Abstract
In the last decade, over two billion people have become victims of mass casualty incidents (MCIs). The success of the emergency response to such events depends heavily on efficient emergency vehicle transportation. During a MCI, the status of transportation pathways is constantly fluctuating, making it difficult to evaluate real-time traffic delays. Standard traffic delay software is not reliable in routing emergency resources to incident sites. Thus, the question of how can image recognition and deep-learning be used in real-time to aid emergency vehicles in MCI efforts emerges. The “You-Only-Look-Once” method is applied to provide accurate vehicle detection; a convex hull is implemented to conduct road detection; and simple supporting methods are used to describe traffic states. This combination yields a classification of traffic congestion based on defined parameter thresholds. The resulting output will ultimately guide a decision-maker or supplemental model to optimize emergency vehicle deployment.
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This research was sponsored by the NATO Science for Peace and Security Programme under grant SPS MYP G5700.
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Julson, R. et al. (2021). Object Detection Using Artificial Intelligence: Predicting Traffic Congestion to Improve Emergency Response to Mass Casualty Incidents. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_36
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DOI: https://doi.org/10.1007/978-3-030-80624-8_36
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