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- W3108840981 abstract "Forecasting anomalies in urban areas is of great importance for the safety of people. In this paper, we propose Supervised-CityProphet (SCP), an anomaly score matching-based method towards accurate prediction of anomalous crowds. We re-formulate CityProphet as a regression model via data source association with mobility logs and transit search logs to leverage user's schedules and the actual number of visitors. We evaluate Supervised-CityProphet using the datasets of real mobility and transit search logs. Experimental results show that Supervised-CityProphet can predict anomalous crowds 1 week in advance more accurately than baselines." @default.
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- W3108840981 date "2020-11-03" @default.
- W3108840981 modified "2023-09-27" @default.
- W3108840981 title "Supervised-CityProphet" @default.
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- W3108840981 doi "https://doi.org/10.1145/3397536.3422219" @default.
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