Between October 2019 and June 2021 I worked as a Data Science Researcher (internship) at Cabify in Madrid — a year and nine months of research on real, city-scale mobility data that in its second year became part of the newly created Cátedra Cabify-UPM at ETSIT. It is the period that turned my bachelor’s thesis into two conference papers and an IEEE journal publication.
With Prof. Pedro J. Zufiria and Carlos — the people who made the Cátedra what it was while I was there.
🏫️ The Cátedra Cabify-UPM
In October 2020, Cabify and ETSIT-UPM signed the agreement creating the “Cátedra de Tecnologías Inteligentes y de Ciencia de Datos para la Movilidad Sostenible” — a university-industry chair that invests up to 100,000 € per year, mainly in research scholarships for ETSIT students. It is co-directed by Prof. Pedro J. Zufiria (my thesis supervisor) and Carlos Ángel Iglesias, and it exists thanks to a very ETSIT story: Cabify was founded by alumni of the school — CEO Juan de Antonio, CFO Juan Ignacio García, CTO Carlos Herrera and others all studied there. As Cabify’s CTO put it, the company receives data from “tens of thousands of devices directly in the streets every few seconds” — exactly the kind of data my research lived on.
🎯 Areas of action
The Cátedra’s research agenda spans five areas, all aimed at turning mobility data into sustainable-city decisions:
- Data analysis for mobility — the area my own work on bus headways belonged to;
- Sustainable mobility simulation;
- The augmented city;
- Negotiation and auction techniques for transport economics;
- The Sustainable Mobility Observatory — a dashboard that continuously collects sustainable-mobility news from cities and enriches it with machine-learning processes to make it easier to study and understand.
On the vision behind it, Cabify’s founder and CEO Juan de Antonio said: “Being able to create knowledge, foster research and connect innovative solutions to the real mobility challenges is key to identifying high-impact opportunities.” And Félix Pérez, director of ETSIT-UPM, on the surprise of finding so many of the school’s alumni at Cabify — “from the most technical positions to those leading the business strategy” —: “it validates the training we give here and the impact we can generate from Madrid.”
📸 The space
The Cátedra got a physical home inside ETSIT — an open-innovation space designed by Cabify’s own team, branded for the chair and open to the whole school community:
🚌 The problem: urban buses, no telemetry
My work focused on EMT Madrid, the city’s public bus operator, using the arrival estimates that thousands of buses already broadcast — no proprietary on-board telemetry needed. The question: can we detect service anomalies in real time — breakdowns, abnormal delays, schedule drift, bus bunching — from the headways (the time gaps between consecutive buses) alone?
📚 Year one: the bachelor thesis
The first year of the internship (October 2019 – autumn 2020) was devoted to my Bachelor’s Thesis, “Statistical Arrival Time Analysis and Anomaly Detection in the EMT Urban Buses”, supervised by Prof. Pedro J. Zufiria in collaboration with Carlos García-Mauriño. I designed an unsupervised anomaly detector on bus headways based on statistical tools — bivariate modeling of consecutive headway pairs, confidence ellipses per time-of-day slice, and threshold-based flagging — and validated it on months of Madrid data.
📰 Year two: two CSCI 2020 papers and IEEE T-ITS
The second year built directly on the thesis and produced the publications:
- “Headway Estimation in Urban Buses based on Available Arrival Time Estimators” — CSCI 2020
- “Bus Pass Time Estimation based on Efficient Data Gathering from a Slow Mobility Server” — CSCI 2020
- “Bus Headways Analysis for Anomaly Detection” — IEEE Transactions on Intelligent Transportation Systems (in Early Access at the time)
The CSCI 2020 talks were recorded and live on the Cátedra Cabify-UPM YouTube channel. The full technical story — the headway paradigm, the stochastic modeling, the London validation and the monitoring cockpit — lives in my thesis post.
🎯 What the role gave me
- City-scale data: processing and modeling arrival streams from thousands of vehicles — messy, non-stationary, real.
- Statistical rigor for production: turning unsupervised statistical methods into something an operations team could actually act on.
- Publishing from industry: two conference papers and a journal paper written while working — deadlines, reviews and all.
- The academia–industry bridge: watching a chair like Cátedra Cabify-UPM connect a university, a company and its alumni — the model I now appreciate even more from the research side.
Cátedra Cabify-UPM YouTube Channel Cabify GitHub CSCI 2020 · Headway Estimation CSCI 2020 · Bus Pass Time Data Science · Madrid · 2019–2021 Innovaspain article