Forecasting spatio-temporal data is a challenging task in transportation scenarios involving agents. In this paper, we propose a statistical relational learning approach to cellular network traffic forecasting, that exploits spatial relationships between close cells in the network grid. The approach is based on Markov logic networks, a powerful framework that combines first-order logic and graphical models into a hybrid model capable of handling both uncertainty in data, and background knowledge of the problem. Experimental results conducted on a real-world data set show the potential of using such information. The proposed methodology can have a strong impact in mobility demand forecasting and in transportation applications.

Predict Cellular network traffic with markov logic / Lippi, M.; Mamei, M.; Zambonelli, F.. - 2129(2018), pp. 9-14. ((Intervento presentato al convegno 10th International Workshop on Agents in Traffic and Transportation, ATT 2018 tenutosi a swe nel 2018.

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