Akronim: AVPT
Vodja: dr. László Hajdu
Članica: Univerza na Primorskem, Fakulteta za matematiko, naravoslovje in informacijske tehnologije
Raziskovalno področje: 1.07. Računalniško intenzivne metode in aplikacije
Vir financiranja: Rektorjev sklad UP
Trajanje projekta: 1. 10. 2022 – 30. 9. 2024
Opis (ang):
Recent global events such as coronavirus have highlighted the vulnerability of our society in a case of a worldwide pandemic. It is clear that next to its direct effects, the SARS-CoV-2 also caused a substantial protracted economic crisis which entails more and more long-term adverse effects such as high inflation and collapsing supply chains in many different areas. In these situations, diffusion models are often used to simulate the spread of diseases in the population helping the policymakers make the right decisions and minimize the number of infected people. However, using these immediate restrictive provisions is not a long-term solution, and without changing our daily infrastructure such as public transportation, the policymakers can not adapt to this fast-changing environment we experience nowadays. Moreover, even if the SARS-CoV-2 disappears in the following years and we will be able to solve the arising economic problems, the chance of a possible more severe future pandemic is higher than ever due to globalization , uncontrolled deforestation , and global warming. Therefore it is essential to learn from today’s events and overthink the possible preventive scenarios to minimize the adverse effects of a possible future pandemic. The public transportation is one of the most important part of our human infrastructure which supports many pillars of the global economy therefore it is crucial to plan a robust and sustainable system even in a case of a pandemic.
This project aims to provide an innovative solution that supports decision making process in public transportation planning considering the possible epidemiological risks and keeping the efficiency of the original system. We integrate the toolset of epidemiological modeling more precisely compartmental models, temporal networks, agent-based models and optimization into an intelligent system that can support the decision-making process of public transport companies in a case of a global pandemic. The project aims to introduce, simulate and solve real world use case from an actual city as well as to show the broad usage of the models using randomly generated and simulated environments. In our real world use case we explore the possible epidemiological scenarios and the effect of different interventional strategies in the case of public transportation infrastructure.
