Open Access

In diesem Beitrag wird die Integration verschiedener Sensoren zur Bestimmung der Position sich bewegender Fahrzeuge vorgestellt. Die ständige Zunahme der Bevölkerung in urbanen Räumen und das damit einhergehende Wachstum des Verkehrsaufkommens führt in Zeiten, in denen die Auswirkungen des Klimawandels immer stärker spürbar werden, dazu, dass die Entwicklung neuer Mobilitätskonzepte als dringend notwendig erachtet wird und den Weg auch auf die Agenda der Politik gefunden hat. Sharing-Systeme mit emissionsfreien Antrieben wie z. B. E-Bikes können hier einen Beitrag leisten, Luftverschmutzung und Lärm in Städten zu reduzieren und damit die Lebensqualität in urbanen Räumen zu steigern. Bei Sharing-Systemen ohne bestimmte Standorte der E-Bikes, an denen sie abgeholt und zu denen sie wieder zurückgebracht werden, ist eine verlässliche Positionierung der E-Bikes von entscheidender Bedeutung. Effiziente, durchgängige und kostengünstige Methoden die E-Bikes zu lokalisieren werden benötigt, sowohl um Kunden korrekte Positionen verfügbarer E-Bikes anzuzeigen und bei einer Fahrt verlässlich zu navigieren, als auch für die Servicemitarbeiter des Sharing-Anbieters. In den meisten Fällen genügt die Genauigkeit einer einfachen Positionierung mittels GNSS. Gerade in urbanen Umgebungen kommt es jedoch öfter zu Ungenauigkeiten aufgrund von Mehrwegausbreitung, Signalbeugung und Abschattung der Satellitensignale. Dies kann bis zum zeitlich begrenzten Ausfall der Positionierung führen. Verschiedene Low-Cost-Sensoren kommen hier in Frage, um eine Trajektorie der gefahrenen Strecke aufzuzeichnen, die ein E-Bike, ab dem Zeitpunkt, ab dem die GNSS-Position nicht mehr verlässlich genug ist, zurückgelegt hat. Um einen zeitlich begrenz- ten Ausfall von GNSS zu kompensieren wird die Methode der Koppelnavigation (engl.: Dead Reckoning) auf der Basis von zusätzlichen Sensoren wie Gyroskope und Beschleunigungssensoren angewendet. In früheren Untersuchungen mit einem Car-Sharing-System wurden hier schon Erfolge erzielt, allerdings lassen sich dort durch unterschiedliche Geschwindigkeiten der Räder der Hinterachse Kurvenradien berechnen sowie durch die 42 Sensorimpulse bei einer kompletten Radumdrehung auch wesentlich genauere gefahrene Strecken ermitteln als bei einem E-Bike mit nur einem Sensorimpuls pro Radumdrehung.

Determining emotions of people during different activities (e.g. surfing the web or walking and driving in urban areas) is of high interest to many industries such as the advertising and marketing industry (Imotions, 2022) or city developers and planners (Zeile et al., 2015). However, as the authors have explained in their previous works (Schneider et al., 2020, Dastageeri et al., 2019, Kohn et al., 2018), it is very difficult to determine emotions using surrogate measurements. This is compounded by the fact that many people have trouble to name their emotions correctly as they are often mixed and hardly ever occur as a single and distinct emotion. In order to improve the attractiveness of cities not just based on general presumptions about how citizens would react to certain changes in the urban environment, but based on physical measurements, previous works have shown approaches to do that (Schneider et al., 2020, Dastageeri et al., 2019). They have developed a first attempt to correlate measured physical parameters such as heart rate and skin conductivity (among others) which are triggered by location and environment to emotional states using machine learning. To correlate locations to emotions is an important aspect for city planners, as a person’s emotion for a location defines the personal relationship to a place which can help to gauge the attractiveness of a place and give indicators about where to improve the city or place.

The presented work provides an overview of sustainability dimensions relevant to urban planning at an urban district level along multiple ressource axis. An analysis of innovative urban development projects and a literature review on city district sustainability were coupled with city grading schemes. Interviews with municipal stakeholders allowed to determine the extent to which these dimensions are already integrated into planning practices, and if not, what the barriers to the implementation of sustainable solutions are, and what the requirements for the development of new tools and methods to enable a holistically approach to sustainable planning are. Based to this, current planning practices prioritize a reduction of building energy demand and greenhouse gas (GHG) emissions during the use phase, with limited or no consideration of dimensions such as GHG from building construction, rainwater retention or urban green for optimizing urban microclimates. However, awareness for such dimensions is rising: this can be seen in pilot projects where the scope of assessment was expanded to include for instance aspects of mobility and rainwater management. It furthermore shows that an approach to optimise planning procedures along multiple dimensions is still absent from many urban planning processes, even though mindsets seem to be shifting gradually.

In the context of climate change, the increasing demand for energy-efficient buildings and sustainable urban development has become a pressing issue due to the significant proportion of global energy consumption and carbon dioxide (CO2) emissions attributable to the building sector. This requires a concerted effort to reduce its environmental impact, and Geographic Information System (GIS) applications are vital tools for achieving this by optimizing heat supply, calculating costs, analyzing profitability, and balancing CO2 emissions. This study aims to address the challenge of achieving energy efficiency and reducing CO2 emissions in the building sector, specifically at the district level. To this end, the research objective is to develop a QGIS plugin that can simulate urban energy demand at the district level by integrating 2D data with CityGML files and connecting QGIS to SimStadt software via API to visualize the simulated urban energy results in 3D on the Web Globe. The proposed plugin leverages the open-source QGIS tool QField to capture building conditions and connect 2D and 3D data on urban energy simulation. Supplementary to this, this plugin provides up-to-date information on energy demand, consumption, CO2 emissions, building component conditions via updating related tables in the database. Decision-makers can use this comprehensive and user-friendly tool to understand and act on the results, ultimately leading to a CO2-neutral district by 2045. The development of the QGIS plugin represents a significant step towards sustainable urban development and climate change mitigation by utilizing GIS applications for optimizing energy demand and reducing CO2 emissions in the built environment.

Safety on road networks is the utmost important factor to consider for public well-being and transportation efficiency. This study introduces a new approach that combines Getis-Ord spatial statistics and crash rate analysis to identify significant road traffic accidents (RTAs) characterized by hotspots on road segments in Addis Ababa’s road network. The study’s results visually portray the crash locations, associating them with the underlying road net-work, which demonstrates a notable concentration of accident hotspots, between the years from 2014 to 2019 on Addis Ababa’s roads network. The RTAs spatial analysis resulted in the identification of hotspots on 33 road segments, 3 intersections, and 10 roundabouts/squares. Among the identified hotspots, the road segment recognized as Djibouti Street, extending from Bole Edna Mall to the 22 “mazoriya” roundabouts, stands out as the most signifi-cant accident hotspot. It exhibits an average of 37.5 crashes per kilometer per year, encompassing a road segment length of 1141 m. Using both methods in this study is crucial for validating findings by identifying high-crash seg-ments and enhancing their reliability and hotspot accuracy. This unique vali-dation method links each traffic accident’s spatial data with the road network using both crash rate and spatial statistical analysis, effectively pinpointing accident hotspots. Given the limited resources, this approach enhances awareness of accident-prone locations, enabling the prioritization of safety measures to improve road safety. It effectively addresses spatial analysis gaps related to RTAs in Ethiopia and holds practical significance by identifying and prioritizing safety measures aimed at reducing accidents within Addis Ababa’s road network.