Cities are experiencing unparalleled growth, facing new challenges as they endeavor to become healthier, more sustainable and safer places for citizens to live and work. One of such challenges arise in urban mobility and related fields like transport planning and traffic engineering, which are evolving to embrace the myriad of new opportunities that mobile personal devices, together with data science, may bring to citizens. Traffic engineering, for instance, has traditionally been based on stationary sensor data like vehicle counters, induction loops and more recently thermo-cameras to understand core traffic characteristic parameters including flow speed, street saturation and the distribution of cars, trucks, and pedestrian participants. With the ubiquity of mobile personal devices (e.g., wearables, smartphones) and the development of machine learning (ML), however, new opportunities have emerged to collect and analyze mobility data at the granularity of a driver or a pedestrian.

This kind of personal data, which is composed of the geolocated registers left by devices that can be considered to be attached to a single individual, can provide valuable information to understand mobility and traffic patterns and enable new kinds of analytics like origin-destination flow, detecting critical traffic junctions, heavily frequented transfer areas and road segments, estimate street maintenance and parking needs, and many other applications. With this information, for example, a city may seek to better understand the current mobility flow patterns and design an effective strategy for managing traffic and reduce the associated pollution.

The MOBILYTICS project focuses on such a scenario in which multiple private companies and public entities may be collecting, through their respective apps and services, mobility data of their users and may be interested, for example, in acquiring data from other companies/entities to complement theirs, in releasing the data for the public good or research studies, or they may wish to monetize their data through other means.

Anonymizing a microdata (i.e., a database containing records at the individual level) faces two opposing objectives that need to be harmonized. On the one hand, the privacy of the individuals appearing in the microdata must be guaranteed, and this is typically achieved by modifying the original data. On the other hand, the utility of the released data must be preserved, that is, analyses on the anonymized data should not yield results that are too different from those on original data. The field of statistical disclosure control (SDC), also known as anonymization, provides three main techniques to find a suitable trade-off between privacy and utility: (i) non-perturbative masking (methods that reduce the level of detail of the original data while preserving their truthfulness); (ii) perturbative masking (methods that modify the original data without preserving their truthfulness); and (iii) synthetic data (methods that build a new data set from scratch based on a model of the original data).

The development of a more sustainable mobility, the core goal of MOBILYTICS, has numerous immediate benefits to our society as a whole, such as reducing the number of polluting vehicles in big cities, increasing the use of green or alternative public transport services, which ultimately may help reduce environmental impact and atmospheric pollution, and improve people's daily lives.

Read More... Read Less...


2024 - Articles

2023 - Journal articles

  1. Jordi Mongay Batalla, Luis J. de la Cruz Llopis, Germán Peinado Gómez, Elzbieta Andrukiewicz, Piotr Krawiec, Constandinos X. Mavromoustakis, Houbing Song, "Multi-layer security assurance of the 5G automotive system based on Multi-Criteria Decision Making", IEEE Transactions on Intelligent Transportation Systems, Accepted, 2023.
  2. Juan Pablo Astudillo León, Luis J. de la Cruz Llopis and Francisco Rico-Novella, "A machine learning based Distributed Congestion Control Protocol for multi-hop wireless networks", Computer Networks, DOI: 10.1016/j.comnet.2023.109813, Volume 231, p. 109813, 2023.
  3. A Robles-González, P Arias-Cabarcos, J Parra-Arnau, "Privacy-Centered Authentication: a new Framework and Analysis", Computers and Security, DOI: 10.1016/j.cose.2023.103353 , Volume 132, p. 103353, 2023.
  4. Cristina Satizábal, Rafael Páez, Jordi Forné, "Secure Internet Voting Protocol (SIVP): A secure option for electoral processes", Journal of King Saud University - Computer and Information Sciences, DOI: 10.1016/j.jksuci.2020.12.016 , Volume 34, Issue 6B, pp. 3647-3660, 2022.

2024 - Conferences

  1. P. Guerra-Balboa, À. Miranda-Pascual, J. Parra-Arnau, T. Strufe, "Composition in Differential Privacy for General Granularity Notions", IEEE Computer Security Foundations Symposium (CSF), Accepted, 2024.

2023 - Conferences

  • Mateo Sebastian Lomas, Robinson Paspuel, Cristhian Iza, Mónica Aguilar Igartua, "SLOW-Based Pseudonym Changing Schemes for Location Privacy in Vehicular Networks", 26th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2023), 10.1145/3616388.3617520, 2023.
  • Rubio Jornet, Víctor; Parra Arnau, Javier; Forné Muñoz, Jordi, "Generación sintética de trayectorias mediante aprendizaje profundo con garantías de privacidad diferencial", XV Jornadas de Ingeniería Telemática (JITEL 2023), Accepted, 2023.
  • Bazán, Alberto; Barbecho, Pablo; Aguilar Igartua, Mónica, "Diseño de esquema de carga/descarga para vehículos eléctricos en entornos urbanos", XV Jornadas de Ingeniería Telemática (JITEL 2023), Accepted, 2023.
  • Juan Pablo Astudillo, Anthony Busson, Luis J. de la Cruz Llopis, Thomas Begin, Azzedine Boukerche, "Strategies to plan the number and locations of RSUs for an IEEE 802.11p-based infrastructure in urban environment", ACM MSWiM 2023, Accepted, 2023.
  • Ahmad Khalil, Aidmar Wainakh, Ephraim Zimmer, Javier Parra-Arnau, Antonio Fernández Anta, Tobias Meuser, Ralf Steinmetz, "Label-Aware Aggregation for Improved Federated Learning", IEEE International Conference on Fog and Mobile Edge Computing, Accepted, 2023.
  • Àlex Miranda-Pascual, Patricia Guerra-Balboa, Javier Parra-Arnau, Jordi Forné, Thorsten Strufe, "SoK: Differentially Private Publication of Trajectory Data", Proceedings on Privacy Enhancing Technologies, DOI: 10.56553/popets-2023-0065 , Volume 2023, Issue 2, pp. 496-516, 2023.
  • Apps

    MobilitApp is a tool to help to analyse the urban mobility in our cities. MobilitApp periodically records 3 sensors (accelerometer, magnetometer and gyroscope) from the citizens' smartphones during their multimodal trips. The information obtained is processed using deep learning techniques to predict the transportation mode used by the user, i.e. bicycle, bus, car, e-bicycle, e-scooter, metro, motorbike, run, stationary, train, tram and walk. The user's information is treated completely anonymously.

    Data Sets


    Mónica Aguilar Igartua Coordinator


    Copyright © 2023 MOBILYTICS . Template by W3layouts

    • Este trabajo es parte del proyecto de I+D+i ``Anonymization technology for AI-based analytics of mobility data (MOBILYTICS)", TED2021-129782B-I00, financiado por MICIU/AEI/10.13039/501100011033 y por la Unión Europea NextGenerationEU/PRTR.
    • This work is supported by the Spanish Government under research project ``Anonymization technology for AI-based analytics of mobility data (MOBILYTICS)", TED2021-129782B-I00, funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.