To provide mobility assistance for people with disabilities, pedestrian network data that includes accessibility attributes within public spaces is required. The freshness of data is necessary as public space conditions change frequently; however, its regular maintenance incurs substantial costs. In this paper, we propose a data platform for an accessible pedestrian network with a workflow similar to continuous software development. With this approach, the platform aims to allow local governments and citizens to maintain data in a collaborative and sustainable manner. This paper reports the initial phase of our project aiming at realizing such a platform; we designed and prototyped a platform based on this idea and conducted an actual field experiment in a specific area of Tokyo. Through this experiment, we demonstrated the feasibility of this idea and gained valuable insights for practical implementation.

Under the COVID-19 pandemic, it is necessary to balance social distancing and continuous economic activities. In this study, we report on our developed service that forecasts the congestion level of regional commercial facilities using point-of-sales (POS) statistics. POS statistics data were collected for over a year from 150 commercial facilities in Tokyo. Through the analysis of a total of over 100 million customers, we clarified the factors that affect congestion levels of commercial facilities in each ward of Tokyo. Based on this analysis, we developed a congestion forecast model that predicts future congestion levels from several factors such as a big event, business restrictions, and weather. We implemented a web service incorporating this model and published estimated congestion levels both on our website and a television program. The experimental results show that the model has a high prediction accuracy with a coefficient of determination greater than 0.95 on average, which implies that big data from POS has great potential for value creation under the pandemic.