The International Conference on Advances in Geographic Information Systems 2020 (ACM SIGSPATIAL 2020) will be held on November 3-6, 2020 at online conference. The following poster presentation will be delivered.
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.
Poster/Demo Session 1B (Wednesday, November 4, 2020, 02:30 PM – 04:00 PM PST (07:30 AM – 09:00 AM JST))
Soto Anno*, Kota Tsubouchi**, and Masamichi Shimosaka*: Supervised-CityProphet: Towards Accurate Anomalous Crowd Prediction.
(*Tokyo Institute of Technology, **Yahoo Japan Corporation)
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