Investigating Bluetooth Low Energy Advertisements in Urban Public Space for Street-level Crowd Density Sensing

Technologies for sensing crowd density have a potential to make our society smarter, and such technologies have been used to help social distancing in the context of COVID-19 pandemic. We have developed a method to sense and forecast street-level crowd density by observing public Bluetooth Low Energy (BLE) advertisements from popular contact tracing applications in Japan. We have deployed our methods in several locations in Tokyo and published the estimated street-level crowd density level on our website as well as a television program. In this paper, we report the status of our project, focusing on the result of experiments to verify the potential of our method after the contact tracing applications stop working. Through an experiment in an urban public space in Tokyo, we have shown that BLE advertisements are almost occupied with contact tracing applications and manufacture specific data from a few companies. In addition, by monitoring different types of BLE advertisements in several locations in Japan, we have clarified that those containing manufacture specific data with a certain company identifier have almost the same trend as those from contact tracing applications, with the average correlation coefficient of 0.990. © 2022 TRON Forum.

Store Congestion Forecast under the Pandemic using Point of Sales Statistics

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. © 2022 IEEE.

Design and Implementation of Street-level Crowd Density Forecast using Contact Tracing Applications

Social distancing plays an important role in the control of the spread of infectious diseases. This study proposes a service that forecasts street-level crowd density in the near future. We collected street-level crowd density levels for months during the COVID-19 pandemic by observing public Bluetooth Low Energy advertisements from popular contact tracing applications. We then designed a model to predict crowd density level from other factors such as calendars, weather, and recent trends of crowd density level using Random Forest Regressor. Based on the model, we implemented a crowd density forecast service by incorporating an external weather forecast service, and we published the forecast on our website and a Japanese television program. The experimental results indicate that the model can predict the crowd density for the following week with a coefficient of determination of 0.85 or higher on average, which demonstrates that a practical crowd density forecast can be realized with our method. © 2022 IEEE.

Sensing Street-level Crowd Density by Observing Public Bluetooth Low Energy Advertisements from Contact Tracing Applications

Smart cities in current and future pandemics are expected to implement features that ensure social distancing in order to prevent the spread of infection. Technologies for sensing street-level crowd density are considered helpful in avoiding crowded situations;however, street-level crowd density is difficult to sense effectively using existing techniques. In this paper, we propose a method for sensing street-level crowd density with good accuracy by observing public Bluetooth low energy (BLE) advertisements from popular contact tracing applications. We conducted an experiment in major shopping districts in Tokyo by deploying our developed sensing devices and demonstrated that our method can estimate the street-level crowd density in 30-min intervals with high accuracy, compared to manually counting the number of pedestrians. Using this method, we have begun to publish the street-level crowd density on our website and a news program on Japanese television. Moreover, through long-term monitoring of the collected street-level crowd density data, we analyzed the factors that affect crowd density and constructed a model to predict crowd density from other factors with a coefficient of determination of 0.9 or higher using support vector regression. © 2021 IEEE.