Die gesundheitsschädliche Wirkung unreiner Luft ist Gegenstand unzähliger Studien und wurde bereits hinreichend nachgewiesen. Der Straßenverkehr ist dabei eine der wesentlichsten Schadstoffquellen, denen der Mensch im Alltag ausgesetzt ist. Ziel dieser Arbeit ist es, die Schadstoffbelastung von Nutzern des Straßenverkehrs vertiefend abzubilden. Im Rahmen eines Literaturreviews werden insgesamt 50 wissenschaftliche Studien analysiert. Obwohl sich viele Studien mit dem Vergleich der Verkehrsmittel bezüglich der Immissionsexposition auseinandersetzen, widersprechen sich ihre Ergebnisse je nach Schadstoff regelmäßig hinsichtlich der Reihenfolge der Verkehrsmittel oder auch grundsätzlich bezüglich der Signifikanz der Verkehrsmittelwahl. Ursache dafür sind die zahlreichen Einflussfaktoren, die sich von Arbeit zu Arbeit unterscheiden und eine Vergleichbarkeit der Messergebnisse stark erschweren. Trotz einer steigenden Anzahl an Studien zum Thema mangelt es den Messmethoden an einem strengen Qualitätsstandard sowie einer ausführlichen Dokumentation der Messbedingungen. Eine Verallgemeinerung und Vergleichbarkeit der Forschungsergebnisse untereinander ist damit bislang nicht gewährleistet. Die Betrachtung der Luftqualität bildet die wissenschaftliche Grundlage, um Grenzwertüberschreitungen aufzuspüren, Gegenmaßnahmen zu erarbeiten und diese auf ihre Wirksamkeit zu kontrollieren. Die Werte stationärer Messeinrichtungen geben dabei ein nur unzureichend genaues Bild über die Immissionen, denen die Nutzer des Straßenverkehrs ausgesetzt sind. Um ein realistischeres Bild über die Immissionsbelastung von Verkehrsteilnehmern zu gewinnen, wird mittels eines mobilen Messgeräts die PM10-Belastung für Radfahrer in Teilen des Dresdner Straßennetzes bestimmt. Dabei wird deutlich, dass die gemessene Schadstoffkonzentration sowohl räumlich innerhalb einer Messstrecke als auch zeitlich im Laufe des Tages variiert. Um den weitestgehend emissionsarmen Rad- und Fußverkehr weiter zu fördern und auszubauen, ist es nötig, die Schadstoffbelastung dieser Verkehrsteilnehmer noch besser zu verstehen und quantifizieren zu können. Nur dann sind Verkehrsplaner und Entscheidungsträger in der Lage, eine Infrastruktur zu schaffen, die ihre Nutzer so wenig wie möglich gesundheitlich beeinträchtigt.

El caótico tráfico de nuestra ciudad y sumado a eso, la crisis sanitaria por la que atraviesa actualmente el país ha llevado a que gran parte de la población use la bicicleta no solo con un fin recreacional, sino también, como un medio de transporte más sostenible logrando tener un impacto directo y positivo con la salud y el cuidado del medio ambiente. El tema resulta importante debido a que hemos encontrado una gran oportunidad de negocio, el cual la experiencia de viaje del ciclista es fundamental para enfocar y proponer soluciones a sus necesidades. De acuerdo con esta premisa, el presente trabajo de investigación tiene como objetivo desarrollar, exponer y sustentar el modelo de negocio de la aplicación “Bikla Zone”, el cual ofrece como propuesta de valor: un servicio integral, especializado, con una plataforma amigable, de navegación intuitiva, rápida y dinámica, consolidando un amplio abanico de funciones, los cuales brinden solución, facilitando y simplificando las necesidades del ciclista. El método de investigación utilizado en el presente trabajo es mixto, puesto que se recogieron y se analizaron datos cuantitativos, así también como cualitativos, los cuales corresponde a la recopilación y análisis de entrevistas a profundidad. Dicha investigación abarca lo siguiente: aspectos generales del negocio, planeamiento estratégico, investigación del mercado, plan de marketing, plan de operaciones, estructura organizacional y recursos humanos, plan económico y financiero y finalmente conclusiones. / He chaotic traffic of our city and added to that, the health crisis that the country is currently experiencing has led a large part of the population to use the bicycle not only for recreational purposes, but also as a more sustainable means of transport. achieving a direct and positive impact on health and care for the environment. The issue is important because we have found a great business opportunity, in which the cyclist's travel experience is essential to focus and propose solutions to their needs. In accordance with this premise, the present research work aims to develop, expose, and sustain the business model of the “Bikla Zone” application, which offers as a value proposition: a comprehensive, specialized service, with a friendly platform, intuitive, fast, and dynamic navigation, consolidating a wide range of functions, which provide solutions, facilitating and simplifying the cyclist's needs. The research method used in this work is mixed, since quantitative data were collected and analyzed, as well as qualitative ones, which correspond to the collection and analysis of in-depth interviews. This research includes the following: general aspects of the business, strategic planning, market research, marketing plan, operations plan, organizational structure and human resources, economic and financial plan and finally conclusions. / Trabajo de investigación

Using a bicycle helmet markedly reduces the risk of head injuries. The most recent systematic review and meta-analysis has documented that the use of bicycle helmet reduces the risk of serious head injuries by 60 % [1]. Given the large safety-gains from wearing a helmet while cycling, it is highly relevant to promote the uptake of bicycle helmets while also measuring how the prevalence of bicycle helmet use evolves over time across different groups. In Denmark, bicycle helmet use has been promoted in several nationwide campaigns targeting both the general cyclist population and cycling school children. Since 2004, the use of bicycle helmets among cyclist in Denmark has also been measured observationally in two different nationwide time series. One time series (termed 'city traffic'') consists of observations of cyclist across all age groups in city traffic. The other nationwide time series (named 'school traffic'') consists of observations in front of schools, and only includes observations of cycling children. This paper presents the results of these two observational time series, while also cautiously linking the increase in bicycle helmet use to campaigns that have promoted the uptake of bicycle helmets. This paper is structured as follows. First, the methods of the observational bicycle helmet studies are presented. Second, the results of the bicycle helmet observational time series are presented. Third, the discussion section assesses potential explanations for the increased bicycle helmet use in Denmark. The paper concludes that the use of bicycle helmets has increased markedly in Denmark. In 2004, approximately 6 % of the observed cyclists in city traffic used bicycle helmets. This number increased to 28 % in 2014 and to 48 % in 2021. In school traffic, helmet use among cycling school children increased from 33 % in 2004 to 60 % in 2014 and to 80 % in 2021. [From Introduction]

The aim of the research project RADimFOKUS was to develop and evaluate a cyclist waming system (CWS) prototype in order to prevent safety critical events (SCEs), such as accidents, for the specifically vulnerable group of cyclists and, in turn, contributing to an enhanced traffic safety for this sustainable and healthy mode of transport. The basic idea of the system was to warn cyclists in case a SCE is detected. Although research about CWS is rather scarce, :first evaluations of such systems are promising [1]. Considering actual developments and trends, the CWS detects SCEs based on connected traffic information and is in a fust step intended for the implementation in electrified bicycles (i.e. pedelecs), where power supply is provided by the integrated battery. In the scope of the project, we performed the following 3 stages, which are described in the current contribution: (1) Development of the waming model and user interface for the CWS prototype, (2) Development of a bicycle simulator and implementation of the CWS interface for user studies, and (3) First evaluation of the CWS prototype in the scope of a bicycle simulator user study.

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE.

Experience and key data suggest that snow and ice lead to increased numbers of cyclist falls during the winter months. Reliable in-depth data concering the extent and characteristics of this issue are currently not available in most countries. In Germany, this is due to the high level of under-reporting in official statistics, particularly for incidents involving only one bicyclist. In combination with the lack of knowledge on exposure this causes difficulties to quantify risks for cyclist falls. This study addresses these gaps. lt aims at quantifying the risk of single bicycle accidents in inclement weather conditions. This study focusses on icy and snowy conditions as these are of relevance for the risk to fall. Cyclists are particularly affected by slippery icy and snowy road conditions; these might exist in clear, cloudy, or foggy weather, in situations with high or low humidity and with higher or lower wind speed. Variables from official weather data are purposefully combined in this study to identify time periods with snow or ice on the roads and to allow for the comparison of those with all other time periods ('other weather''). We address the above-mentioned problems of exposure and underreporting by using multiple data sources for quantifying the risk of falls. This approach allows to compute clear risk ratios for icy/snowy and the other weather conditions and thus contributes to the scarce and fragmented literature that has generated such values so far. [from Background, AIM]

Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.

While cycling is promoted as a sustainable and healthy mode of transport in many eitles in the Global North [1, 2], there are increasing concerns about the safety of cyclists. The increasing bicycle use in urban areas leads to a more intensely used cycling network, resulting in safety risks for cyclists [3]. Since 2010, the number of bicycle fatalities stagnated and the number of severely injured cyclists increased by 28% until 2018 in the European Union [4]. lt is therefore necessary to examine how bicycle use and motorized vehicle use in cities affects the nunber of bicycle crashes. To investigate this, the effect of the network-wide hourly exposure to cyclists and motorized vehicles on bicycle crash frequency is examined. That is, the total number of cyclists and motorized vehicles in the whole road network for each hour of the week were estimated and used as the network-wide hourly exposure. This approach allowed us to capture safety impacts of temporal variation in the numbers of cyclists and motorized vehicles in the same network more accurately. lt is a different approach compared to most bicycle safety studies, which often only use the daily average of bicycle and motorized vehicle volumes. The work presented here is based on our publication in Safety Science [5].

No description available.

En réponse au nombre toujours élevé de décès provoqués par les accidents routiers, l'industrie automobile a fait de la sécurité un sujet majeur de son activité global. Les radars automobiles qui étaient de simples capteurs pour véhicule de confort, sont devenus des éléments essentiels de la norme de sécurité routière. Le domaine de l’automobile est un domaine très exigent en terme de sécurité et les radars automobiles doivent avoir des performances de détection très élevées et doivent répondre à des nombreuses contraintes telles que la facilité de production et/ou le faible coût. Cette thèse concerne le développement d’algorithmes pour la détection et la localisation de piétons et de cyclistes pour des radars automobiles de nouvelle génération. Nous avons proposé une architecture de réseau d'antennes non uniforme optimale et des méthodes d'estimation spectrale à haute résolution permettant d’estimer avec précision la position angulaire des objets à partir de la direction d'arrivée (DoA) de leur réponse. Ces techniques sont adaptées à l'architecture du réseau d'antennes proposé et les performances sont évaluées à l'aide de données radar automobiles simulées et réelles acquises dans le cadre de scénarios spécifiques. Nous avons également proposé un détecteur de cible de collision, basé sur la décomposition en sous-espaces Doppler, dont l'objectif principal est d'identifier des cibles latérales dont les caractéristiques de trajectoire représentent potentiellement un danger de collision. Une méthode de calcul d'attribut de cible est également développée et un algorithme de classification est proposé pour discriminer les piétons, cyclistes et véhicules. Les différents algorithmes sont évalués et validés à l'aide de données radar automobiles réelles sur plusieurs scenarios. / In response to the persistently high number of deaths provoked by road crashes, the automotive industry has promoted safety as a major topic in their global activity. Automotive radars have been transformed from being simple sensors for comfort vehicle, to becoming essential elements of safety standard. The design of new generations automotive radars has to face various constraints and generally proposes a compromise between reliability, robustness, manufacturability, high-performance and low cost. The main objective of this PhD thesis is to design algorithms for the detection and localization of pedestrians and cyclists using new generation automotive radars. We propose an optimal non-uniform antenna array architecture and some high resolution spectral estimation methods to accurately estimate the position of objects from the direction of arrival (DOA) of their responses to the radar. These techniques are adapted to the proposed antenna array architecture and the performance is evaluated using both simulated and real automotive radar data, acquired in the frame of specific scenarios. We propose a collision target detector, based on the orthogonality of angle-Doppler subspaces, whose main goal is to identify lateral targets, whose trajectory features represent potentially a danger of collision. A target attribute calculation method is also developed and classification algorithm is proposed to classify pedestrian, cyclists and vehicles. This classification algorithm is evaluated and validated using real automotive radar data with several scenarios.