October 2026 – FEDORA

Urban Mobility and Logistics Conference

The Urban Mobility and Logistics Conference 2026 will take place on 11 November in Milan, following its first event in Bologna on 18 February 2026. The conference will explore innovative solutions to urban mobility and logistics challenges, bringing together public authorities, industry representatives, and experts. Key topics will include emerging mobility technologies, sustainable transport, last-mile logistics, transport system integration, and inclusive mobility. The event aims to promote collaboration and identify opportunities to develop smarter, more sustainable, and integrated urban transport systems.

FEDORA will be featured at the conference through a presentation on the Reggio Emilia case study, delivered by Alberto Merigo, Urban Mobility Planner at the Municipality of Reggio Emilia. The presentation will focus on the dynamic pricing pilot planned for early 2027, exploring innovative approaches to urban traffic management and mobility regulation.

8th Conference On Sustainable Mobility & Intelligent Transportation Systems

The 8th Sustainable Mobility & Intelligent Transport Systems Conference will take place on October 29–30, 2026, in Nicosia, Cyprus. The event will bring together public authorities, operators, companies, and industry experts to discuss key challenges and opportunities in sustainable mobility, public transportation, intelligent transportation systems, and the development of more connected cities.

Over two days, the program will cover topics including MaaS, mobility infrastructure, artificial intelligence in transportation, traffic management, smart cities, and emerging technologies for public transit.

FEDORA will be presented at the Conference during a dedicated session highlighting EU-funded transportation projects on 30 October 2026. Prof. Panayiotis Kolios, Assistant Professor at the KIOS Research and Innovation Center of Excellence at the University of Cyprus, will deliver a 10-minute presentation introducing the project, entitled “Development of the Next Generation of Multimodal Traffic Management.

12th IEEE International Smart Cities Conference

The IEEE International Smart Cities Conference (ISC2) is the annual flagship event sponsored by the IEEE Smart Cities Technical Community. It brings together researchers, engineers, practitioners, students, and urban planners from academia, industry, and public authorities to present and discuss cutting-edge research and practice in Smart Cities, sustainable communities, and urban planning. The conference provides a multidisciplinary forum for exchanging ideas and experiences on emerging developments in theory, analytical and numerical modelling and simulation, experimentation, advanced deployment, and real-world case studies that address the complex challenges of contemporary urban environments and ecosystems.

FEDORA Partner UCY will present the following paper:

Learning Charging Priority Policies for Adaptive Electric Vehicle Charging Management

on October, 27th, 2026. The paper will cover an adaptive EV charging management system that uses real-world data to efficiently manage charging priorities, even when drivers’ arrival times, parking durations, and charging needs are uncertain.

Same Place, Many Names: Resolving Data Ambiguity Across Languages

Teaching Data to Recognise “Gipuzkoa” and “Gipuzcoa” as the Same Place

This data-matching method was developed as part of FEDORA Deliverable D3.1 – Integrated Cross-Sectoral Data Space.

Anyone who has worked with real-world, multi-source data knows the problem: the same place, organisation or entity can appear under several different names. A Basque province might be spelled “Gipuzkoa” in one dataset and “Gipuzcoa” in another. A city might be abbreviated as “S. Cristóbal” in one source and written out fully as “San Cristóbal” in the next. And in genuinely multilingual regions, the same place might carry two entirely different names in two different languages — “Pamplona” in Spanish, “Iruña” in Basque — with no obvious textual overlap at all.

For a data space pulling in datasets from dozens of organisations and regions, this isn’t a cosmetic issue — it silently creates duplicate entries, breaks joins between datasets, and undermines analysis that depends on knowing two records actually refer to the same thing.

FEDORA’s solution combines three distinct matching techniques, each suited to a different kind of naming variation. The first, EditDistance, measures how many single-character changes — insertions, deletions or substitutions — it would take to turn one string into another, making it effective for catching typos and minor misspellings. The second, TF-IDF, breaks text into fragments and compares their statistical overlap, which makes it well suited to catching abbreviations. The third, and most sophisticated, uses text embeddings — numerical representations that capture the underlying meaning of a word rather than just its spelling — allowing the system to recognise that two completely different-looking strings, in two different languages, refer to the same concept.

Run together through the open-source PolyFuzz framework, these three models let the system automatically group name variants and flag likely duplicates for human review, rather than requiring every dataset to be manually reconciled by hand. In one worked example from the deliverable, the system correctly grouped “Hondarribia Gipuzkoa” with its misspelled variant “Hondarribia Gipuzcoa,” each match carrying a confidence score to support the final human check.

It’s a small step in a larger data-cleaning pipeline, but it’s foundational: before mobility data from six different European regions can be meaningfully compared, combined, or analysed together, it first has to agree on what to call the places it’s describing.

Learn more: The full methodology and worked examples are available in FEDORA Deliverable D3.1 – Integrated Cross-Sectoral Data Space, Section 5.1.1.

Filling the Gaps in Traffic Sensor Data

Reconstructing Missing Traffic Data with Tensor Mathematics

This tensor-based data imputation method was developed and tested as part of FEDORA Deliverable D3.1 – Integrated Cross-Sectoral Data Space.

Traffic sensors fail. Induction loops drop out, communication links go down, and entire stretches of a city’s traffic network can go dark for hours, days, or longer. Yet the analytics, forecasting and traffic-management tools that cities rely on need continuous, complete data to work properly. The question FEDORA set out to answer is a practical one: when the data has holes in it, how do you reliably fill them back in?

The answer draws on a mathematical structure called a tensor — essentially a multi-dimensional generalisation of the tables and matrices most data tools use. Traffic data naturally has several dimensions at once: it varies by location, by day, and by time of day, and all of these dimensions are correlated — a traffic jam on one street tends to be linked to conditions nearby and to what happened at the same time yesterday. A technique known as low-rank tensor learning uses exactly this structure, treating the network’s spatial layout and its recurring temporal patterns as built-in clues that guide how missing values should be reconstructed.

Two specific methods were put to the test. One, called ST-LRTC, builds in the physical logic of how traffic actually flows through a network — respecting the continuity and daily rhythm of traffic and the way congestion propagates spatially between locations. The other, GLSKF, is tuned to capture both the big-picture, long-range patterns across the network and the short, sudden fluctuations — like a rush-hour spike — that a smoother model might miss.

Both were tested against real-world data: a month of vehicle-flow readings from 339 induction loops in a northern Spanish city, recorded every minute — over 44,000 data points per sensor location. The team then deliberately introduced six different patterns of missing data, from random single dropouts to entire sensors or entire days going silent, at severities ranging from a light 10% gap to a severe 90% blackout, and measured how accurately and how quickly each method could reconstruct the missing values.

The results give city operators and data teams something genuinely useful: an evidence-based comparison of how each method performs under different real-world failure scenarios, not just in theory. That matters because the type of data gap — a single faulty sensor versus a whole network outage — calls for different reconstruction strategies, and now there’s a documented basis for choosing between them.

It’s unglamorous, behind-the-scenes work, but it’s exactly the kind of engineering that keeps mobility data usable when the real world doesn’t cooperate — and it’s a foundation piece for any traffic-management or simulation tool built on top of incomplete, real-world sensor networks.

Learn more: The full methodology, algorithm details and results tables are available in FEDORA Deliverable D3.1 – Integrated Cross-Sectoral Data Space, Section 5.3.2.1.