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.

Accessibility and usability have been key concerns in the design of computer interfaces through which users interact with applications and systems. Recently, chatbots have gained popularity with service providers for improvements in this area. In this paper, we present our experience in designing and implementing CAPRIO’s inclusive chatbot-based interface for pedestrian path recommendations. Our CAPRIO system provides inclusive usability by extracting user preferences in a non-intrusive dialog and using them to build a more accurate model for the user’s intent. It uses the Microsoft Bot Framework (MBF) and NLP modeling to support text and voice dialog.

In this demo paper, we present the new module of our HealthDist system that performs contact tracing in a privacy-preserving manner and considers the COVID-19 exposure risk. This is achieved by answering a new spatio-temporal query, dubbed ST-Aggregate Join, which calculates the COVID-19 exposure risk of an individual on their devices. It utilizes a special-purpose access structure to record the trajectories of users on their devices and optimize the ST-Aggregate Join processing. We demonstrate interactively using a smartphone application how our system can provide effective contact tracing within a university campus. We also illustrate how our new module is working through an intuitive web interface that shows the exposure risk of a person by coloring the trajectory of the infected person and the person(s) in high risk in a preloaded real dataset.

The COVID-19 pandemic poses new challenges in providing safe pedestrian navigation information that helps to reduce the risk of severe illness due to the highly contagious nature of the virus. In this paper, we present an innovative system, dubbed HealthDist, which utilizes the context (e.g., weather conditions), location (e.g., crowded areas) and user's preferences to support safe mobility. It consists of four modules that allow efficient contact tracing, social distancing, and isolation. HealthDist's modular design reduces the time and resources needed to provide accurate localization for measuring density in common spaces and measuring potential infection exposure, and recommend outdoor and indoor paths satisfying the user's preferences. HealthDist's initial deployment within a university campus demonstrated its capability to provide real time navigation information that reduces the COVID-19 exposure risk while at the same time satisfying the constraints defined by the user.

In this demo paper, we feature HealthDist, an innovative system that is an additional asset in the fight against the COVID-19 pandemic. HealthDist utilizes context (e.g., weather conditions), location (e.g., crowded areas), and user preferences to provide safe pedestrian paths which decrease the exposure to the virus causing COVID-19. Its modular design, consisting of four components, reduces the time and resources needed to provide accurate localization and indoor-outdoor path recommendations that satisfy the user's preferences. We demonstrate interactively using smartphones how HealthDist can provide real time navigation information within a university campus and illustrate the reduction of the COVID-19 exposure risk while satisfying the constraints defined by the user.Video: http://bit.ly/3bMicbs