Leveraging emerging data sources and Artificial Intelligence for improving transport sustainability

Leveraging emerging data sources and Artificial Intelligence for improving transport sustainability
Engineering for the Information Society and Sustainable Development
Antonio D. Masegosa, DeustoTech, Faculty of Engineering, University of Deusto, Spain/ Ikerbasque, Basque Foundation for Science, Spain

Andres R. Masegosa, Department of Mathematics, University of Almería, Spain
DeustoTech is a research institute at Faculty of Engineering of the University of Deusto for applied and basic research for the development of novel ICTs applications. Deustotech focuses its activity around TRLs 2-7 and articulates it into different applied fields: Health, Industry, Mobility, Energy and Society. It channels advanced ICT activity into business and society through research, development, innovation and knowledge transfer. https://deustotech.deusto.es/
We are looking for pre-doctoral researchers with a background in computer science, computer engineering, applied mathematics or related fields. The candidate must have good writing skills and preferably with some publications in journals and/or conferences related to these topics.

Good programming skills and experience in some of the next programming languages is also required: Python, Keras, PyTorch, TensorFlow, R, etc. Researchers with experience in the application and development of metaheuristics, machine learning, deep learning or probabilistic graphical models will be especially welcome. Experience in the fields of intelligent transportation systems, travel behaviour analysis or transport planning will be also welcome.

Fluency in English is expected, along with good communication and presentation skills. Moreover, you should be curious, innovative, have a proactive attitude, and be a good team player.
  • Information Sciences and Engineering (ENG)
Transport is a fundamental sector for the European economy. At EU level, it covers a fairly complex network that includes around 1.2 million private and public companies, employing around 10.5 million people. This is why it is a crucial sector of the EU economy: in 2015, it accounted for about 9 % of total gross added value, 9 % of total employment, and 17.2 % of total EU exports of services were transport-related. However, transport also generates negative social effects, such as accidents, GHGs, air pollution, noise and environmental effects. Overall, external costs due to transport have been estimated at around 4% of GDP in 2011.

Although transport at EU level presents important challenges to be addressed, technological advances in areas such as ICTs or energy storage are revolutionising the transport and mobility of both people and goods and opening up new areas of opportunity. Two of the main areas of opportunity are the high availability of transport data nowadays and the appearance of emerging mobility solutions (electric vehicles, autonomous vehicles, micro-mobility, etc.).

This research aims at addressing the previous challenges mentioned by developing novel Artificial Intelligence techniques that will make it possible to exploit the areas of opportunity described before. Within the wide range of AI methods that can be found today, in this project we will mainly focus on Machine Learning methods and optimisation techniques based on Metaheuristics.

EXCELLENCE OF THE HOST RESEARCH UNIT

Publications from Antonio D. Masegosa:

T. Bogaerts, A. D. Masegosa, J. S Angarita-Zapata, E. Onieva, P. Hellinckx, “A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data”, Transportation Research Part C: Emerging Technologies, 112: 62-77, 2020. [Q1]
J. S. Angarita-Zapata, A. D. Masegosa and I. Triguero, «A Taxonomy of Traffic Forecasting Regression Problems from a Supervised Learning Perspective,» IEEE Access, 7: 68185-68205, 2019. [Q1]
P. Lopez-Garcia, A. D. Masegosa, E. Osaba, E. Onieva, A. Perallos, Ensemble Classification for Imbalanced Data Based on Feature Space Partitioning and Hybrid Metaheuristics, Applied Intelligence 49(8): 2807-2822, 2019. [Q2]
J. F. Calderín, A. D. Masegosa, D. A. Pelta, An algorithm portfolio for the dynamic maximal covering location problem. Memetic Computing 9(2): 141–151, 2017 [Q2]
P. Lopez-Garcia, E. Onieva, E. Osaba, A. D. Masegosa, A. Perallos, A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy, IEEE Transactions on Intelligent Transportation Systems 17(2): 557-569, 2016 [Q1]

Publications from Andres R. Masegosa:

Masegosa, A. R., Lorenzen, S. S., Igel, C., & Seldin, Y. Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. In Advances in neural information processing systems 2020. [Core A*]
Masegosa, A. R. Learning under Model Misspecification: Applications to Variational and Ensemble methods. in Advances in neural information processing systems 2020. [Core A*]
Cózar, J., Cabañas, R., Salmerón, A., & Masegosa, A. R.. Inferpy: Probabilistic modeling with deep neural networks made easy. Neurocomputing, 415, 408-410. 2020. [Q1]
Masegosa, A. R., Nielsen, T. D., Langseth, H., Ramos-López, D., Salmerón, A., & Madsen, A. L. Bayesian models of data streams with hierarchical power priors. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2334-2343. 2017. [Core A*]
Masegosa, A. R. Stochastic discriminative EM. In Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence. p. 573-582. 2014. [Core A*]
Antonio D. Masegosa:

SENATOR: Smart Network Operator Platform enabling Shared, Integrated and more Sustainable Urban Freight Logistics. EU H2020. Coordinator: Correos. Total budget: 3.999.746,25. Budget host unit: 638.851,48 €. Duration: Sept 2020 – August 2024.
MOMENTUM: Modelling Emerging Transport Solutions for Urban Mobility. EU H2020. Coordinator: EMT Madrid S.A. Total budget: 2,927,875€. Budget host unit: 204,000€. Duration: June 2019 – May 2022.
LOGISTAR: Enhanced data management techniques for real time logistics planning and scheduling. EU H2020, Total budget: 4.997.548,75€. Budget host unit: 811.000€. June 2018 – May 2021,
TIMON: Enhanced real-time services for optimized multimodal mobility relying on cooperative networks and open data. EU H2020, Total budget: 5.605.213 €, Budget host unit: 943.750€, June 2015 – November 2018.

Andres R. Masegosa:

Explainable Machine Learning: A probabilisic approach. Spanish Ministry of Science and Innovation [PID2019-106758GB-C32]. Funds: 62.000 €. June 2020 – May 2023. Role: Principal Investigator.
Infer.java: A probabilistic programming language for the development of intelligent systems from big data. Spanish Ministry of Economy and Competitiveness [TIN2015-74368-JIN]. Funds: 172.000 €. January 2017 – December 2019. Role: Principal Investigator.
AMIDST: Analysis of massive data streams. EU FP7 programme [Grant Agreement – 619209], Funding consortium: 2.762.000 €, Funding group: 324.132 €. Januray 2014-December 2016

INTERDISCIPLINARY COLLABORATION

The research posed in this project is located on the intersection among two knowledge areas, Intelligent Transportation Systems and Artificial Intelligence. Below, we briefly describe these areas:

– Intelligent Transport Systems: According to the EU Directive 2010/40/EU, Intelligent Transport Systems (ITSs) are advanced applications which without embodying intelligence as such aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated and ‘smarter’ use of transport networks. ITSs integrate telecommunications, electronics and information technologies with transport engineering.

– Artificial Intelligence: This discipline can be seen as a group of methodologies that aim at performing tasks that are usually assumed for humans as Natural Language Processing, Speech Recognition, Reasoning, etc. In this project, the research will mainly focus on Machine Learning (a subarea of Artificial Intelligence that has experienced great advances in the last decade, and it is the technology behind the main breakthroughs in Artificial Intelligence) and on Metaheuristics (a category of methods that are considered to be the state-of-the-art in a wide variety of transport optimization problems in areas as supply chain, facility planning, etc.).
This thesis is planned to be co-directed by the two scientists in charge, Dr. Antonio D. Masegosa from the University of Deusto and Dr. Andrés R. Masegosa from the University of Almería.
The two main disciplines that combine this proposal are Intelligent Transportation Systems and Artificial Intelligence.

The background knowledge related to Artificial Intelligence will be mainly provided by the two scientists in charge, Antonio D. Masegosa and Andres R. Masegosa, for metaheuristics and machine learning respectively. Dr. Antonio D. Masegosa , awarded as IKERBASQUE Research Fellow, has a strong experience on metaheuristics for optimization. During this period he has published 19 JCR indexed papers and more than 20 articles in both national and international conferences. Dr. Andrés R. Masegosa has also a strong experience on machine learning methods. He has published 25 JCR indexed papers and 8 articles in top international conferences in machine learning. He is also the principal investigator of two national research proyects.

The required expertise about Intelligent Transport Systems will be also provided in part by Dr. Antonio D. Masegosa, since he also counts with a strong experience in the application of AI techniques to this area. Furthermore, this project will count with the support of other well-known researcher in DeustoTech. Concretely, Dr. Enrique Onieva who has extensive experience in this field where he accumulates around 50 papers in JCR journals.

Furthermore, the interdisciplinarity will be also strengthened through the collaboration in on-going H2020 projects as LOGISTAR, MOMENTUM or SENATOR.

Aligned with the 2030 Agenda, the Horizon Europe missions and goals and the Basque Country Smart Specialisation Strategy, DEUSTO research blends competitiveness, innovation and technology to tackle challenges for communities, companies and public bodies in the region. Ageing and Wellbeing; Cultural and Creative Industries and Cities; Gender; Social Justice and Inclusion and Strengthening Participation are the focus of the 5 Interdisciplinary Research Platforms. Besides, advanced research units contribute to generate knowledge and innovative solutions in the fields of efficient and clean Energy, Industry 4.0 (advanced manufacturing, data analytics…), Health and Territory.
https://www.deusto.es/cs/Satellite/deustoresearch/es/inicio/plataformas-interdisciplinares-de-investigacion

INTERNATIONAL COLLABORATION

The research proposed in this project is aligned with the H2020 societal challenge on Smart, Green and Integrated Transport, especially with two of its key objectives:

Better mobility, less congestion, more safety and security. As will be explained below, the research done in this thesis could contribute to reducing congestion and road traffic accidents.

Global leadership for the European transport industry. The research proposed here would help to improve the design, planning and reliability of European distribution networks, and therefore to the global leadership of the European transport industry.

Furthermore, it is also well aligned with the societal challenge on Energy and Transport of the upcoming European Framework Programme, Horizon Europe; and with the United Nation’s Sustainable Goals 11th and 13th on Sustainable Cities and Communities, and Climate Action.
A third international co-director is under exploration at this moement with colleagues from the University of Nottingham (Dr. Isaac Triguero), CERTH-HIT institute from Greece (Dr. Josep María Salanova), University of Aalborg from Denmark (Dr. Thomas D. Nielsen), Norwegian University of Science and Technology (Dr. Helge Langseth).

We plan the next mechanisms to successfully implement the international co-direction:

– Establish a thesis project agreed among the co-directors and the researcher. This project will set the objectives of the thesis, the methodology and the work plan for the three years period of the thesis.

– Weekly audio or videoconferences to check the progress of the student and to establish the next steps to follow.

– Personal meetings every 6 months approximately in conferences or other events where both the co-directors and the student will attend.

– At least one research stay, from three to four months long, at the institution of the international co-director.

INTERSECTORAL COLLABORATION

Deusto Research works closely with more than 1250 organisations (public administration, business, non-profit organisations, education and training institutions, technology centres, etc.) in areas such as health, urban development, gender, social justice and inclusion, democratic participation, regional competitiveness and energy. Since 2014 DEUSTO has been involved in 89 international research projects and worked with more than 770 partner organisations. From data collection to the implementation and piloting phases, stakeholders have an active role throughout the research project life cycle.
https://www.deusto.es/cs/Satellite/deustoresearch/es/inicio/transferencia-3/colaboracion-con-agentes-externos

IMPACT

These advances with respect to the state-of-the-art foreseen in this project would have a high impact on transport. Some of the main impacts are the following:

– Improve design, planning and reliability of urban distribution networks. Travel times among hubs, customers, factories, providers, warehouses, etc. are pivotal in the design and planning of last mile distribution networks. The research posed here will allow us to carry out these tasks using more precise and rich information about travel times, which would result in higher reliability.

– Reduce traffic congestion. Routes passing through road stretches with a higher probability of congestion are usually subject to lower reliability in travel times. If users are better informed about these facts, they will tend to avoid these routes.

– Reduce traffic accidents. The stress motivated by bad travel planning or unexpected road conditions is an important cause of road accidents. The systems developed in this thesis would help the users to do better planning and to be aware of possible delays.

– Reduce urban pollution. One of the aspects foreseen in this project is the use of deep learning to predict urban pollution, which will contribute to alert in advance urban authorities, helping them to take the best possible actions to mitigate high-pollution scenarios.

Deusto Research pursues societal impact that goes beyond academia focusing on the transformation of today’s society, leading to fairer and more diverse societies, where inclusive social development and welfare are enhanced.
https://www.deusto.es/cs/Satellite/deustoresearch/en/home/dissemination-and-transfer

INNOVATION

The main innovative aspects of the hosting offer are the following:

– Analysis of Open Data and Volunteered Geographic Information to take advantage of this publicly available data and provide valuable insights to citizens.

– Use of new Big Data technologies to deal with the vast amount of data available nowadays in cities, particularly due to the increasing digitalization of cities, thanks to the popularity of the smart city concept.

– Development of novel Machine Learning techniques capable of dealing simultaneously with information of a diverse nature as images, trajectory data, text, etc.

– Development of novel Machine Learning techniques to model complex mobility patterns, which allow to compute the effect of interventions and, in consequence, the design of better policies.

– Development of novel Metaheuristic techniques based on computational graph paradigms, such as TensorFlow or Pytorch, in order to exploit modern hardware accelerators such as GPUs, TPUs, etc.

– Development of novel Metaheuristics techniques able to exploit predictive models for solving transport optimization problems under highly uncertainty settings.

– Analysis of new forms of mobility as car-sharing, car-pooling, hailing services, bike-sharing services, etc. in order to help the cities understand what could be its impact on the mobility patterns of citizens.

DEUSTO research internationalisation strategy pursues innovation through the participation in international research and innovation initiatives (H2020 projects, Innovation Radar)
https://www.deusto.es/cs/Satellite/deustoresearch/en/home/transfer-of-knowledge-0/innovation-radar

INCLUSION

Inclusion is an underlying principle of the 6i Dirs project. The leadership vocation of the University of Deusto goes hand in hand with its aspiration to excellence and quality and a firm commitment to social justice and inclusion. The Interdisciplinary Platform on Social Justice and Inclusion articulates interdisciplinary collaborations and channel research efforts for contributing to social justice and more inclusive and fairer societies.
https://www.deusto.es/cs/Satellite/deustoresearch/es/inicio/plataformas-interdisciplinares-de-investigacion/deusto-social-justice-and-inclusion/the-platform
Deusto recognises gender equality as a key driver for sustainable development and inclusive growth across regions, and believes that equality of academic opportunity for women is key. The Interdisciplinary Research Platform on Gender is a dynamic vehicle that teams up experts from different areas of knowledge with the dual purpose of fostering collaboration and integrating existing expertise to address society’s emerging challenges on gender issues. (https://www.deusto.es/cs/Satellite/deustoresearch/es/inicio/plataformas-interdisciplinares-de-investigacion/deusto-gender/the-university-of-deusto)
Moreover, to seek real inclusion for people with specific support needs, the 6i Dirs project ensures equal rights and opportunities with respect to access to the programme and the acquisition of the skills expected to achieve the PhD and enhance their career development and future employability. All vacancies on the Euraxess EU portal bear the Science4refugee logo to encourage refugees to apply for them. Furthermore, DEUSTO is a partner of the Scholars At Risk Network (SAR), which offers safety to scholars facing grave threats.