In urban environments, FCD, Bluetooth, ANPR and camera systems continuously generate data. MANGO-HUB is a centralized platform that integrates these heterogeneous data streams within a unified big data architecture, transforming raw records into real-time mobility analytics and decision-support outputs.
Multi-Source Data Integration
Urban mobility cannot be monitored through a single sensor or software system. Floating Car Data (FCD) provides insights into speed and movement patterns across road segments, while Bluetooth sensors measure travel times between designated points. Vehicle-counting, PTZ, and fisheye cameras generate data on traffic volume, vehicle classification, direction of travel, and intersection approach data.
The first element that makes MANGO-HUB a “hub” is its ability to bring together data sources from different brands and models within a unified data ingestion layer. Data from environmental and meteorological sensors, parking systems, public transport operations, and micromobility datas can also be incorporated into the same urban mobility context. This creates an integrated data ecosystem instead of a collection of isolated datasets.
Data Fusion and Time-Location Matching
Simply collecting data in one place is not sufficient. Data sources may differ in their timestamps, spatial resolution, measurement intervals, and quality. MANGO-HUB uses data quality control, spatial and temporal normalization, and data fusion to evaluate related records from multiple sources together.
For example, when an FCD-based speed reduction is detected on a road segment, it can be correlated with Bluetooth travel-time data, camera-based vehicle queue lengths, intersection queues, and incident data. The system therefore goes beyond simply indicating that traffic has slowed. It also helps determine whether the slowdown results from recurring peak-hour conditions, demand exceeding capacity, a bottleneck, or a potential anomaly.
Real-Time and Historical Mobility Analytics
MANGO-HUB combines real-time stream processing with historical data analysis within a unified framework. While operators monitor current traffic conditions, transportation specialists can examine historical speed profiles, congestion indices, and changes in travel times.
The platform can generate outputs such as FCD and Bluetooth flow analytics, Origin–Destination (OD) matrices, peak-hour analysis, queue length measurements, bottleneck identification, intersection performance assessments, and anomaly detection. Time-series analysis enables daily, weekly, and long-term trends to be compared, while predictive models support the assessment of short-term traffic patterns. Findings identified at the intersection level can also be reanalyzed at the corridor, district, or citywide scale.
More Than a Dashboard
MANGO-HUB is not merely a dashboard that displays charts and maps. A dashboard serves as the visual presentation layer for generated indicators, whereas a hub encompasses the entire process of collecting, validating, standardizing, correlating, and analyzing data, and transforming data into decision-support outputs.
Interactive maps, stress maps, time-series analyses, and Key Performance Indicators (KPIs) help operators interpret changes across the network more efficiently. The analytical outputs generated by MANGO-HUB can also be integrated with other applications within the MANGO ecosystem. For example, MANGO-AI can use these outputs for natural language queries and decision-support scenarios.
Conclusion
What makes MANGO-HUB a true hub is not its ability to store large volumes of data, but its capacity to integrate diverse data sources, analytical scales, and user needs within a unified analytical framework. Positioned at the center of the data chain extending from collection to decision support, the platform enables urban mobility to be evaluated as a comprehensive and measurable system rather than as a collection of isolated components.
Keywords: MANGO-HUB, Mobility Data Platform, Integrated Traffic Data, FCD Analysis, Big Data Analytics, Intelligent Transportation Systems, MANGO-AI, MANGO, Manage and Go