DLOREAN: Dynamic Location-aware Reconstruction of multiway Networks
Paper i proceeding, 2013

This paper presents a method for learning time-varying higher-order interactions based on node observations, with application to short-term traffic forecasting based on traffic flow sensor measurements. We incorporate domain knowledge into the design of a new damped periodic kernel which lever- ages traffic flow patterns towards better structure learning. We introduce location-based regularization for learning models with desirable geographical properties (short-range or long-range interactions). We show using experiments on synthetic and real data, that our approach performs better than static methods for reconstruction of multiway interactions, as well as time-varying methods which recover only pair-wise interactions. Further, we show on real traffic data that our model is useful for short-term traffic forecasting, improving over state-of-the-art.

spatio-temporal

kernel-reweighting

higher-order

hierarchical inclusion

structure learning

Traffic prediction

Författare

Fredrik Johansson

Chalmers, Data- och informationsteknik, Datavetenskap

Vinay Jethava

Chalmers, Data- och informationsteknik, Datavetenskap

Devdatt Dubhashi

Chalmers, Data- och informationsteknik, Datavetenskap

2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013; Dallas, TX; United States; 7 December 2013 through 10 December 2013

1012-1019 6754033

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

DOI

10.1109/ICDMW.2013.57