Reduce Data Silos via Realtime AI to Deliver Intelligent Data and Actionable Insights in Chicago
A custom AI Platform in Chicago helped reduce departmental data silos and make progress on public safety, transportation, air quality, emergency response, and other resident prioritized pain-points. This effort was a key first step in making progress towards being more data-driven and achieving smart city goals.
Public Private Partnership
Operational since 2012
Chief Information Officer
The City of Chicago knew that it had access to lots of unique data sources and streams.
But the data was hard to access and use to improve safety, transportation, public health, and other citizen identified pain-points because of existing department silos. In addition, the insights from the data weren't being communicated with residents to improve their daily life in the city.
The holistic approach integrated data from 311, social networks, existing sensors and citizen surveys to improve data sharing across agencies.
Improved access to data through this platform helped the city make progress across a variety of inter-department challenges like adjusting transportation to reduce air quality impacts and determining ways to generate energy savings through smart street-lighting. Building on an existing main operations center model, IPgallery provided an interactive dashboard which used machine learning to analyze both big-data and real-time data to push automatic alerts for both city officials and residents. In addition, the system was designed to generate action plans for events that have happened, are happening or may occur in the near future helping to streamline response to situations like flooding, heatwaves and other emergencies.
Produced CityApp, a personalized mobility app for Chicago residents includes traffic and route recommendations, along with parking, bus and shared mobility options ZipCar, bike and scooters.
Adjusted snow plow schedules to prioritize school bus routes based on citizen sentiment discovered through aggregated survey and data analysis.
Real-time AI and Machine learning algorithms proposed automated cross-departmental business process and workflow improvements to increase overall efficiency
Enabled the city to both increase transparency of city data and monetize their data through expanded service provision
Before and after citizen sentiment analysis showed an increase in satisfaction around safety and lifestyle after deploying data-driven solutions
The platform is modular, so while the data integration is the first step and the backbone of the platform, cities can start by focusing on one use case - for example real time traffic management and air quality impacts - and add additional use cases over time.
Who Should Consider
Large to Medium size cities that are looking to reduce data silos and improve data-driven decision making.
Last UpdatedDec 3rd, 2019