MLSM2020: Workshop on Machine Learning in Smart Mobility |
Website | http://simusafe.eu/workshop/mlsm/ |
Submission link | https://easychair.org/conferences/?conf=mlsm2020 |
Submission deadline | June 30, 2020 |
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 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.
Important Dates
– Submission of manuscripts (hard deadline): June 25, 2020.
– Notification of acceptance: July 10, 2020
– Camera-ready (final) version submission: July 31, 2020
- Workshop: November 4, 5 or 6, 2020
Submission Guidelines
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, 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.
List of Topics
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.
Committees
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
- 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
- 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
Publication
MLSM2020 accepted papers will be published in the proceedings of IDEAL 2020, in a Springer LNCS volume.
Venue
The conference will be held in Guimarães, Portugal.
Contact
All questions about submissions should be emailed to hlc@fe.up.pt