Improving Data-driven Urban Mobility Decision Making through Extreme-scale Urban Mobility Data Analytics
A new Mobility Analytics as a Service (MAaaS) toolset, “EMERALDS”, will set itself apart by moving analytics and sensitive data analytics task to the edge (processing the data on the same devices that collect the data or nearby devices to improve response times and privacy of sensitive data).
EMERALDS aspires to take a groundbreaking step towards improving data processing capabilities, enhancing data security, facilitating data sharing, supporting innovation and research, and promoting sustainability, under the collaborative effort between academic and research institutions, as well as industry stakeholders.
The project’s first physical plenary meeting took place on 8-9 February 2023 in Athens, Greece.
In a rapidly urbanised and connected world, data drives decision-making in European cities. Many 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 data expansion. 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, contribute to improved urban mobility decision making equipping mobility stakeholders with the necessary means to tackle environmental and operational challenges.
Mobility Data Analytics on the Edge
The EMERALDS Toolset demonstrates advanced capabilities in data mining, filtering, management and processing of large amounts and varieties of urban mobility data without neglecting to protect the privacy of individuals. As opposed to off-the-shelf solutions, EMERALDS is creating tailor-made data acquisition services which go beyond the simple collection of extreme mobility data and can distribute the computational workload of the clean-process-analyse pipeline to many nodes of different types, including the data collection layer. To this end, analytics tasks will be pushed towards the node where data reside, enabling the establishment of robust distributed analytics pipelines (including AI inference etc.). The project will evaluate the privacy-awareness effect in the developed tools and services aiming to leverage data locality to perform sensitive data analytics tasks in situ, without data leaving the source, further protecting sensitive data. Leveraging data compression methods tailored for spatiotemporal data, higher-quality data will be transmitted to the subsequent computing levels enabling the design of far more advanced urban mobility data analytics tools and services.
EMERALDS Coordinator, Foivos Galatoulas:
“EMERALDS tackles the challenge of processing extreme mobility data, stressing the capabilities of currently available cloud computing solutions and pinpointing the need to shift towards ground-breaking data processing paradigms. The developments related to big mobility data have already spurred disruption across business domains, revolutionizing platform economies and unleashing innovative and intelligent services. Digitization is driving the evolution of the mobility ecosystem and services, changing the experience of urban transport and mobility services for consumers and shaping their expectations for years to come.
By advancing state-of-the-art extreme data mining, aggregation, and analytics technologies and integrating them with urban mobility and transportation domain expertise, the project presents an unprecedented opportunity to achieve impactful end-to-end solutions to real-life problems. The project uses advanced technologies, including AI, Big Data, and edge/fog/cloud computing, to explore new pathways on spatiotemporal data analytics, providing urban mobility operators and planners with advanced solutions for extreme-scale mobility data analytics tools and services.
The outcomes of EMERALDS can assist in evaluating prospective or existing transport infrastructure with varying resolutions, infer which mobility modes can enhance service quality in transport networks while delivering trip demand, event occurrence, smart routing, and traffic congestion predictions with improved accuracy.“
Applications: Public event safety, traffic pollution reduction, public transport optimisation and more!
To demonstrate the wide-ranging and relevant applications of the EMERALDS Toolset, three use cases are being carried out in The Hague, Utrecht and Riga. The first focuses on risk assessment, prediction and forecasting during events to reduce crowdedness, reduce disturbance for residents, efficiently deploy support personnel and reduce the risk of riots. The second focuses on multi-modal integrated traffic management to reduce greenhouse gases, increase urban livability, increase public transport accessibility, and reduce maintenance costs. The third focuses on Trip and Traffic Flow Data Analytics to help to optimise the public transport network (cost, user experience, quality of service) and the profitability of the network by determining the profitable routes and the need for subsidised routes.
EMERALDS comprises 16 consortium partners and 4 associated entities from 10 countries. The consortium that will bring the platform and its initial use cases forward is led by Inlecom and includes the Austrian Institute of Technology, Trust-IT, Commpla, University of Piraeus Research Center, Atos, Université libre de Bruxelles, PTV Group, Sistema, Telecommunication Systems Institute, Groupa93, Carto, TU Delft, Arane, Konnecta Systems and Argaleo with associated entities Rigas satiksme, City of York Council, Gemeente Utrecht and Gemeente Den Haag.
The project is scheduled to run until December 2025, but even before then, EMERALDS will validate the toolset with two Early Adoption Demonstrators, Transport Network Optimisation in York and North Yorkshire and Commuter Patterns Analytics in a newly developed application.
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