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”.

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.

Submissions

Authors are invited to submit their manuscripts (in pdf format) written in English by the deadline via Easychair, at https://easychair.org/conferences/?conf=mlsm2020

Papers should be within 8 pages but must not exceed 12 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) sara@fe.up.pt
Henrique Lopes Cardoso (University of Porto, Portugal) hlc@fe.up.pt
Rosaldo Rossetti (University of Porto, Portugal) rossetti@fe.up.pt

Program Committee

Name	Organization	Country
Achille Fonzone	Edinburgh Napier University	United Kingdom
Alberto Fernandez	Rey Juan Carlos University	Spain
Ana L. C. Bazzan	Federal University of Rio Grande do Sul	Brazil
Ana Paula Rocha	University of Porto – LIACC	Portugal
Carlos A. Iglesias	Polytechnic University of Madrid	Spain
Cristina Olaverri-Monreal	Johannes Kepler University Linz	Austria
Daniel Castro Silva	University of Porto – LIACC	Portugal
Dewan Farid	United International University	Bangladesh
Eduardo Camponogara	Federal University of Santa Catarina	Brazil
Eftihia Nathanail	University of Thessaly	Greece
Fenghua Zhu	Chinese Academy of Sciences	China
Francesco Viti	University of Luxembourg	Luxembourg
Gianluca Di Flumeri	Sapienza University of Rome	Italy
Giuseppe Vizzari	University of Milano-Bicocca	Italy
Gonçalo Correia	TU Delft	Netherlands
Hilmi Berk Celikoglu	Technical University of Istanbul	Turkey
Holger Billhardt	Rey Juan Carlos University	Spain
Joao Jacob	University of Porto – LIACC	Portugal
Josep-Maria Salanova	CERTH/HIT	Greece
Juergen Dunkel	FH Hannover – University for Applied Sciences and Arts	Germany
Lior Limonad	IBM Research – Haifa	Israel
Luís Nunes	ISCTE-IUL, Instituto de Telecomunicações	Portugal
Marin Lujak	IMT Lille Douai	France
Mir Riyanul Islam	Mälardalen University	Sweden
Nihar Athreyas	University of Massachusetts, Amherst	USA
Rui Gomes	ARMIS Group	Portugal
Soora Rasouli	Eindhoven University of Technology	Netherlands
Tânia Fontes	University of Porto – INESC TEC	Portugal
Thiago Rúbio	University of Porto – LIACC	Portugal
Zafeiris Kokkinogenis	CEiiA	Portugal