An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities.

Area of Science:

• Urban systems analysis
• Resilience engineering
• Smart city technologies

Background:

• Urban systems face increasing complexity and threats, necessitating smart technologies for informed decision-making.
• Operationalizing resilience requires mitigating uncertainty through comprehensive data analysis.

Purpose of the Study:

• Introduce a new conceptualization of resilience.
• Present a full-stack solution for managing urban transport system (UTS) resilience using IoE and big multimedia data.
• Support evidence-driven decision-making in smart cities.

Main Methods:

• Novel data-driven approach to resilience engineering.
• Functional Resonance Analysis Method (FRAM) for understanding and modeling UTS.
• Architecture for big data collection, semantic aggregation, enrichment, and sense-making from diverse sources (social media, IoT, user behavior).

Main Results:

• A comprehensive decision model combining analytics, experience, and structural city information.
• Demonstrated application in Florence metropolitan city within the RESOLUTE H2020 project.
• Enhanced ability to manage urban transport system resilience.

Conclusions:

• The proposed solution effectively leverages IoE and big data for smart city resilience.
• The data-driven approach and FRAM provide a robust framework for UTS resilience management.
• Informed decision-making is crucial for enhancing the resilience of critical urban infrastructure.