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MANGO Platforms

MANGO-HUBMANGO HUB Urban Mobility Data Platform

From Data to Actionable Urban Intelligence

MANGO-HUB is an integrated big data platform that collects urban mobility data in a single center, processes it, and transforms it into meaningful information through advanced analytics models.

The city’s digital intelligence: one center, holistic management. It fuses data from different sources, processes it within seconds, and converts it into intelligent outputs on which operators can take direct action.

3D Explore the Experience

Integrated Urban Mobility and Big Data Platform

With its comprehensive, brand- and model-independent data collection capability, MANGO-HUB integrates radar sensors, Bluetooth (BLUESIS), ANPR imaging, PTZ and fisheye cameras, FCD/wide-area Floating Car Data, environmental, weather, and meteorological data, environmental sensors, and urban/public transport operations in a common center.

Through its data fusion, real-time processing, and interpretation layers, the platform produces scalable analyses ranging from individual junctions to the city scale and provides municipalities and operators with real-time decision-making capabilities.

MANGO-HUB CORE — DATA FUSIONRADARBLUETOOTHANPRPTZ / FISHEYEFCDMETEOROLOGYENVIRONMENTALPUBLIC TRANSPORTMANGO-HUBbig data core
How It Works

From data to action in six steps

FIELD SYSTEMSJUNCTIONCAMERASENSORMANGOall data sources
01 Data is collected from multiple sources
Radar, Bluetooth, ANPR, PTZ and fisheye cameras, FCD, environmental sensors, and public transport operations are connected to MANGO-HUB Core independently of brand and model.
DATA FUSION — SINGLE POOLRADARBTANPRFCDFUSIONsingle data layer
02 Data fusion is performed
Data from different sources is fused to create an integrated mobility data layer.
VIDEO STREAM — REAL-TIMEAIDATAAI-BASED VIDEO ANALYTICS PLATFORM
03 Real-time processing and quality control
Data is analyzed within seconds and made reliable through quality control.
ANALYSIS — HOURLY / PERIODICroad network performance · density · junction performance
04 Advanced analytics and AI forecasting
Trend forecasting and incident detection are performed using time-series databases, distributed processing infrastructure, statistical models, and machine learning algorithms.
STRESS MAP — TRAFFIC LOADSTRESS LEVELHIGH · center
05 Interpretation through the Stress Map
The city’s traffic load is visualized through the “Stress Map”; the situation is interpreted using peak-hour, incident, and layer analyses.
DIGITAL MAP — LIVE VIEWFLOW STATUS
06 The operator takes action from a single screen
Using intelligent outputs that support direct action, the operator monitors and manages all of the city’s mobility arteries from a single screen.

MANGO-HUB analytics modules

Analytics modules that monitor the pulse of the city transform different data sources into meaningful outputs.

Bluetooth Traffic Analytics Travel time measurements on road sections, average speed profiles, delay times, and corridor-based performance comparisons.
FCD Flow Analytics Wide-area traffic flow, vehicle speeds, detection of critical congested nodes, and dynamic density maps.
Visual Intelligence and Junction Management Vehicle classification, pedestrian detection, and turning movement analysis; queue length estimation, junction performance indices, and pedestrian density monitoring.
Image Processing Processing raw data received from cameras and converting it into quantitative metrics.
Public Transport Analytics Schedule regularity, route performance, waiting times, capacity utilization, and punctuality analyses.
Micromobility Analytics Scooter/bicycle movements, instantaneous usage density, demand profiles, and station location efficiency.
Environmental Analytics The relationship between air quality, temperature, humidity, and noise data and mobility; measurement of the impact of traffic density on air quality.
Parking Management Occupancy rates for open and enclosed parking facilities, entry-exit movements, peak-hour analyses, and dynamic demand profiles.
Artificial Intelligence and Forecasting Incident detection, future trend forecasts, and visualization of the city’s traffic load through the “Stress Map.”

Key Features

Data fusion — combining multiple sources in a single data environment Real-time processing — analysis within seconds Brand- and model-independent data collection Radar sensors Bluetooth (BLUESIS) data ANPR imaging PTZ and fisheye cameras FCD / wide-area Floating Car Data Environmental, weather, and meteorological data Environmental sensors Public transport operations data Dynamic density and speed maps Stress Map visualization Time-series databases Distributed processing infrastructure Statistical models and machine learning Peak-hour and incident analysis Layer control

Why MANGO-HUB?

Scalable, measurable, and manageable mobility from individual junctions to the city scale.

Scalability
Scalable analytics infrastructure from junctions to entire cities.
Efficiency
Improvements that save fuel and time.
Manageability
The ability to monitor and manage all of the city’s mobility arteries from a single screen.
Real-Time Decision-Making
Fast, accurate, data-driven decision-making capabilities for municipalities and operators.

Advantages

MANGO-HUB provides an integrated big data and decision-support layer extending from data fusion to operator action.

Combines data from different sources in a single center
Processes data within seconds and subjects it to quality control
Produces intelligent outputs on which operators can take direct action
Provides scalable analysis from junctions to the city scale
Supports improvements that save fuel and time
Enables all of the city’s mobility arteries to be managed from a single screen
Forecasts future trends through artificial intelligence and predictive analytics
Makes traffic load visible through the Stress Map

Frequently Asked Questions

What is MANGO-HUB? +

MANGO-HUB is an integrated big data platform that collects urban mobility data in a single center, processes it, and transforms it into meaningful information through advanced analytics models.

Which data sources does MANGO-HUB work with? +

Radar sensors, Bluetooth (BLUESIS), ANPR imaging, PTZ and fisheye cameras, FCD/wide-area Floating Car Data, environmental, weather, and meteorological data, environmental sensors, and public transport operations can be integrated independently of brand and model.

Which analytics modules does MANGO-HUB provide? +

It includes Bluetooth and FCD flow analytics, visual intelligence and junction management, public transport and micromobility analytics, environmental analysis, parking management, and artificial intelligence and forecasting modules.

What is the Stress Map? +

It is a visualization of the city’s traffic load that enables the situation to be interpreted quickly through peak-hour, incident, and layer analyses.

Who is MANGO-HUB suitable for? +

It provides real-time decision-making capabilities for municipalities and traffic operators that require scalable analysis from individual junctions to the city scale.

Does MANGO-HUB process data in real time? +

Yes. The platform analyzes data from different sources within seconds, performs quality control, and provides operators with intelligent outputs.

Learn more about MANGO-HUB

Contact us to learn more.

ISSD

A complete mobility provider developing intelligent transportation technologies since 2009.

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