In this work we present a forecasting method that can be used to predict crowd distribution across the city. Specifically, we analyze and forecast cellular network traffic and estimate crowd on such basis. Our forecasting model is based on a neural network combined with time series decomposition techniques. Our analysis shows that this approach can give interesting results in two directions. First, it creates a forecasting solution that fits all the variability in our dataset without having to create specific features and without complex search procedures for optimal parameters. Second, the method performs well, showing to be robust even in the presence of spikes in the data thus enabling better applications such as event management and detection of crowd gathering.
Forecasting Crowd Distribution in Smart Cities / Cecaj, A.; Lippi, M.; Mamei, M.; Zambonelli, F.. - 2020-:(2020), pp. 1-6. (Intervento presentato al convegno 17th IEEE International Conference on Sensing, Communication and Networking, SECON Workshops 2020 tenutosi a Como nel 2020) [10.1109/SECONWorkshops50264.2020.9149774].
Forecasting Crowd Distribution in Smart Cities
Cecaj A.;Lippi M.;Mamei M.;Zambonelli F.
2020
Abstract
In this work we present a forecasting method that can be used to predict crowd distribution across the city. Specifically, we analyze and forecast cellular network traffic and estimate crowd on such basis. Our forecasting model is based on a neural network combined with time series decomposition techniques. Our analysis shows that this approach can give interesting results in two directions. First, it creates a forecasting solution that fits all the variability in our dataset without having to create specific features and without complex search procedures for optimal parameters. Second, the method performs well, showing to be robust even in the presence of spikes in the data thus enabling better applications such as event management and detection of crowd gathering.File | Dimensione | Formato | |
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