Name
Title | ||
|---|---|---|
Estimating and Leveraging Latent Social Demand Based on IoT sensors: An Empirical Study in a Large Public Park |
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 R... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ICMU50196.2021.9638789 | 2021 Thirteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU) |
Keywords | DocType | ISBN |
Text mining,Maximum likelihood estimation,Costs,Urban areas,Machine learning,Big Data,Sensors | Conference | 978-4-907626-48-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiteru Nagata | 1 | 0 | 0.34 |
| Daichi Murai | 2 | 0 | 0.34 |
| Shin Katayama | 3 | 0 | 1.01 |
| Kenta Urano | 4 | 0 | 0.68 |
| Shunsuke Aoki | 5 | 0 | 1.69 |
| Takuro Yonezawa | 6 | 0 | 0.68 |
| Nobuo Kawaguchi | 7 | 0 | 0.68 |