Smart City Scenario Editor for General What-If Analysis.

Area of Science:

• Urban planning and smart city technologies
• Data science and big data analytics
• Environmental science and sustainable mobility

Background:

• Urbanization presents complex challenges in mobility, energy, and environment.
• Existing solutions for urban scenario analysis are domain-specific, inflexible, and use proprietary formats.
• Big data from various sources (IoT, GIS, legacy systems) can offer insights into city status and potential changes.

Purpose of the Study:

• To present a novel, flexible, multi-domain scenario model and editor for urban environments.
• To integrate this solution into the open-source Snap4City platform.
• To enable advanced data processing and what-if analyses for smart city applications.

Main Methods:

• Development of a novel scenario model and editor.
• Integration into the Snap4City open-source platform.
• Implementation of NGSIv2-compliant data models and a scenario versioning method.
• Demonstration through multi-domain analyses and a traffic congestion case study.

Main Results:

• The proposed solution supports multi-domain processing and what-if analyses.
• It utilizes NGSIv2-compliant data models with formal urban context descriptions.
• A case study on traffic congestion validates the model's effectiveness and utility.

Conclusions:

• The novel scenario model and editor enhance the Snap4City platform's capabilities for smart city management.
• The solution offers flexibility and addresses limitations of existing single-domain tools.
• It provides a robust framework for analyzing urban changes and supporting sustainable mobility initiatives.