arXiv:2505.07830v1 Announce Type: new
Abstract: A total of more than 3400 public shootings have occurred in the United States between 2016 and 2022. Among these, 25.1% of them took place in an educational institution, 29.4% at the workplace including office buildings, 19.6% in retail store locations, and 13.4% in restaurants and bars. During these critical scenarios, making the right decisions while evacuating can make the difference between life and death. However, emergency evacuation is intensely stressful, which along with the lack of verifiable real-time information may lead to fatal incorrect decisions. To tackle this problem, we developed a multi-route routing optimization algorithm that determines multiple optimal safe routes for each evacuee while accounting for available capacity along the route, thus reducing the threat of crowding and bottlenecking. Overall, our algorithm reduces the total casualties by 34.16% and 53.3%, compared to our previous routing algorithm without capacity constraints and an expert-advised routing strategy respectively. Further, our approach to reduce crowding resulted in an approximate 50% reduction in occupancy in key bottlenecking nodes compared to both of the other evacuation algorithms.
Expert Commentary: Multi-Disciplinary Approach to Emergency Evacuation
In the face of increasing public shootings in the United States, it is essential to develop effective strategies for emergency evacuation in high-risk locations such as educational institutions, workplaces, retail stores, and restaurants. The study mentioned in this article presents a novel multi-route routing optimization algorithm that not only determines multiple optimal safe routes for evacuees but also takes into account the available capacity along these routes, thus reducing the risk of overcrowding and bottlenecks.
This algorithm represents a significant advancement in the field of emergency management by combining principles from various disciplines such as computer science, operations research, and safety engineering. By integrating real-time information and capacity constraints into the decision-making process, the algorithm is able to provide tailored evacuation routes for each individual, ultimately leading to a substantial reduction in total casualties.
One of the key strengths of this approach is its ability to adapt to the dynamic nature of emergency situations, where unforeseen changes in the environment can impact the effectiveness of evacuation plans. By continuously optimizing routes based on updated information, the algorithm is able to respond in real-time to evolving threats and ensure the safety of evacuees.
Furthermore, the study highlights the importance of considering human behavior and psychology in the design of evacuation strategies. By acknowledging the intense stress and uncertainty that individuals experience during emergencies, the algorithm aims to alleviate some of this burden by providing clear and efficient routes for evacuation.
Looking ahead, the multi-disciplinary nature of this research opens up new possibilities for improving emergency response systems in various settings. By harnessing the power of technology, data analytics, and human-centric design principles, we can continue to enhance the safety and security of our communities in the face of escalating threats.