Utilize Digital Transformation to Create Event Digital Twins for Marathons and Long-Distance Races

Keywords:
Digital Twin, Digital Twin in Sport, Marathon races, Marathon digital data, Marathon organization

Abstract:
In recent years, the world of long-distance races has experienced a digital revolution, leveraging technology to enhance various aspects of the event. Marathons and other long-distance races have embraced digital technologies to improve organization, streamline staff and volunteer coordination and management, optimize equipment and merchandise management, enhance medical infrastructure, implement efficient runner tracking systems, and address event security protocols and safety measures. The digital transformation of marathons and long-distance races has resulted in an exponential increase in event data, preparing for the creation of event digital twins. This paper explores how the larger digital footprint generated by these events can be leveraged to develop event digital twins, virtual representations that mirror the physical event environment. Based on this research, two digital twins were proposed - Event Organization DT and Race DT. The proposed models of digital twins include enhanced event planning, risk mitigation, staff and volunteers’ engagement, and post-event analysis, ultimately contributing to safer, more engaging, and well-organized race experiences.

CITATION:

IEEE format

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S. Malinović, Z. Konjović, M. Segedinac, “Utilize Digital Transformation to Create Event Digital Twins for Marathons and Long-Distance Races,” in Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2024, pp. 141-147. doi:10.15308/Sinteza-2024-141-147

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Malinović, S., Konjović, Z., Segedinac, M. (2024). Utilize Digital Transformation to Create Event Digital Twins for Marathons and Long-Distance Races. Paper presented at Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2024-141-147

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