Smart Tourism Evaluation, Prediction and Sustainable Development (STEPS)
Description
Following the increase in the number of visitors in recent years, many tourism destinations experience a higher utilization of local resources, such as landscapes and infrastructure. While this development brings economic opportunities, it entails the risk of overstressing both natural and socio-cultural resources, particularly in sensitive alpine environments. The implementation of sustainable management is frequently constrained by a lack of objective information to support decision making and by institutional regimes that inadequately balance conservation and use interests.
The interdisciplinary project STEPS integrates data-driven monitoring in the field of computer science with institutional analysis of resource use in human geography to support sustainable tourism development in alpine destinations, focusing on Lauterbrunnen and Grindelwald of the Jungfrau Region as well as the Aletsch Arena Region as case studies. We analyze the interplay of visitor flows, resource systems, stakeholder constellations, and governing institutional regimes. This encompasses qualitative approaches, including expert interviews, document analysis, and conflict observation, with the objective of identifying sustainability challenges.
The qualitative approach is supported through the advancements in digitalization and data availability by developing innovative methodologies for the fusion and analysis of heterogeneous data sources, including mobile network data, booking records, and traffic sensors. The application of distributed machine learning techniques will improve the spatial and temporal resolution of visitor flow monitoring and will be used to predict potential conflicts in resource use, translating them into applicable indicators. Furthermore, our research focuses on developing tools to assist decision-makers in operating, adapting, and planning multimodal transport systems. Specifically, the project aims to create digital twins, virtual models that simulate real-world mobility and its interactions with its surroundings. The results will be visually synthesized to facilitate evidence-based decision-making and proactive responses to sustainable development challenges.
The project provides systemic solutions by combining quantitative data, qualitative analysis and participatory design in an applied research approach. The project advances scientific understanding and provides actionable tools for stakeholders in Alpine tourism destinations, supporting digitally-enabled sustainable transformation in line with the needs of local communities, businesses and the environment. The project therefore also has a transdisciplinary character.
The interdisciplinary project STEPS integrates data-driven monitoring in the field of computer science with institutional analysis of resource use in human geography to support sustainable tourism development in alpine destinations, focusing on Lauterbrunnen and Grindelwald of the Jungfrau Region as well as the Aletsch Arena Region as case studies. We analyze the interplay of visitor flows, resource systems, stakeholder constellations, and governing institutional regimes. This encompasses qualitative approaches, including expert interviews, document analysis, and conflict observation, with the objective of identifying sustainability challenges.
The qualitative approach is supported through the advancements in digitalization and data availability by developing innovative methodologies for the fusion and analysis of heterogeneous data sources, including mobile network data, booking records, and traffic sensors. The application of distributed machine learning techniques will improve the spatial and temporal resolution of visitor flow monitoring and will be used to predict potential conflicts in resource use, translating them into applicable indicators. Furthermore, our research focuses on developing tools to assist decision-makers in operating, adapting, and planning multimodal transport systems. Specifically, the project aims to create digital twins, virtual models that simulate real-world mobility and its interactions with its surroundings. The results will be visually synthesized to facilitate evidence-based decision-making and proactive responses to sustainable development challenges.
The project provides systemic solutions by combining quantitative data, qualitative analysis and participatory design in an applied research approach. The project advances scientific understanding and provides actionable tools for stakeholders in Alpine tourism destinations, supporting digitally-enabled sustainable transformation in line with the needs of local communities, businesses and the environment. The project therefore also has a transdisciplinary character.
Primary Conductor
Start Date
2025-01-01
Expected Completion Date
2028-12-31
Languages
en
de