Publications
論文誌 (国際) CityOutlook+: Early Crowd Dynamics Forecast through Unbiased Regression with Importance-based Synthetic Oversampling
Soto Anno (Tokyo Institute of Technology), Kota Tsubouchi, Masamichi Shimosaka (Tokyo Institute of Technology)
IEEE Pervasive Computing
2023.11.7
This article studies crowd dynamics forecast one week in advance to detect irregular urban events, which plays an important role in infection prevention and crowd control. Previous approaches have failed to deal with the scarcity of anomalous events, resulting in a large model bias, and could not quantify the number of visitors in anomalous crowding. We proposed an unbiased regression using importance weighting (IW), called CityOutlook [1], and successfully reduced the model bias and showed promising results. However, the straightforward weighting of the scarce data risks leading to the instability of the model due to the increase in model variance. To address this issue, we propose a non-trivial extension of our prior work called CityOutlook+ that realizes unbiased and less-variant regression by performing synthetic minority oversampling based on the importance. We evaluate CityOutlook+ using real datasets and demonstrate the superiority of our model to CityOutlook and state-of-the-art approaches.
Paper :
CityOutlook+: Early Crowd Dynamics Forecast through Unbiased Regression with Importance-based Synthetic Oversampling
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