| Title | Estimating and Leveraging Latent Social Demand Based on IoT sensors: An Empirical Study in a Large Public Park |
|---|---|
| ID_Doc | 43958 |
| Authors | Nagata, Y; Murai, D; Katayama, S; Urano, K; Aoki, S; Yonezawa, T; Kawaguchi, N |
| Title | Estimating and Leveraging Latent Social Demand Based on IoT sensors: An Empirical Study in a Large Public Park |
| Year | 2021 |
| Published | |
| Abstract | The continuously changing conditions of cities are now technically understandable in the information space through real-world sensing methods and analytical methods such as big data analysis and machine learning. On the other hand, it is currently difficult to estimate and present what the people in the city need (latent demand). This paper aims to solve latent demand by developing Latent Demand Resolver (LD-Resolver), the new latent demand estimation and exchanging system. LD-Resolver has two components, Latent Demand Extractor (LD-Extractor) and Latent Demand Exchanger (LD-Exchanger). LD-Extractor extracts a latent demand from various social conditions using IoT sensors, Web, and SNS. LD-Exchanger has a new structure to exchange a latent demand with an appropriate service supply. Finally, we developed the LD-Resolver and conducted a demonstration experiment at the Higashiyama Zoo and Botanical Garden to verify the method. As a result of the two-week experiment, the proposed method can be effectively used in actual facility operations. |
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