Machine Learning in Smart Mobility

The Workshop on Machine Learning in Smart Mobility (MLSM), is co-located with the 21st International Conference on Intelligent Data Engineering and Automated Learning — IDEAL 2020, to take place in Guimarães, Portugal, on November 4-6.

The workshop’s technical program will include a session hosted by the H2020 SIMUSAFE project, “New Training Modules to Increase Usage of ‘Soft’ Modes of Transport”.

The SIMUSAFE session will take place on November 4th, from 2:30PM to 18:00PM

Please join the virtual session by following this link:

Workshop Program

Call for Papers

The workshop will gather both the ML community and transportation practitioners to discuss how cutting-edge ML technologies can be effectively applied to improve the performance of transportation and mobility systems on a sustainable basis, according to three important dimensions: economic, environmental, and social. This forum also aims to generate new ideas towards building innovative applications of machine learning into smarter, greener, and safer mobility systems, stimulating contributions that emphasize on how theory and practice are effectively coupled to solve real-life problems in contemporary transportation, naturally including all sorts of mobility modes and their intrinsic interactions. Indeed, contemporary transportation is evolving rapidly on a more intelligent basis, and the concept of Intelligent Transportation Systems (ITS) has become already a reality among us, supporting the infrastructure leading to the emergence of the so-called Smart Mobility, and to a whole bunch of Mobility-as-a-Service (MaaS) options as we witness today. Also, when placed within the framework of Smart Cities, smart mobility gains more and more complexity and brings about new performance measures such as equity and social impact, privacy and security, ethical and legal compliance, explainable decision-support, while environmental sustainability is strongly emphasized.

Therefore, this workshop is within the application-oriented, integrative, and multi-disciplinary perspectives of Machine Learning, and contributes to the IDEAL Conference with an appropriate forum to foster discussions on emerging and challenging topics in intelligent data analysis, data mining and their associated learning systems and paradigms in the very dynamic and evolving domain of urban mobility. It is intended to leverage the cross-fertilization between ML and Smart Mobility, offering the appropriate support for a more effective and improved decision-making platform underlying urban mobility planning and management tasks, to which data is paramount. This workshop is also being promoted as an initiative of the IEEE ITS Society’s Technical Activities Sub-committee on Artificial Transportation Systems and Simulation, and as part of the H2020 SIMUSAFE Project.

Topics of interest include, but are not limited to:

  • Big data, data mining, and knowledge discovery in urban mobility;
  • Traffic management and highway control;
  • Traffic flow forecasting and incident detection;
  • Traveller behaviour and decision-making modelling;
  • Applications of deep learning ranging from object detection to end-to-end learning in autonomous driving;
  • Probabilistic graphical models for SLAM and sensor fusion;
  • Machine learning for sparse sensors like LIDAR and RADAR;
  • Reinforcement learning for trajectory and route planning;
  • Machine learning-based multi-sensor fusion algorithms;
  • Real-time system implementations on machine learning platforms for intelligent transportation;
  • Data-driven design, operation, timetabling and real-time control of logistics systems and freight transport;
  • Machine learning in transport policy, planning, design and management of urban mobility;
  • Travel demand analysis, prediction and transport marketing;
  • Machine learning for advanced traveler information systems and services;
  • Naturalistic data collection and monitoring of pedestrians and crowds;
  • Data-driven urban planning toward sustainable mobility;
  • Naturalistic data for human factors and road safety studies;
  • Behaviour modelling and social network analysis of transportation systems;
  • Machine learning in electric mobility and its relationship with smart grids and the electricity market;
  • Computer vision in autonomous driving;
  • Data-driven preventive maintenance policies;
  • Anomalous trajectory mining and fraud detection;
  • Smart architectures for vehicle-to-vehicle/vehicle-to-infrastructure data communications;
  • Automatic assessment and/or evaluation on the transport reliability;
  • Open-data infrastructure management and maintenance for mobility applications;
  • Legal and ethical issues in data management for smart mobility.


Authors are invited to submit their manuscripts (in pdf format) written in English by the deadline via Easychair, at

Papers should be within 8 pages, and must comply with the format of Springer LNCS/LNAI Proceedings.

Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers.

For accepted papers, the corresponding author will be required to complete and sign a Consent-to-Publish form.

Important Dates

The planned dates are the following:
Submission of manuscripts (hard deadline): June 30, 2020.
– Notification of acceptance: July 30, 2020
– Camera-ready (final) version submission: September 1, 2020
– Workshop: November 4, 5 or 6, 2020

Workshop Program Chairs

Sara Ferreira (University of Porto, Portugal)
Henrique Lopes Cardoso (University of Porto, Portugal)
Rosaldo Rossetti (University of Porto, Portugal)

Program Committee

Achille FonzoneEdinburgh Napier UniversityUnited Kingdom
Alberto FernandezRey Juan Carlos UniversitySpain
Ana L. C. BazzanFederal University of Rio Grande do SulBrazil
Ana Paula RochaUniversity of Porto – LIACCPortugal
Carlos A. IglesiasPolytechnic University of MadridSpain
Cristina Olaverri-MonrealJohannes Kepler University LinzAustria
Daniel Castro SilvaUniversity of Porto – LIACCPortugal
Dewan FaridUnited International UniversityBangladesh
Eduardo CamponogaraFederal University of Santa CatarinaBrazil
Eftihia NathanailUniversity of ThessalyGreece
Fenghua ZhuChinese Academy of SciencesChina
Francesco VitiUniversity of LuxembourgLuxembourg
Gianluca Di FlumeriSapienza University of RomeItaly
Giuseppe VizzariUniversity of Milano-BicoccaItaly
Gonçalo CorreiaTU DelftNetherlands
Hilmi Berk CelikogluTechnical University of IstanbulTurkey
Holger BillhardtRey Juan Carlos UniversitySpain
Joao JacobUniversity of Porto – LIACCPortugal
Josep-Maria SalanovaCERTH/HITGreece
Juergen DunkelFH Hannover – University for Applied Sciences and ArtsGermany
Lior LimonadIBM Research – HaifaIsrael
Luís NunesISCTE-IUL, Instituto de TelecomunicaçõesPortugal
Marin LujakIMT Lille DouaiFrance
Mir Riyanul IslamMälardalen UniversitySweden
Nihar AthreyasUniversity of Massachusetts, AmherstUSA
Rui GomesARMIS GroupPortugal
Soora RasouliEindhoven University of TechnologyNetherlands
Tânia FontesUniversity of Porto – INESC TECPortugal
Thiago RúbioUniversity of Porto – LIACCPortugal
Zafeiris KokkinogenisCEiiAPortugal