Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization

  • Yufei Han
  • Fabien Moutarde


In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the typical temporal patterns of the global traffic states and achieve long-term prediction of the large-scale traffic evolution in a unified data-mining framework. To this end, we formulate this joint task using regularized Non-negative Tensor Factorization, which has been shown to be a useful analysis tool for spatio-temporal data sequences. Clustering and prediction are performed based on the compact tensor factorization results. The validity of the proposed spatio-temporal traffic data analysis method is shown on experiments using simulated realistic traffic data.


Large-scale traffic dynamics Non-negative tensor factorization 



This work was supported by the grant ANR-08-SYSC-017 from the French National Research Agency. The authors specially thank Cyril Furtlehner and Jean-Marc Lasgouttes for helpful discussions and providing the benchmark database used in this article.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.RITS(previously IMARA), INRIARocquencourtFrance
  2. 2.Robotics Lab (CAOR)Mines ParisTech, PSL Research UniversityParisFrance

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