André Maia Pereira is originally from Curitiba, Brazil. He holds a degree in Transportation and Vehicle Engineering at Budapest University of Technology and Economics, and received a M.S. double degree in Intelligent Transport Systems, from Linkoping University and the Czech Technical University (CTU). His participation on the European project Managing Automated Vehicles Enhances Network (MAVEN) inspired him to pursue a Ph.D. research on Transportation Systems and Technology at CTU. André is part of the Czech Technical University (CTU) team participating in nuMIDAS.
1. Please tell us about the Faculty of Transportation Sciences of the Czech Technical University in Prague and how your role in your team plays in nuMIDAS?
The Czech Technical University in Prague (CTU) was established in 1707 and is one of the oldest institutes of technology in Europe with 8 faculties. At the Faculty of Transportation Sciences, we have project-focused education in four specific areas: Intelligent Transportation Systems (ITS), air transportation and pilot training, logistics and transportation management, and Smart Cities. Since 2021 I am a member of the Laboratory of Applied Mathematics in Transport and Logistics (LAMbDA), where we support partners and students on modelling, optimisation, and analysis. The main goal of the LAMbDA laboratory is to create a competence centre that will build knowledge in the field of mathematical modelling and the application of mathematical tools for transport and logistics tasks. I have been exploring Cooperative-ITS solutions, data analysis, traffic modelling and simulation, and integration of cooperative automated vehicles into the roadside infrastructure.
In nuMIDAS, I cooperate on the research of the literature as well as on the suggestion of methodologies, models and tools. I also have an active role in documentation and proposing new concepts. In this way, I aim to collaborate on understanding the current situation in the mobility sector, and solving its problems by imagining the future and ideal situations, all the while considering constrained resources and other limitations.
2. What are the main challenges and opportunities you currently see in mobility regarding the use of data?
Innovations in the mobility sector are happening at a fast pace, and the emergence of digitalisation not only brings new ways in which data are being generated, collected, stored, and exchanged, but also supports new types of policies, services, and solutions. In general, digital platforms facilitate the flow of information and improve the usage of resources, but that can lead to a disruption in the sector and also in a change of citizen’s behaviour.
As main challenges, we can point out: the interoperability of systems, privacy, cyber-security, open data and open platforms, proper infrastructure, and slower legislation against faster innovation. In order to build an efficient integrated urban transport system, cities will have to adjust their policies and legislation while services adapt their business models and processes. However, we can also see opportunities in the sector, such as: data standardisation, promotion of open data and open platforms, and the development of tools to support sustainability as well as urban space management and impact assessment. Industry 4.0 and artificial intelligence offer new opportunities that are usually not explored by existing mobility tools.
3. As nuMIDAS WP2 leaders, what are the biggest lessons and insights you have obtained from the project so far?
I believe that the core of our acquired knowledge is that in order to help stakeholders achieving their goals, we must shift from the tools-oriented perspective to a service-oriented perspective. Usually, cities have common problems but caused by different reasons, which may be influenced by common factors. Instead of having a tool (or a set of tools) that may be powerful on their capabilities, the final goal is always to solve real world challenges. That means establishing methodologies that solve specific real-world problems by having as inputs a well-defined set of causes, influencing factors, and a set of meaningful KPIs for the decision-making process.