Recommending the Least Congested Indoor-Outdoor Paths without Ignoring Time
DocUID: 2023-002 Full Text:
PDF Author: Vasilis Ethan Sarris, Panos K. Chrysanthis, Constantinos Costa
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.
Keywords: Pedestrian Path Recommendation, Constraint-based Path Finding, Indoor-Outdoor Graphs Generation, Indoor Congestion, COVID-19, Crowd Diseases
Published In: Proceedings of the 18th International Symposium on Spatial and Temporal Data
ISBN: 9798400708992
Pages: 121-130
Place Published: Calgary, AB, Canada
Year Published: 2023
DOI: 10.1145/3609956.3609969
Project: CAPRIO Subject Area: Path Finding, Recommender Systems
Publication Type: Conference Paper
Sponsor: NIH R01HL159805