Patricia Patitucci

Paris Smart City

A control room for Place de la Nation


Using Cisco’s Immersive Lab, I designed a smart city control room to visualize real-time data from sensors at Place de la Nation in Paris. Dynamic templates adapt throughout the day to highlight key insights—morning traffic, pedestrian flow, or environmental changes—while also responding to abnormal sensor readings and situational events like weather, pollution, and metro closures, ensuring timely and informed decision-making.

ROLE

UX and Visual Design

DATE

2015

The brief

As part of Cisco’s exploration of IoT and smart city solutions, we deployed multiple sensors at Place de la Nation in Paris to monitor urban metrics such as air quality, noise levels, pedestrian flow, vehicle traffic, and smart bin usage. My role was to define the concept, UX, and UI for the control room, which visualized this data on the Cisco Immersive Lab’s L-shaped touchscreen video wall.

Research

Exploring Existing Smart City Control Rooms

To better understand the landscape of smart city control rooms, I began researching their design, functionality, and user experience. I gathered insights from people who had visited the Songdo Control Room in South Korea and explored online and print references for additional perspectives.

Most control rooms follow a similar structure: the space is built around a large video wall, with analysts seated in front of it, monitoring their own screens. The video wall primarily displays CCTV footage, supplemented by occasional graphs and data visualizations.

One key observation was the overwhelming nature of these setups—specialists must actively scan for critical information, which could potentially slow decision-making.

This raised an important question: How might we empower decision-makers to better understand, process, and act on the data presented to them?

Understanding Place de la Nation and Its Users

To design an effective solution for Place de la Nation, it was essential to first understand the people who use it—residents of nearby blocks, employees working in and around the area, and those simply passing through. I explored key questions: What venues surround it? How does the space feel? Is it safe and welcoming? How does its atmosphere change throughout the year, month, week, or even different times of day?

Much like analyzing website traffic, I conducted field observations at Place de la Nation, assessing its environment firsthand. Complementing these insights with social media data, I developed personas to map out typical user behaviors across different times of the day and week.

Additionally, I documented commercial venues, local events (such as street markets), and demographic data from the area. Using this information, I built average activity charts, helping to visualize key use cases and trends that shape how Place de la Nation is used throughout the week.

Key Concepts

Awareness of the Present

A major challenge in existing control rooms is the lack of prioritization. To address this, we designed a system of dynamic templates that adapt in real time to highlight the most relevant data at any given moment.

For example, at 8 AM, the screens focus on morning rush-hour traffic, monitoring street flow around schools and pollution levels where students are likely walking. By mid-morning, the emphasis shifts to pedestrian movement in the square, continuously adjusting throughout the day based on key use cases.

Templates also dynamically respond to abnormal sensor readings, drawing analysts’ attention through visual cues—such as enlarging critical data points or changing colors—or switching to a more relevant template for the situation.

Situational templates were designed for extreme weather conditions (heavy rain, snow, heatwaves), high pollution alerts, metro closures, and events like public rallies, ensuring analysts always have the right information at the right time.

Understanding the Past

To support deeper analysis, users can rewind and compare past trends under similar conditions. A timelapse video captures key moments from CCTV footage, while an interactive dashboard provides a snapshot of critical data points for any selected day, week, or month.

Predicting the Future

Leveraging machine learning algorithms, the system enables proactive decision-making by forecasting potential issues before they arise.

This starts with basic insights—such as how weather conditions might impact traffic and public transport—and extends to more advanced predictions, like identifying areas at higher risk of accidents.

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