Congestion-aware Evacuation Routing using Augmented Reality Devices
This addresses safety and efficiency in emergency evacuations for building occupants, though it is an incremental improvement on existing path planning techniques.
The paper tackles indoor evacuation routing by developing a congestion-aware algorithm that uses real-time population density from AR devices to generate individualized routes, showing it is more computationally efficient and reduces overall congestion compared to classic methods.
We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model the congestion distribution inside a building. To efficiently search the evacuation route among all destinations, a variant of A* algorithm is devised to obtain the optimal solution in a single pass. In a series of simulated studies, we show that the proposed algorithm is more computationally optimized compared to classic path planning algorithms; it generates a more time-efficient evacuation route for each individual that minimizes the overall congestion. A complete system using AR devices is implemented for a pilot study in real-world environments, demonstrating the efficacy of the proposed approach.