Summary
Data4Mobility – PSML is developing a data and analytics platform for mobility in the historic area of Sintra, focusing on monitoring flows, access, and tourist pressure. The solution integrates dispersed sources (traffic, parking, transport, ticketing, meters, weather, events, among others) into a single repository and provides dashboards and reports to support operational management and strategic decision-making.
The project is being developed under AI4PA – Digital Innovation Hub, as a Test Before Invest activity.
Challenges
Integrate and operationalize mobility and tourism data (almost) in real time and historically, to support decisions on access, transportation, parking, tourist demand management, sustainability, and safety in the historic area of Sintra.
Objectives
- Monitor mobility in near real time (traffic, parking, public transport, taxis/TVDE, and soft modes).
- Integrate dispersed data sources into a single repository (with history, quality, and consistency).
- Support the definition and evaluation of mobility policies (scenarios, simulations, and before/after analyses).
- Manage the impact of tourism on the historic area (peak demand, carrying capacity, and flow distribution).
- Improve the visitor experience and information to the public (visitor forecasts, recommendations, and real-time information).
- Provide dashboards and reports for decision-makers and stakeholders, with access segmentation and data governance (privacy, GDPR, and sharing rules).
NOVA Cidade in the project
NOVA Cidade (NOVA IMS) ensures the design and implementation of data and governance infrastructure, the integration of top-down and bottom-up sources, the development of indicators, analytical models (descriptive and, where applicable, predictive/prescriptive), and th and the provision of the portal with dashboards and interactive visualizations, based on a lakehouse-type Microsoft Fabric architecture, with differentiated access profiles.
Results
N/A (project ongoing)
Partners
Parques de Sintra Monte da Lua (PSML) · NOVA IMS