Development of a scalable geospatial data analytics platform for smart urban mobility

The client faces difficulties in processing and analyzing vast, dynamic geolocated data streams from numerous IoT devices, connected vehicles, and mobile sources to derive actionable insights for traffic management, prediction, and pattern detection. Existing systems lack scalable, high-performance solutions capable of supporting real-time decision-making and spatial-temporal analysis in large metropolitan areas.

A large metropolitan transportation authority seeking to leverage real-time and historical mobility data from connected vehicles, city sensors, and mobile applications to optimize traffic flow, reduce congestion, and improve urban mobility planning.

• Implement a cloud-based management system for large-scale mobility and spatial-temporal geodata.
• Enable real-time traffic measurement and monitoring using high-volume data streams from connected vehicles, sensors, and mobile apps.
• Develop scalable predictive models for traffic routing, logistics optimization, and congestion cost reduction.
• Create geospatial indexes and visualizations supporting geolocated event processing and anomaly detection.
• Increase system performance through GPU-based acceleration and scalable backend architecture, supporting thousands of IoT elements and millions of citizens.
• Deliver new data-driven features that improve urban mobility organization, monitoring, and planning capabilities.

• Cloud-based management system for high-volume mobility data integration
• Support for spatial-temporal data processing with geospatial indexes and event processing engines
• Real-time traffic data ingestion and live streaming analytics
• Historical data storage with pattern recognition, origin-destination matrix computation, and anomaly detection
• Predictive modeling capabilities for traffic flow, routing, and logistics optimization
• GPU-accelerated geospatial indexing and high-performance computing for scalable analytics
• Mobile application backend support for data collection and user engagement
• User interfaces for data visualization, traffic monitoring, and decision support

• Connected vehicle data streams
• City sensors and IoT device feeds
• Mobile application data sources
• High-performance geospatial data engines
• Predictive analytics and machine learning models
• Real-time alerting and visualization tools

• Support processing of millions of geolocated data points per day
• Real-time analytics with minimal latency
• High system availability and fault tolerance
• Data security and privacy compliance (e.g., GDPR)
• Scalable architecture supporting future growth and data volume increases
• Performance benchmarks ensuring response times within seconds for live data queries

The new mobility data analytics platform aims to enable real-time traffic monitoring and prediction, ultimately reducing congestion and improving urban mobility efficiency. Expected outcomes include enhanced decision-making, cost reductions in traffic management, and improved citizen experience through reliable navigation and mobility services. The scalable architecture will support continuous data growth and evolving urban transportation needs, providing a foundation for innovative smart city mobility solutions.