Crowd monitoring in Lisbon

Challenge:

Lisbon, a city renowned for hosting large-scale events such as World Youth Day (WYD) 2023, Rock in Rio, Kalorama, New Year's Eve celebrations at Terreiro do Paço, and major football matches, faces significant challenges in managing the dynamic movement and concentration of people. These events attract massive crowds, with WYD 2023 alone drawing approximately 1.5 million attendees. Understanding crowd densities, flow patterns, and tourist behaviours is crucial for ensuring public safety, optimising transportation, and enhancing resource allocation. Additionally, monitoring the flow of individuals entering and exiting the city through its 11 main access points is essential for effective traffic management and emergency response.

Approach:

To address these challenges, Lisbon implemented a strategy leveraging anonymised mobile device data, specifically through the triangulation of cell tower signals. This method captures real-time information on the presence and movement of groups of individuals while adhering to privacy standards by only collecting data when more than ten devices are within a 200x200 meter area.

The strategy began with data collection, utilising cell tower triangulation to estimate the number of mobile devices within specific areas. This approach provided insights into crowd densities and movement patterns without relying on GPS data or call records, aligning with mobile positioning methods used in urban studies. Next, the city was divided into grids of 200x200 meter squares, enabling spatial segmentation. This allowed for the analysis of movement patterns at varying levels of granularity and facilitated the differentiation between public spaces and building areas, offering a detailed examination of how people navigate different urban environments.

Advanced analytical techniques were then employed to process the collected data, which provided insights into crowd densities, flow patterns, and the identification of popular tourist destinations. These insights supported informed decision-making in urban planning and event management. To further refine the analysis, tourists were distinguished from residents by examining patterns such as devices in roaming and movement trajectories. This differentiation enabled a focused analysis of tourist behaviours, including their most frequented areas and points of origin, leveraging mobile positioning data effectively as seen in tourism studies.

Lastly, the flow of individuals through the city's eleven main entry and exit points was monitored to understand traffic patterns and peak usage times. This information proved crucial for optimising traffic management strategies and planning infrastructure improvements.

Outcomes:

The implementation of this data-driven approach yielded significant benefits in multiple areas.

Firstly, it enhanced decision-making processes. Urban planners gained a deeper understanding of crowd dynamics, which enabled them to make more informed decisions regarding infrastructure development, public transportation planning, and resource allocation. The integration of mobile device data into urban planning processes has demonstrably improved the standard of living in smart cities.

Secondly, it contributed to improved public safety. Real-time monitoring of crowd densities allowed for prompt responses to potential safety hazards, such as overcrowding during events. This capability significantly enhanced public safety measures, as mobile-based crowd monitoring systems proved effective in managing urban spaces.

Additionally, the approach supported tourism management. By providing insights into tourist behaviours, it enabled targeted marketing strategies, better management of tourist attractions, and improved visitor experiences. Mobile positioning data has been instrumental in evaluating tourism marketing strategies and understanding tourist experiences.

Finally, it optimised traffic management. By analysing the flow through key entry and exit points, urban planners could refine traffic management strategies, reducing congestion and improving overall traffic flow. This application of mobile data provided a practical solution to longstanding traffic challenges.

Data Sources:

The data sourced includes anonymised mobile device activity detected via cell tower triangulation, providing insights into crowd densities, movement patterns, and traffic flow. Additionally, roaming status and movement trajectories are analysed to differentiate tourists from residents and understand their behaviours.

Additional Information:

• Lisbon’s Bet on Urban Data Platform - Open & Agile Smart Cities

Point of Contact:

• sg [dot] dmccm-lisboa [dot] pt (sg[dot]dmc[at]cm-lisboa[dot]pt)
• coord [dot] dadosabertoscm-lisboa [dot] pt (coord[dot]dadosabertos[at]cm-lisboa[dot]pt)