Recommending the Least Congested Indoor-Outdoor Paths without Ignoring Time

Abstract

The exposure to viral airborne diseases is higher in crowded and congested spaces, the COVID-19 pandemic has revealed the need of pedestrian recommendation systems that can recommend less congested paths which minimize exposure to infectious crowd diseases in general. In this paper, we introduce ASTRO-C, an extension of previous work ASTRO, which optimizes for minimum congestion. To our knowledge, ASTRO-C is the only solution to this problem of constraint-satisfying, indoor-outdoor, congestion-based path finding. Our experimental evaluation using randomly generated Indoor-Outdoor graphs with varying constraints matching various real-world scenarios, show that ASTRO-C is able to recommend paths with, on average a 0.62X reduction in average congestion, while on average, total travel time increases by 1.06X and never exceeds 1.10X compared to ASTRO.