Traffic forecasting has recently become a crucial task in the area of intelligent transportation systems, and in particular in the development of traffic management and control. We focus on the simultaneous prediction of the congestion state at multiple lead times and at multiple nodes of a transport network, given historical and recent information. This is a highly relational task along the spatial and the temporal dimensions and we advocate the application of statistical relational learn- ing techniques. We formulate the task in the supervised learning from interpretations setting and use Markov logic networks with grounding- specific weights to perform collective classification. Experimental results on data obtained from the California Freeway Performance Measurement System (PeMS) show the advantages of the proposed solution, with respect to propositional classifiers. In particular, we obtained significant performance improvement at larger time leads.

Collective Traffic Forecasting / Marco Lippi; Matteo Bertini; Paolo Frasconi. - ELETTRONICO. - 6322:(2010), pp. 259-273. (Intervento presentato al convegno European Conference on Machine Learning (ECML) tenutosi a Barcelona nel 2010) [10.1007/978-3-642-15883-4_17].

Collective Traffic Forecasting

LIPPI, MARCO;
2010

Abstract

Traffic forecasting has recently become a crucial task in the area of intelligent transportation systems, and in particular in the development of traffic management and control. We focus on the simultaneous prediction of the congestion state at multiple lead times and at multiple nodes of a transport network, given historical and recent information. This is a highly relational task along the spatial and the temporal dimensions and we advocate the application of statistical relational learn- ing techniques. We formulate the task in the supervised learning from interpretations setting and use Markov logic networks with grounding- specific weights to perform collective classification. Experimental results on data obtained from the California Freeway Performance Measurement System (PeMS) show the advantages of the proposed solution, with respect to propositional classifiers. In particular, we obtained significant performance improvement at larger time leads.
2010
Machine Learning and Knowledge Discovery in Databases
259
273
Collective Traffic Forecasting / Marco Lippi; Matteo Bertini; Paolo Frasconi. - ELETTRONICO. - 6322:(2010), pp. 259-273. (Intervento presentato al convegno European Conference on Machine Learning (ECML) tenutosi a Barcelona nel 2010) [10.1007/978-3-642-15883-4_17].
Marco Lippi; Matteo Bertini; Paolo Frasconi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/394774
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