DLOREAN: Dynamic Location-aware Reconstruction of multiway Networks
Paper in proceedings, 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

Author

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Vinay Jethava

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

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

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Probability Theory and Statistics

Computer Science

DOI

10.1109/ICDMW.2013.57