Pedestrian-focused modeling of urban environments is difficult due to a lack of publicly available realistic datasets, and the time and labor-intensive manual processes required to make one, creating barriers to effective evaluation and analysis. In this paper, we introduce GIO.G, a Generator for Indoor-Outdoor Graphs, designed to address these challenges and enhance pedestrian-focused simulation in urban environments. GIO.G offers configurable parameters such as building characteristics, urban density, and foot traffic congestion levels, enabling users to explore a wide range of scenarios with precision and scalability. Through a series of scenarios, we highlight GIO.G’s unique features and showcase GIO.G’s versatility and effectiveness in generating realistic Indoor-Outdoor Graphs.

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.

CAPRIO is an indoor-outdoor pedestrian path rec-ommendation system that optimizes for shortest distance. Its path-finding algorithm, ASTRO, takes into account a set of user-provided congestion constraints and as such can recommend paths that can reduce the risk of COVID-19 exposure. In this paper, we extend ASTRO to consider the changes on congestion when providing path recommendations for overlapping requests. Our new algorithm, called ASTRO-K, can provide K alternative paths that satisfy the congestion constraints of all the path requests within a short time-window. Our experimental eval-uation is conducted using two real-world datasets and shows that ASTRO-K can reduce the total average congestion of the recommended paths up to 4.5X with the trade-off of up to 7% increased total path time.