wie der "Clusteranalyse"

INCORPORATION OF FUNCTIONAL CONSIDERATIONS IN HIGHWAY PAVEMENT DESIGN AND OPERATIONS

Ph.DDOCTOR OF PHILOSOPH

Undergraduate engineering and built environment project conference 2017: book of abstracts - Toowoomba, Australia, 18-22 September 2017

Book of Abstracts of the USQ Undergraduate Engineering and Built Environment Conference 2017, held Toowoomba, Australia, 18-22 September 2017. These proceedings include extended abstracts of the verbal presentations that are delivered at the project conference. The work reported at the conference is the research undertaken by students in meeting the requirements of courses ENG4111/ENG4112 Research Project

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Design and development of a bus simulator for bus driver

The bus industry is plagued by high accident costs and risks of passenger injuries. A bus simulator may offer a method of reducing accident rates by delivering targeted training to bus drivers who are most at risk. The first part of this thesis describes the design of the UK's first bus simulator, the fidelity of which was based on a thorough analysis of bus crashes. The second part describes the first studies in a multi-staged method to evaluate the training effectiveness of the simulator: face validity, effects of bus driver experience and stress on simulated performance and simulator sickness. This approach ensured that the ABS has a reasonable level of fidelity, is capable of eliciting behaviourally valid responses from bus drivers and is the first step is achieving training transfer effectiveness. The final study investigated the occurrence of self-bias in bus drivers. The conclusions drove the design of simulated scenarios to be used for bus driver training. Keywords: Bus, Simulator, Fidelity, Validity, Accidents, Driving, Stress, TrainingEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Participatory Road Design: An Investigation into Improving Roads, Drivers' Attitude and Behaviour Using Partiticipatory Design

Improving road safety is currently based mostly on Education, Enforcement and Engineering or the 3 Es. Despite these measures having saved millions of lives since their inception in around 1915, millions of people are still injured or killed in accidents worldwide annually. One relatively unexplored area is the use of driver's tacit (unspoken) knowledge to help in the reduction of accidents, particularly in the area of speed management. Participatory design may offer a way to help utilise drivers' tacit (hidden) knowledge for the improvement of speed management and road safety techniques in a positive and ethical manner. Involvement in the process may also aid in the improvement of drivers' behaviour and attitudes. Previous research in participatory design indicates that the benefits of participatory design are quick acceptance of new designs and innovative solutions to difficult problems, as well as a sense of ownership of the new artefact. My research has investigated the efficacy of using participatory design in road safety. This was done by having participants take part series of four different types of workshops aimed at improving driver behaviour and attitudes as well as road design using models. The research involved a total of 105 participants with group sizes ranging from 3 to 28 people. It was found that participatory design workshops were capable of: allowing people to redesign a variety of roads and improve them by reducing their estimated speeds, without adversely affecting other ratings such as safety, aesthetics, preference and liveability; improving self reported driver behaviour; and allowing the interaction of people from various backgrounds in a positive and stimulating environment. Workshops were also rated highly as a teaching and design tool by all those involved in the process. Finally, unlike standard participatory design processes, some workshops also included more than just the design team with the inclusion of additional participants as audience members. This was also found to be a practical method of including more people in the participatory design process without reducing the effectiveness of the process

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Design and Electronic Implementation of Machine Learning-based Advanced Driving Assistance Systems

200 p.Esta tesis tiene como objetivo contribuir al desarrollo y perfeccionamiento de sistemas avanzados a la conducción (ADAS). Para ello, basándose en bases de datos de conducción real, se exploran las posibilidades de personalización de los ADAS existentes mediante técnicas de machine learning, tales como las redes neuronales o los sistemas neuro-borrosos. Así, se obtienen parámetros característicos del estilo cada conductor que ayudan a llevar a cabo una personalización automatizada de los ADAS que equipe el vehículo, como puede ser el control de crucero adaptativo. Por otro lado, basándose en esos mismos parámetros de estilo de conducción, se proponen nuevos ADAS que asesoren a los conductores para modificar su estilo de conducción, con el objetivo de mejorar tanto el consumo de combustible y la emisión de gases de efecto invernadero, como el confort de marcha. Además, dado que esta personalización tiene como objetivo que los sistemas automatizados imiten en cierta manera, y siempre dentro de parámetros seguros, el estilo del conductor humano, se espera que contribuya a incrementar la aceptación de estos sistemas, animando a la utilización y, por tanto, contribuyendo positivamente a la mejora de la seguridad, de la eficiencia energética y del confort de marcha. Además, estos sistemas deben ejecutarse en una plataforma que sea apta para ser embarcada en el automóvil, y, por ello, se exploran las posibilidades de implementación HW/SW en dispositivos reconfigurables tipo FPGA. Así, se desarrollan soluciones HW/SW que implementan los ADAS propuestos en este trabajo con un alto grado de exactitud, rendimiento, y en tiempo real