To determine accurately the number of serious injuries at EU level and to compare serious injury rates between different countries it is essential to use a common definition. In January 2013, the High Level Group on Road Safety established the definition of serious injuries as patients with an injury level of MAIS3+(Maximum Abbreviated Injury Scale). Whatever the method used for estimating the number or serious injuries, at some point it is always necessary to use hospital records. The aim of this paper is to understand the implications for (1) in/exclusion criteria applied to case selection and (2) a methodological approach for converting ICD (International Classification of Diseases/Injuries) to MAIS codes, when estimating the number of road traffic serious injuries from hospital data. A descriptive analysis with hospital data from Spain and the Netherlands was carried out to examine the effect of certain choices concerning in- and exclusion criteria based on codes of the ICD9-CM and ICD10. The main parameters explored were: deaths before and after 30 days, readmissions, and external injury causes. Additionally, an analysis was done to explore the impact of using different conversion tools to derive MAIS3 + using data from Austria, Belgium, France, Germany, Netherlands, and Spain. Recommendations are given regarding the in/exclusion criteria and when there is incomplete data to ascertain a road injury, weighting factors could be used to correct data deviations and make more real estimations.

Psychological discussions of the background of risk behaviour in road traffic mainly point on two different theoretical approaches. First, risk behaviour may be understood as rational behaviour, trading off costs and benefits of alternative options. On the other hand, the motivational background of risk behaviour takes center stage which can not be described sufficiently by rational choice models. Both approaches are outlined and put into the context of traffic psychological behaviour and decision models. Successful intervention to risk behaviour in road traffic in particular has to include non rational background of behaviour. / Die psychologische Diskussion um Hintergründe des Risikoverhaltens im Straßenverkehr wird wesentlich durch zwei unterschiedliche theoretische Ansätze geleitet. Einmal wird Risikoverhalten als rationales Verhalten verstanden, das Kosten und Nutzen alternativer Möglichkeiten abwägt. Zum anderen stehen motivationale Hintergründe des Risikoverhaltens im Mittelpunkt, die nicht mit klassischen Rational-choice-Ansätzen zu beschreiben sind. Die Ansätze werden umrissen und in den Kontext verkehrspsychologischer Handlungs und Entscheidungsmodelle gestellt. Eine erfolgreiche Beeinflussung des Risikoverhaltens im Straßenverkehr muss gerade die nicht-rationalen Verhaltenshintergründe ansprechen.

QC 20101216

Roads from the Perspective of the Environmental Law Abstract In this Thesis, in light of amendment of the Line Act which severely dampens the concerned authorities' means of protecting the environmental public interests, I present a brief introduction to the environmental protection legislation in relation to roads, road construction and road traffic, showing the state of related present-day legislation and means through which the environmental public interests are protected during the road construction process as of today. To this end, I present a brief description of negative effects of roads on the environment, i. e. environmental fragmentation, land annexation, pollution of land and air, noise and artificial lighting. Next, I am showing an in-depth analysis of means through which the Czech law deals with these negative effects and which means of protection against them it offers. I show the road construction process, means of environmental public interest protection in this process and administrative-legal acts through which the means of protection are applied. Here I mostly focus on the environmental public interests in environmental protection, limiting the environmental fragmentation and protection of land. Next, I present the current state of legislation dealing with the negative aspects of road...

Magister Artium (Social Work) - MA(SW) / Recovery from traumatic brain injury (TBI) can be a lengthy and challenging process for survivors, their families, and health and welfare services. Globally, South Africa has the second-highest prevalence of TBI that is due to road traffic accidents. The growing challenges for TBI survivors are the financial constraints, health implications, and the psychosocial support required by survivors of TBI and their families, which impacts family reintegration, post-hospitalisation. The aim of the study was to understand the challenges experienced by TBI survivors with regards to the psychosocial support services for family reintegration. A qualitative approach, using an exploratory-descriptive design was used. The person-centred perspective was chosen as a theoretical framework.

Porozumění chování silniční dopravy je klíčem pro její efektivní řízení a organizaci. Tato úloha se stává čím dál více důležitou s rostoucími požadavky na dopravu a počtem registrovaných vozidel. Informace o dopravní situaci je důležitá pro řidiče a osoby zodpovědné za její řízení. Naštěstí v posledních několika dekádách došlo k značnému rozvoji technologií pro monitorování dopravní situace. Stacionární senzory, jako jsou indukční smyčky, radary, kamery a infračervené senzory, mohou být nainstalovány na důležitých místech. Zde jsou schopny měřit různé mikroskopické a makroskopické dopravní veličiny. Bohužel mnohá měření obsahují nekorektní data, která není možné použít při dalším zpracování, například pro predikci dopravy a její inteligentní řízení. Tato nekorektní data mohou být způsobena poruchou zařízení nebo problémy při přenosu dat. Z tohoto důvodu je důležité navrhnout obecný framework, který je schopný doplnit chybějící data. Navíc by tento framework měl být také schopen poskytovat krátkodobou predikci budoucího stavu dopravy. Tato práce se především zabývá vybranými problémy v oblasti doplnění chybějících dopravních dat, predikcí dopravy v krátkém časovém horizontu a predikcí dojezdových dob. Navrhovaná řešení jsou založena na kombinaci současných metod strojového učení, například Support vector regression (SVR) a multikriteriálních evolučních algoritmů. SVR má mnoho meta-parametrů, které je nutné dobře nastavit tak, aby byla dosažena co nejkvalitnější predikce. Kvalita predikce SVR dále silně závisí na výběru vhodné množiny vstupních proměnných. V této práci používáme multiktriteriální optimalizaci pro optimalizaci SVR meta-parametrů a množiny vstupních proměnných. Multikriteriální optimalizace nám umožňuje získat mnoho Pareto nedominovaných řešení. Mezi těmito řešeními je možné dynamicky přepínat dle toho, jaká data jsou aktuálně k dispozici tak, aby bylo dosaženo maximální kvality predikce. Metody navržené v této práci jsou především vhodné pro prostředí s velkým množstvím chybějících hodnot v dopravních datech. Tyto metody jsme ověřili na reálných datech a porovnali jejich výsledky s metodami, které jsou v současné době používány. Navržené metody poskytují lepší výsledky než stávající metody, a to především ve scénářích, kde se vyskytuje mnoho chybějících hodnot v dopravních datech.

Jährlich sterben weltweit 1,35 Millionen Menschen durch Verkehrsunfälle. Ob Cloud-Computing zu einer Reduzierung dieser Zahl beitragen wird, wird in der vorliegenden Arbeit untersucht. Zunächst werden Cloud-Anwendungen ermittelt, welche im Straßenverkehr verwendet werden und Einfluss auf die Straßenverkehrssicherheit haben. Anschließend wird deren genauer Einfluss auf die Straßenverkehrssicherheit durch die Betrachtung von Stu-dienergebnissen, Herstellerangaben sowie eigenen Kalkulationen untersucht. Zudem werden die Gefahren von Cyberangriffen und Netzausfällen für die ermittelten Anwendungen analysiert. Es folgt eine kritische Diskussion sowie die Zusammenfassung des Erarbeiteten: Cloud-Computing ermöglicht durch den einfachen, risikoarmen Zugang zu unlimitierten Rechenressourcen die Entwicklung und den Betrieb von Anwendungen, die Einfluss auf die Verkehrssicherheit nehmen. Es ist davon auszugehen, dass die Anwendungen bei einer weiter voranschreitenden Felddurchdringungsrate positiven Einfluss auf die Straßenverkehrssicherheit in Deutschland und anderen Industriestaaten nehmen und damit aktiv zur Reduzierung der Anzahl Verkehrstoter und -verletzter beitragen werden. Cloud-Computing wird somit ein indirekt positiver Einfluss auf die Straßenverkehrssicherheit zugeschrieben. / 1.35 million people die in traffic accidents worldwide every year. Whether cloud computing contributes to a reduction of this number is examined in the present work. First, cloud com-puting applications are identified, which are used in road traffic and have an impact on traffic safety. The exact influence on traffic safety is then examined by considering study results, manufacturer information and through own calculations. In addition, the dangers of cyber-attacks and network failures are examined in the context of the identified applications. This is followed by a critical discussion and a summary of what has been worked out: Cloud computing enables the development and operation of applications that affect traffic safety through simple, low-risk access to unlimited computing resources. It can be assumed that the applica-tions will have a positive influence on traffic safety in Germany and other industrialized countries and will thus actively contribute to reducing the number of deaths and injuries, given that the penetration rate is further increasing. Cloud computing is thus attributed an indirect positive influence on road safety.

In a time when urban areas continue to expand, environmental noise pollution especially from road traffic remains a big challenge. This study was aimed at using open-source GIS tools to predict road traffic noise pollution using the mid-sized city of Gävle as a case study. The noise indicators measured were the equivalent day (Lday), evening (Levening), nighttime (Lnight), and the equivalent daily average (Lden). Traffic data (composition and flow of vehicles on selected roads), traffic source characteristics (road gradient, road surface type), and buildings (geometry) were integrated into Quantum GIS (QGIS) using the CNOSSOS-EU prediction method packaged in OpeNoise, a QGIS plug-in. The resultant noise levels at receiver points were interpolated using the Inverse Distance Weighting method to create noise maps for the city.  The results showed the maximum equivalent day, evening, nighttime predicted noise levels at 85 dB (A), 80 dB (A), 75 dB (A) respectively while the maximum for overall daily average noise level predicted was 85 dB (A). These limits far exceed population exposure threshold limits for the onset of annoyance (55 dB (A)) and sleep disturbance (40 dB (A)). This result is indicative of a poor sound acoustic environment. The pattern of noise level across the city was found to follow street connectivity and traffic intensity. The maximum noise levels were clustered around the highway. Within the city, areas with the highest noise levels were found close to main roads. Residential areas served by service roads were areas with the lowest noise levels. Predicted daytime noise levels (Lday) were compared with 60-second measurements of equivalent noise levels measured at 85 locations during the day in residential and mixed land use areas in the city. The mean of differences between predicted and observed noise levels was found at +1 dB for both residential and areas of mixed land use respectively. Correlation and regression analyses performed for observed and predicted values showed an initial weak positive association with a correlation coefficient of 0.21. However, when outliers were excluded, a correlation coefficient of 0.69 was observed indicating a strong association and linear relationship between the observed and predicted noise levels. Most outliers were underestimations recorded in residential areas at hidden facades. These were attributed to local effects at the measuring locations and assumptions made for building diffraction. The application of the CNOSSOS-EU method in this study did not consider attenuation from ground reflection and terrain effects. Despite these limitations, the results show that the CNOSSOS-EU has good predictive power. However, this study has only been exploratory in nature. It is recommended that further studies be performed with this model as well as in comparison with other models to find the one that best reflects the acoustic environment of the city. A wide application of the CNOSSOS-EU method across several cities will be integral in increasing our understanding of its strengths and weaknesses.

No / The operator of a traffic control centre has to select the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control actions that need to be considered during the decision making process. The identification of suitable control actions for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic actions for a number of control measures in a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural networks, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.

In my thesis I deal with the effects of noise on real estate prices in our capital city. The aim is to estimate the traffic noise impact on property prices, using the hedonic price method. Hedonic price analysis is a useful tool for evaluating the impact of environmental policies, especially for assessing measures aiming to reduce traffic noise, and for apprising landscape architecture during the forming of green quiet areas in the city. In the theoretical part of the thesis I deal with the noise characteristics, hedonic price method, and searches of foreign studies that are centered on the benefits of the silence in households. From these studies, I deduced how the noise affects the real estate market, defined the determinants of the real estate valuation, and showed the appropriate format of these determining variables. The practical part of my work focuses on estimating the effect of particular characteristics of a property on its price. From the noise and real estate price data plotted on the map of Prague, I evaluate the relationship between the noise reading and the real estate price, using the econometric analysis. According to my findings, the negative correlation has been proven to be of a significant measure.