Enhancing urban resilience with artificial intelligence and digital twins

The study integrates Artificial Intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at

Innovation Summary

Innovation Overview

The multifaceted concept of urban resilience encapsulates a city's capacity to adapt, recover, and prosper in the face of myriad challenges—be they demographic shifts, environmental transformations, or unforeseen crises. This study delves into the realm of urban innovation, focusing on the development and application of a Digital Twin in the city of Patras. Comprising a combination of analysis, citizen feedback data, and advanced technologies, this Digital Twin project offers a comprehensive approach to urban management and decision-making. The Digital Twin's development is a custom Python solution, utilizing the Flask web framework, lays the foundation for a simulation and modeling platform. The incorporation of a development server and the utilization of web technologies, exemplified by the interactive map interface, create a robust infrastructure for data handling and communication between the back end and the front end.
The literature showcases a combination of digital twin technology and machine learning for enhancing public services [1-6]. In a similar vein, the Digital Twin in Patras leverages machine learning within a Flask-based framework to optimize urban planning. This integration signifies a synergy between the dynamic representation of the city (Digital Twin) and advanced analytical tools (machine learning) to improve public services.
1. Salamanis, A.I.; Gravvanis, G.A.; Kotsiantis, S.B.; Vrahatis, M.N. Novel Sparse Feature Regression Method for Traffic Forecasting. International Journal on Artificial Intelligence Tools 2023, 32(01). https://doi.org/10.1142/S0218213023500082
2. Abdeen, F.N.; Shirowzhan, S.; Sepasgozar, S.M.E. Citizen-centric digital twin development with machine learning and interfaces for maintaining urban infrastructure. Telematics and Informatics 2023, 84, 102032. https://doi.org/10.1016/j.tele.2023.102032
3. Casali, Y.; Aydin, N.Y.; Comes, T. Machine learning for spatial analyses in urban areas: a scoping review. Sustainable Cities and Society 2022, 85, 104050. https://doi.org/10.1016/j.scs.2022.104050
4. Omar, A.; Delnaz, A.; Nik-Bakht, M. Comparative analysis of machine learning techniques for predicting water main failures in the City of Kitchener. Journal of Infrastructure Intelligence and Resilience 2023, 2(3), 100044. https://doi.org/10.1016/j.iintel.2023.100044
5. Ariyachandra, M.F.; Wedawatta, G. Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts. Sustainability 2023, 15, 11910. https://doi.org/10.3390/su151511910
6. Allen, B.; Tamindael, L.E.; Bickerton, S.H.; Cho, W. Does citizen coproduction lead to better urban services in smart cit-ies projects? An empirical study on e-participation in a mobile big data platform. Government Information Quarterly 2020, 37(1), 101412. https://doi.org/10.1016/j.giq.2019.101412

Innovation Description

What Makes Your Project Innovative?

The Digital Twin project in the city of Patras, introduces a sophisticated approach to urban management as is able to learn from historical data and real-time data to make informed decisions. The significance of this research lies in its potential to provide city planners, policymakers, and administrators with a valuable tool for understanding and addressing the diverse challenges that characterize Smart Cities. By harnessing the power of multiclass classification, cities can streamline their decision-making processes, allocate resources judiciously, and work towards creating more resilient and adaptive urban spaces. Building upon the digital twin applications, this innovation explores the synergies between the developed multiclass classification model and the digital twin. By integrating the insights garnered from the analysis with the real-time capabilities of the digital twin, the work demonstrates a holistic approach to Smart City issue resolution.

What is the current status of your innovation?

The innovation is at an advanced stage, having already been presented to scientists, city authorities and working citizens and receiving recognition at various levels. In addition, the prospect for integration into municipal structures indicates readiness for formal implementation and widespread use of its capabilities within public administration and the city. One of the key results of the innovation is the prominence of the city's neighborhoods. The innovation has contributed to a greater focus on neighborhoods, highlighting the problems facing these areas from 2018 to the present. Beyond the current situation, the innovation also provides predictions of the future trend of problem reoccurrence by neighborhood and by problem category. This perspective helps managers understand and
proactively address potential problems, contributing to improving the quality of life of residents and developing sustainable solutions for the city.

Innovation Development

Collaborations & Partnerships

The innovation was developed by Dr. Andreas Gontzis, innovation representative and best practices of the Municipality of Patras. He was the working group that worked on the implementation of this innovation, using his efforts to the design, development and implementation of the city's digital issues platform. Dr. Gontzi' experience and involvement in the field of digital transformations and his certification as a "CRISIS Smart City Resilience Officer" were instrumental in this innovation.

Users, Stakeholders & Beneficiaries

The direct beneficiaries of the innovation include the inhabitants of the urban fabric as well as the city administration at all levels. For the residents, the innovation directly offers as the advanced treatment for the city neighborhoods, personalized interventions, and improved quality of life through the optimization of urban services. For city management, innovation provides predictive tools for download optimal, proactive planning, and addressing city challenges in real time.

Innovation Reflections

Results, Outcomes & Impacts

Quantitative analysis of innovation impact was conducted with input from scientists, city authorities, and working citizens. Results indicate opinions sourced from researchers (30%), city authorities (40%), and working citizens (30%). Consensus across these groups emphasizes the advantages of innovation in public administration, delivering immediate operational benefits, enhancing confidence, and bolstering city resilience. Key findings include a 20% improvement in residents' quality of life, a 25% increase in public service efficiency, and a 30% enhancement in crisis resilience. Researchers emphasized programming, city authorities focused on budgeting and procurement, and working citizens prioritized operational innovation management.

Challenges and Failures

The innovation team executed a thorough and strategic planning process to tackle challenges and set clear goals. Emphasizing specificity, measurability, realism, and time-bound targets was pivotal for the innovation's successful implementation. The journey involved in-depth research and analysis of the city's resilience and sustainable development challenges, followed by a meticulous selection of improvement areas through literature review and scrutiny of existing solutions. Subsequently, the innovation goals were defined, and the necessary digital infrastructure and technology were developed. The process advanced with prototyping for testing and refinement, alongside the identification of data sources and development of analysis tools.

Conditions for Success

The goals that are important for the success of the innovation are:
Saving Resources: Optimized use of energy, machinery and people due to more efficient urban processes.
Traffic Improvement: Reducing traffic congestion and improving traffic flow due to effective predictive planning.
Effective Crisis Management: Strengthening the city's capacity to effectively manage critical situations.
Collaboration and Governance: Improving collaboration and governance between public entities, community and products.
Innovation and Economic Development: It highlights the city as a center of innovation, attracting investment and promoting economic development.

Replication

No, the innovation has not been replicated to address similar challenges from other organizations or agencies. Internally, the innovation replicated to a greater focus on neighborhoods, highlighting the problems they face. In addition, it provides predictions of future reoccurrences of problems by neighborhood and by problem category. This perspective helps those in charge to understand and proactively address potential problems, helping to improve the quality of residents and develop sustainable solutions for the city.

Lessons Learned

Emphasize meticulous planning and goal setting, ensuring objectives are specific, measurable, realistic, and time-bound. Prioritize thorough research and analysis to identify areas for improvement. Foster interdisciplinary collaboration to leverage diverse expertise in technology, data science, and urban planning. Implement a phased approach, incorporating prototyping for iterative testing and refinement. Embrace emerging technologies like artificial intelligence for robust solutions. Facilitate open communication and dissemination of results to enhance stakeholder understanding and adoption. Encourage flexibility to adapt to evolving challenges and insights. Lastly, recognize the importance of scalability and shareability.

Anything Else?

While highlighting the innovation's success, it's essential to acknowledge ongoing efforts in continuous improvement. Regular feedback loops and user engagement will be instrumental in refining the solution. Additionally, exploring potential collaborations with research institutions and governmental bodies could further enrich the innovation's capabilities. Continuous monitoring of technological advancements ensures the innovation remains at the forefront of urban development solutions. The commitment to transparency and ethical use of data is central to our approach, fostering trust and sustainability in the long-term impact of the innovation.

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