São Paulo, Brazil
"URANO Predictive Rapid Response System for Urban Maintenance"
BASIC CITY DATA
- Population size: 12,300,000
- Population Growth Rate(%):0.85
- Surface Area (sq.km): 17,000
- Population Density (people/sq.km): 7,216.3
- Main Source of Prosperity: Poder Público – Governo
The initiative of the URANO Predictive Rapid Response System for Urban Maintenance is an innovative solution implemented in the city of São Paulo. Utilizing artificial intelligence, the system integrates and processes information to provide accurate predictions about the effects of rainfall in the territory. These predictions are directed to the sub-municipalities through the Operational Control Center of the Urban Maintenance Department of the Sub-municipal Secretariat (SMSUB/DZU), enabling more efficient targeting of prevention actions and emergency response at the local level.
The URANO Predictive System represents a significant technological advancement in urban maintenance management, contributing to the improvement of safety and quality of life for citizens by enabling the implementation of preventive measures and proper management of the impacts caused by extreme weather events in the city of São Paulo.
The URANO Predictive System is taking place within the municipal legislative and political framework of the Summer Rain Preventive Plan (PPCV) in the city of São Paulo.
Mayor's Order PREF 1123, dated August 23, 2021, is a public policy document issued by the municipal government with the objective of preventing and mitigating the impacts of summer rainfall in the city of São Paulo.
Mitigate the issue of occurrences caused by weather events in the city through a predictive model. The objective is to enhance the process of prioritizing urban maintenance services by issuing automatic service orders for cleaning, repairs, and preventive maintenance of macro and micro-drainage elements. The system functions as a real-time alert system, directing maintenance teams and assisting in decision-making to effectively tackle challenges related to weather events and urban infrastructure.
To enhance the planning and execution of preventive and responsive maintenance actions, utilizing predictive analyses through machine learning. The objective is to provide a higher quality of life to residents and increase economic, operational, and logistical efficiency in urban maintenance activities, adding value to the city as a Smart City. The implementation and utilization of the system are already a reality, and changes and results are already taking place.
The URANO Predictive System involves innovation in the planning, management, and execution of urban maintenance actions. The information provided by the system enables the Secretariat and Sub-municipalities to plan and make more informed decisions to deal with the consequences of occurrences caused by extreme weather events in the city. To ensure innovation, they implemented changes that involved interdepartmental cooperation and the establishment of clear rules and procedures for project development.
The URANO Predictive System for Urban Maintenance involves a public-public partnership between the Sub-municipal Secretariat and the Foundation for Technological Development in Engineering of the Federal University of São Paulo. This partnership also involves IT service providers. The Foundation plays a crucial role in development analysis while the Secretariat contributes practical knowledge of urban maintenance, service data, and technology procurement. This innovative partnership benefits both the public administration and residents, providing better management of urban maintenance and efficient emergency responses.
The implementation of the initiative involved the hiring of information technology services for system development and maintenance. Additionally, the technical team from the Department of Urban Maintenance (DZU) of the Sub-municipal Secretariat (SMSUB) played a crucial role in the operation and monitoring of the URANO Predictive System, ensuring its calibration throughout the events. The combination of these technological and human resources was essential for the success of the initiative and the results achieved thus far.
The initiative of the URANO Predictive System for Urban Maintenance is evolutionary as it is based on lessons learned over time. The implementation of the system was driven by the need to improve urban maintenance actions. The use of artificial intelligence has allowed for continuous adjustments and enhancements in the predictive model, resulting in progressive and cumulative changes in how maintenance operations are planned and executed. An innovative approach, which builds on existing technologies and is adapted to the context and needs of the city of São Paulo, thus reducing the risk associated with completely disruptive approaches.
DESIRED CHANGE OR OUTCOME
The URANO Predictive System for Urban Maintenance achieved results in guiding maintenance actions and teams, and the prediction of occurrences caused by weather events. The products resulting from the integration and processing of information improved the planning, management, and execution of urban maintenance actions through preventive measures This improvement in urban maintenance management has local impacts and contributes to enhancing the city's resilience in the face of climate challenges.
The evaluations are carried out through the collection and analysis of data related to the cleaning and maintenance services of the drainage system, correlated to the events of rain and occurrence records. Measurements are carried out by the URANO Predictive System for Urban Maintenance support team and the results are used to guide prevention planning and actions in different areas of the city.
The URANO Predictive Rapid Response System for Urban Maintenance acts as a technological tool to support both preventive and responsive custodial actions throughout the city of São Paulo, particularly in the areas most affected by extreme rainfall and the occurrences caused by such events.
The support provided by the URANO Predictive System directly benefits all residents and non-resident workers in the city of São Paulo. By utilizing the system's information, maintenance response teams can anticipate occurrences caused by heavy rainfall and restore the city to normalcy within a short period of time, around 2 hours.
RELEVANCE TO SUSTAINABLE DEVELOPMENT GOALS
Goal 3: Ensure healthy lives and promote well-being for all ages
Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable
Goal 13: Take urgent action to combat climate change and its impacts