Artificial Intelligence applied to Urban Mobility
The advanced analytics and Artificial Intelligence platform for obtaining knowledge from urban, metropolitan and interurban mobility data in real time.
Case studies
With AI4T, we generate valuable insights into mobility patterns, demands, and flows, enabling informed decisions to improve the experience of citizens and the management of urban resources.
- Intelligent mobility optimization
- Obtaining multimodal source-destination matrices
- Scenario prediction and analysis
- Efficient management of public transport
- Infrastructure and services planning
- Accelerated MaaS Implementation
- AI-based dynamic pricing
- Cost and time optimization with SaaS
Market needs
Data security
Data is secure due to strict compliance with the ENS (National Security Scheme).
Accurate data
Optimize the planning of transport infrastructure and services with better knowledge.
ITS Implementation
Integrate intelligent systems to improve mobility, sustainability and offer new services.
Real-time information
Having up-to-date data to respond effectively to events and incidents.
How do we solve it?
AI4Transportation integra analítica avanzada e IA con múltiples fuentes de datos (ticketing, CDRs, IoT, tracking de apps), garantizando una gobernanza del dato robusta, con virtualización y trazabilidad del linaje de la información.
- Optimización de la movilidad: generación de matrices Origen-Destino y cadenas modales.
- Modelos avanzados: para transporte público y privado, incluyendo predicción de la demanda.
- Análisis espacial: clustering de demanda y detección de puntos de atracción/detracción en las ciudades.
Todo ello respaldado por una infraestructura y software que asegura la calidad, seguridad y accesibilidad de los datos en un ecosistema unificado.
Solution
How do we solve it?
AI4Transportation integrates advanced analytics and AI with multiple data sources (ticketing, CDRs, IoT, app tracking), ensuring robust data governance, with virtualization and traceability of information lineage.
All of this is supported by an infrastructure and software that ensures the quality, security, and accessibility of data in a unified ecosystem.
Mobility optimization
Generation of Origin-Destination matrices and modal chains.
Advanced models
For public and private transport, including demand forecasting.
Spatial analysis
Demand clustering and detection of attraction/detraction points in cities.
Data sources
Ticketing contactless
Passenger Information
System (Apps)
Urban sensors
(Wi-fi, Bluetooth, CCTV)
GPS vehicle
location (SAE)
Planning and scheduling
of the offer
Vehicle control
(telematics)
Exogenous sources
(AEMET, INE, TPV…)
Automatic collection
Automatic passenger
counting
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