Nina Wiedemann
Biographic Data
| ID | 6581802 |
|---|---|
| NAME | Nina Wiedemann |
| GIVEN NAMES | Nina |
| FAMILY NAME | Wiedemann |
| SIGNATURE | WIEDEMANN N |
| AFFILIATIONS | ETH Zurich |
| ORCID | 0000-0002-8160-7634 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Geography for AI sustainability and sustainability for GeoAI
Recent years have witnessed a boom in the development of multimodal large-scale generative AI models. These computationally intensive AI models, such as GPT-4, and their associated data centers have undergone increasing scrutiny in terms of their energy consumption and carbon emissions. As awareness of the energy costs and carbon footprints of AI models grows, attention has broadened to include other sustainability-related aspects such as their w…
An ontology-based approach for harmonizing metrics in bike network evaluations
The urgency to decarbonize the transportation sector has accelerated the adoption of micro-mobility solutions, with cycling network development witnessing remarkable growth. Robust and quantitative evaluation frameworks are needed to evaluate the quality of such developments. While a plethora of bike network evaluation approaches exist, their diversity creates issues of interpretability and comparability due to varying metrics and domain-specific…
Trackintel: An open-source Python library for human mobility analysis
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-sour…
Graph-based mobility profiling
The decarbonization of the transport system requires a better understanding of human mobility behavior to optimally plan and evaluate sustainable transport options (such as Mobility as a Service). Current analysis frameworks often rely on specific datasets or data-specific assumptions and hence are difficult to generalize to other datasets or studies. In this work, we present a workflow to identify groups of users with similar mobility behavior t…
Spatially-aware station based car-sharing demand prediction
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly deman…
Spatially-aware station based car-sharing demand prediction
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly deman…
Trackintel: An open-source Python library for human mobility analysis
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-sour…
Graph-based mobility profiling
The decarbonization of the transport system requires a better understanding of human mobility behavior to optimally plan and evaluate sustainable transport options (such as Mobility as a Service). Current analysis frameworks often rely on specific datasets or data-specific assumptions and hence are difficult to generalize to other datasets or studies. In this work, we present a workflow to identify groups of users with similar mobility behavior t…
Spatially-aware station based car-sharing demand prediction
In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly deman…
An ontology-based approach for harmonizing metrics in bike network evaluations
The urgency to decarbonize the transportation sector has accelerated the adoption of micro-mobility solutions, with cycling network development witnessing remarkable growth. Robust and quantitative evaluation frameworks are needed to evaluate the quality of such developments. While a plethora of bike network evaluation approaches exist, their diversity creates issues of interpretability and comparability due to varying metrics and domain-specific…
Geography for AI sustainability and sustainability for GeoAI
Recent years have witnessed a boom in the development of multimodal large-scale generative AI models. These computationally intensive AI models, such as GPT-4, and their associated data centers have undergone increasing scrutiny in terms of their energy consumption and carbon emissions. As awareness of the energy costs and carbon footprints of AI models grows, attention has broadened to include other sustainability-related aspects such as their w…
Computer Science (4 works) · Geography (3 works) · Human Mobility and Location-Based Analysis (3 works) · Cartography (2 works) · Data mining (2 works) · Data science (2 works) · Engineering (2 works) · Transport engineering (2 works) · Transportation and Mobility Innovations (2 works) · Urban Transport and Accessibility (2 works)