Skip to Main content Skip to Navigation
Journal articles

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

Abstract : 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.
Complete list of metadatas

Cited literature [32 references]  Display  Hide  Download

https://hal-mines-paristech.archives-ouvertes.fr/hal-01085971
Contributor : Fabien Moutarde <>
Submitted on : Friday, November 28, 2014 - 5:18:29 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Friday, April 14, 2017 - 8:10:50 PM

File

trafficDynamics-NTF_IJITS2014....
Files produced by the author(s)

Identifiers

Citation

Yufei Han, Fabien Moutarde. Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization. International Journal of Intelligent Transportation Systems Research, Springer Verlag, 2016, 14 (1), pp.36-49. ⟨10.1007/s13177-014-0099-7⟩. ⟨hal-01085971⟩

Share

Metrics

Record views

846

Files downloads

1213