Collective traffic forecasting

Authors:
Marco Lippi;Matteo Bertini;Paolo Frasconi
Affiliations:
Dipartimento Sistemi e Informatica, Università degli Studi di Firenze;Dipartimento Sistemi e Informatica, Università degli Studi di Firenze;Dipartimento Sistemi e Informatica, Università degli Studi di Firenze
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Year:
2010

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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 learning techniques. We formulate the task in the supervised learning from interpretations setting and use Markov logic networks with groundings-pecific 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.