Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analysing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modelling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatiotemporal traffic patterns, ultimately for modelling large-scale traffic dynamics, and long-term traffic forecasting. The authors attack this issue by utilising locality-preserving non-negative matrix factorisation (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. The authors have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network and a basis for potential long-term forecasting.
Statistical traffic state analysis in large-scale transportation networks using locality-preserving non-negative matrix factorisation
IET Intelligent Transport Systems ; 7 , 3 ; 283-295
2013-09-01
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
LPNMF projections , statistical analysis , traffic management , spatial patterns , data analysis , matrix algebra , large scale transportation networks , statistical traffic data analysis , locality preserving non negative matrix factorisation , road traffic , statistical traffic state analysis , spatiotemporal traffic pattern extraction , traffic control , pattern classification , temporal dynamics
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