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CALL FOR PAPERS
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Workshop on Machine Learning in Smart Mobility (MLSM)
To be held at IDEAL 2020
21st International Conference on Intelligent Data Engineering and Automated
Learning
November 4-6, 2020, Guimarães, Portugal
http://islab.di.uminho.pt/ideal2020/
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Submission Deadline: June 5, 2020
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SCOPE OF MLSM@IDEAL2020
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The workshop on Machine Learning in Smart Mobility 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 emphasise 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 emphasised.
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-fertilisation 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
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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 traveller 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.
PAPER SUBMISSION AND PUBLICATION
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Authors are invited to submit their manuscripts (in pdf format) written in
English by the deadline via the Easychair online submission system (
https://easychair.org/conferences/?conf=ideal2020). Papers should be within
8 pages but must not exceed 12 pages, and must comply with the format of
Springer LNCS/LNAI Proceedings (see www.springer.com/lncs).
All accepted papers will be included in the conference proceedings and will
be indexed accordingly. At least one of the co-authors of the accepted
papers is required to register at the conference and ensure their
presentations during the workshop sessions.
IMPORTANT DATES
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* Submission Deadline: 5 June 2020
* Notification of Acceptance: 10 July 2020
* Camera-Ready Copy Due: 31 July 2020
* Early Registration: 31 July 2020
* Conference Presentation: 4-6 November 2020
ORGANISING COMMITTEE
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* Sara Ferreira (University of Porto, Portugal) [log in to unmask]
* Henrique Lopes Cardoso (University of Porto, Portugal) [log in to unmask]
* Rosaldo Rossetti (University of Porto, Portugal) [log in to unmask]
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