AI in railways | Resilient transport networks
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My focus is on advanced data-driven mathematical models and algorithms for improving railway and public transport planning and traffic management, evaluating operation performances and appraisal of infrastructure projects. Currently, my main interest is resilient public transport systems, from determining critical system elements and designing recovery strategies, to making the system better prepared for future disruptions and disasters.
Please refer to the following RG project: https://www.researchgate.net/project/RAILS-Roadmaps-for-AI-integration-in-the-raiL-Sector-Horizon-2020-Shift2Rail-JU
Critical infrastructure networks, such as transport and power networks, are essential for the functioning of a society and economy. On their regular functioning depend millions of commuters and travellers worldwide every day. The rising transport demand increases the congestion in railway networks and thus they become more interdependent and more complex to operate. Therefore, urban mobility becomes more fragile to unexpected changes to the networks. Such events may range from disturbances (daily variations in operations), disruptions (due to failures of infrastructure, vehicles, engineering works and adverse weather conditions such as rain, snow storm, wind), to disasters (earthquakes, floods, and hurricanes). It has been identified that building a resilient railway system is not only about concrete defences - it is just as much about working practices, since gaps, shortcomings and difficulties in railway operations are often a result of bad planning and preparation and lack of operational buffers. More quantitative research is needed in these directions, which needs to gain more attention in the future research. And thus, this project aims to fulfil this significant gap.