EMERALDS is a Horizon Europe project developing a Mobility Analytics as a Service (MAaaS) toolset which will set itself apart by moving more than 30% of the analytics and 50% of sensitive data analytics to edge computing (processing the data on the same devices that collect the data or nearby devices to improve response times and privacy of sensitive data).
The technology can improve decision-making by public authorities and individuals, leading to more efficient mobility, saving time, money and reducing environmental impact.
In a rapidly urbanised and connected world, data is driving decision-making in cities across Europe. A plethora of sensors and computing devices covering the edge/fog/cloud compute continuum are deployed, connected, and monitored. New mobility services are reshaping the urban mobility landscape leading to a radical expansion of data.
By leveraging on the accumulated data created within the duty cycle of these services over the next three years, the EMERALDS project will design, develop and create an urban data-oriented Mobility Analytics as a Service (MAaaS) toolset to exploit the untapped potential of extreme urban mobility data and as a result, improve urban mobility decision making.
The toolset will demonstrate advanced capabilities in data mining of large amounts and varieties of urban mobility data whilst considering privacy aspects. As opposed to off-the-shelf solutions, EMERALDS will actually create tailor-made data acquaintance services which goes beyond the simple collection of extreme mobility data and is able to distribute computational workload of the clean-process-analyse pipeline to many nodes of different types, including the data collection layer per se (i.e., at the edge).
As a result, higher-quality data can be transmitted to the subsequent computing levels leading to the design of far more advanced data analytics tools and services.
The EMERALDS Toolset will provide a new resource providing meaningful knowledge and insights to aid transportation engineers, urban planners, urban data scientists and policy makers to make data-based decisions in a wide spectrum of applications, such as traffic engineering and risk management.
From simple use-cases like rerouting traffic to prevent congestion, multi-modal integrated traffic management, trip demand forecasting, to more crucial cases like crowdedness informed/aware risk assessment, emergency evacuations and search and rescue missions, being able to analyse data from the plethora of available sources can not only save citizens’ time in their routine activities, but also prevent catastrophes that may lead to the loss of life.