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Smart city article details
| Title | Design And Implementation Of Street-Level Crowd Density Forecast Using Contact Tracing Applications |
|---|---|
| ID_Doc | 18616 |
| Authors | Bessho M.; Sakamura K. |
| Year | 2022 |
| Published | ISC2 2022 - 8th IEEE International Smart Cities Conference |
| DOI | http://dx.doi.org/10.1109/ISC255366.2022.9922572 |
| Abstract | 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. |
| Author Keywords | Contact Tracing; COVID-19; Crowd Density Forecast; Smart City; Social Distancing |
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