Determining quantitative sustainable development metrics can be difficult and requires a high effort in manual data acquisition on an institutional level, like the Word Bank or the United Nations, without adequately reflecting reality. To overcome the lack of a transparent and scalable method, which links local actions to global sustainability metrics, the Sustainability Mirror connects the Sustainable Development Goals (SDGs) with publicly and locally available data to proxy SDG metrics. By applying the approach to Germany, we calculated eight SDG metrics on a regional level. Comparing our results to two German cities, we show that the Sustainability Mirror reflects economic and ecological sustainability measures. Furthermore, we demonstrate the mirror’s scalability and spatial resolution by applying the method to each German county. Presenting a proof-of-concept, we show that proxy data can link local and global sustainability metrics. However, further research should include more social sustainability topics. Finally, we are sure our approach and its implementations can contribute to a continuous assessment of spatial and temporal spreads and changes in SDG metrics.

Improving access to essential public services like healthcare and education is crucial for human development, particularly in rural Sub-Saharan Africa. However, limited reliable transportation and sparse public facilities present significant challenges. Mobile facilities like mobile clinics offer a cost-effective solution to enhance spatial accessibility for the rural population.Public authorities require detailed demand distribution data to allocate resources efficiently and maximize the impact of mobile facilities. This includes determining optimal vehicle service stop locations and estimating operational costs. Our integrated approach utilizes GIS data and an accessibility scaling factor to assess spatial accessibility for rural populations. We tailor demand structures to account for remote and underserved populations. To reduce average travel distances to 5 km, we apply a clustering algorithm and optimize vehicle service stop locations. In a case study in rural Ethiopia, focusing on four key public services, our analysis demonstrates that mobile facilities can address 39-62\% of unmet demand, even in areas with widely dispersed populations. This approach aids decision-makers, including fleet operators, policymakers, and public authorities in Sub-Saharan Africa, during project evaluation and planning for mobile facilities. By enhancing spatial accessibility and optimizing resource allocation, our methodology contributes to the effective delivery of essential public services to underserved populations.

In literature, scientists describe human mobility in a range of granularities by several different models. Using frameworks like MATSIM, VehiLux, or Sumo, they often derive individual human movement indicators in their most detail. However, such agent-based models tend to be difficult and require much information and computational power to correctly predict the commutation behavior of large mobility systems. Mobility information can be costly and researchers often cannot acquire it dynamically over large areas, which leads to a lack of adequate calibration parameters, rendering the easy and spontaneous prediction of mobility in additional areas impossible. This paper targets this problem and represents a concept that combines multiple substantial mobility theorems formulated in recent years to reduce the amount of required information compared to existing simulations. Our concept also targets computational expenses and aims to reduce them to enable a global prediction of mobility. Inspired by methods from other domains, the core idea of the conceptional innovation can be compared to weather models, which predict weather on a large scale, on an adequate level of regional information (airspeed, air pressure, etc.), but without the detailed movement information of each air atom and its particular simulation.

Access to transportation is a key enabler of economic and sustainable development in rural emerging economies. Implementing effective improvements requires a deep understanding of the current mobility landscape. Despite its significance, recent research on transportation and mobility characteristics in rural Sub-Saharan Africa remains limited. This study addresses this gap by analyzing mobility patterns and motivations in central Côte d’Ivoire, based on a series of quantitative and qualitative field surveys and a two- electric-vehicle pilot study, conducted between 2021 and 2023. We comprehensively assess rural transportation by situating our empirical findings within the context of existing transportation models and prior studies while evaluating their implications for the Sustainable Development Goals, and offering an outlook on electric vehicle usage patterns in rural areas. The findings provide important guidance for policymakers and industry stakeholders, helping to develop sustainable interventions and business models that improve local access to goods, services, and economic opportunities.

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this fifth report, we present first analyses of the recorded tracking data. 910 participants completed the tracking until September, 30th. First, an overview over the socio-demographic characteristics of the participants within our tracking sample is given. We observe an adequate representation of female and male participants, a slight over-representation of young participants, and an income distribution similar to the one known from the "Mobilit\"at in Deutschland" survey. Most participants of the tracking study live in Munich, Germany. General transportation statistics are derived from the data for all phases of the natural experiment - prior, during, and after the 9 EUR-Ticket - to assess potential changes in the participants' travel behavior on an aggregated level. A significant impact of the 9 EUR-Ticket on modal shares can be seen. An analysis of the participants' mobility behavior considering trip purposes, age, and income sheds light on how the 9 EUR-Ticket impacts different social groups and activities. We find that age, income, and trip purpose significantly influence the impact of the 9 EUR-Ticket on the observed modal split.