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This study introduces a new scenario model and editor on the Snap4City platform for multi-domain urban analysis. It enables flexible what-if scenarios to address city challenges like traffic congestion and pollution.

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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.