Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Intelligent Transportation Systems Research
Published in: International Journal of Intelligent Transportation Systems Research 1/2016

15-08-2014

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

Authors: Yufei Han, Fabien Moutarde

Published in: International Journal of Intelligent Transportation Systems Research | Issue 1/2016

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Semantically Enriched Multi-Modal Routing
next article Risk of Traffic Incident Delay in Routing and Scheduling of Hazardous Materials

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information. 

Order your 30-days-trial for free and without any commitment.

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
Herring, R., Hofleitner, A., Amin, S., Nasr, T., Khalek, A., Abbeel, P., Bayen, A.: ‘Using mobile phones to forecast arterial traffic through statistical learning’. Proc. 89th Transportation Research Board Annual Meeting, Washington D.C. (USA) (2010)
2.
Kinuta, Y., Kitamura, S., Nakamura, T., Makimura, K., Takahashi, M., Morokawa, T.: Examination of the applicability of probe car data to assessment of the effects of link-safety projects. Int. J. Intell. Transp. Syst. Res. 8(2), 67–76 (2010)
3.
Work, D., Blandin, S., Tossavainen, O.-P, Piccoli, B., Bayen, A.: A Distributed Highway Velocity Model forTraffic State Reconstruction. Appl. Math. Res. Express (AMRX) 1, 1–35 (2010)
4.
Thiagarajan, A., Sivalingam, L., LaCurts, K., Toledo, S., Eriksson, J., Madden, S., Balakrishnan, H.: ‘VTrack: Accurate, energy-aware traffic delay estimation using mobile phones’. Proc. 7th ACM Conf. Embedded Networked Sensor Systems (SenSys), Berkeley CA (USA), 85–98 (2009)
5.
Krause, A., Horvitz, E., Kansal, A., Zhao, F.: ‘Toward community sensing’. Proc. of Int. Conf. on Information Processing in Sensor Networks (IPSN), St Louis (USA), 481–492 (2008)
6.
Liu, H., Ma, W.: A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials. Transp. Res. C 17(1), 11–26 (2009)CrossRef
7.
Jaeger, A., Bibmeyer, N., Stubing, H., Huss, A.S.: A novel framework for efficient mobility data verification in vehicular Ad-hoc networks. Int. J. Intell. Transp. Syst. Res. 10(1), 11–21 (2012)
8.
Vanajakshi, L., Subramanian, S.C., Sivanandan, R.: Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses. IET Intell. Transp. Syst. 3(1), 1–9 (2009)CrossRef
9.
Chowdhury, D., Santen, L., Schadschneider, A.: Statistical physics of vehicular traffic and some related systems. Phys. Rep. 329, 199–329 (2000)MathSciNetCrossRef
10.
Herty, M., Klar, A., Pareschi, L.: General kinetic models for vehicular traffic flow and Monte Carlo methods’. Comput. Methods Appl. Math. 5(2), 155–169 (2005)MATHMathSciNet
11.
Rakha, H.: Validation of Van Aerde’s Simplified Steady-state Car-following and Traffic Stream Model. Transp. Lett. : Int. J. Transp. Res. 1(3), 227–244 (2009)CrossRef
12.
Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. 2, 2221–2229 (1992)
13.
Blandin, S., Work, D., Goatin, P., Piccoli, B., and Bayen, A.: ‘A general phase transition model for vehicular traffic’. SIAM J. Appl. Math., to appear (2011)
14.
Wang, Y., Papageorgiou, M.: Real-time freeway traffic state estimation based on extended kalman filter: a general approach. Transp. Res. Part B 39, 141–167 (2005)CrossRef
15.
Statthopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and predicting. Transp. Res. C 11, 121–135 (2003)CrossRef
16.
Helbing, D., Rarita, L., D’Apice, C., Piccoli, B.: Sensitivity analysis of permeability parameters for flows on Barcelona networks. J. Differ. Equat. 249(12), 3110–3131 (2010)MATHMathSciNetCrossRef
17.
Helbing, D.: Derivation of non-local macroscopic traffic equations and consistent traffic pressures from microscopic car-following models. Eur. Phys. J. B 69(4), 539–548 (2009)MathSciNetCrossRef
18.
Helbing, D., Johansson, A.: On the controversy around Daganzo’s requiem for and Aw-Rascie’s resurrection of second-order traffic flow models. Eur. Phys. J. B 69(4), 549–562 (2009)CrossRef
19.
Ghosh, B., Basu, B., O’Mahony, M.: Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Trans. Intell. Transp. Syst. 10(2), 246–254 (2009)CrossRef
20.
Yin, H., Wong, S.C., Xu, J., Wong, C.K.: Urban traffic flow prediction using a fuzzy-neural approach. Transp. Res. Part C: Emerg. Technol. 10(2), 85–98 (2002)CrossRef
21.
Quek, Y., Pasquier, M., Lim, B.: POP-TRAFFIC: a novel fuzzy neural approach to link traffic analysis and prediction. IEEE Trans. Intell. Transp. Syst. 7(2), 133–146 (2006)CrossRef
22.
Vlahogianni, E.I.: Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. J. Intell. Transp. Syst. 13(2), 73–84 (2009)CrossRef
23.
Furtlehner, C., Lasgouttes, J.-M., and de La Fortelle, A.: ‘A belief propagation approach to traffic prediction using probe vehicles’. Proc. 10th Int. Conf. Transportation Systems (ITSC), 1022–1027 (2007)
24.
Arel, I., Liu, C., Urbanik, T., Kohis, A.G.: Reinforcement learning-based multi-agent system for network traffic signal control. IET Intell. Transp. Syst. 4(2), 128–135 (2010)CrossRef
25.
Li, S.Z.: ‘Markov random field modeling in image analysis’ 3rd edn Springer Press, (2009)
26.
Shoham, Y., Leyton-Brown, K.: ‘Multiagent systems: algorithmic, game- theoretic, and logical foundations’. Cambridge University Press, Cambridge (2008)CrossRef
27.
Geroliminis, N., Daganzo, C.F.: Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transp. Res. B Methodol. 42(9), 759–770 (2008)CrossRef
28.
Geroliminis, N., Sun, J.: Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transp. Res. B Methodol. 45(3), 605–617 (2011)CrossRef
29.
Ji, Y., Geroliminis, N.: On the spatial partitioning of urban transportation networks. Transp. Res. B Methodol. 46(10), 1639–1656 (2012)CrossRef
30.
Han, Y., Moutarde, F.: Statistical traffic state analysis in large-scale transportation networks using locality-preserving non-negative matrix factorization. IET J. Intell. Transp. Syst. 7(3), 283–295 (2013)CrossRef
31.
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Wu, A.Y.: ‘An efficient k-means clustering algorithm: analysis and implementation’. IEEE Trans. Patterns Anal. Mach. Intell. 24, 881–892 (2002)CrossRef
32.
33.
34.
Liu, J., Liu J., Wonka, P., Ye, J.P.: ‘Sparse non-negative tensor factorization using column wise coordinate decent’. Pattern Recognition, 45(1) (2012)
35.
Spiegel, S., Clausen, J., Albayrak, S, Kunegis, J.: ‘Link prediction on evolving data using tensor factorization’. Proc. 15th International Conference on New Frontiers in Applied Data Mining, 100–110 (2011)
36.
Lee, D.D., Seung, H.S.: ‘Algorithms for non-negative matrix factorization’. Proc. 13th Neural Information Processing Systems (NIPS), Denver (USA), 556–562 (2000)
37.
Xu, W., Liu, X., Gong, Y.H.: ‘Document clustering based on non-negative matrix factorization’. Proc. 26th ACM SIGIR, Toronto (Canada), 267–273 (2003)
38.
Wang, Y., Jiang, Y., Wu, Y., Zhou, Z.H.: ‘Local and Structural Consistency for Multi-Manifold Clustering’. Proc. 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona (Spain), 1559–1564 (2002)
39.
Chen, G.L., Lerman, G.: Spectral curvature clustering. Int. J. Comput. Vis. 81(3), 317–330 (2009)CrossRef
40.
Chung, F.R.K.: ‘Spectral Graph Theory’. CBMS Regional Conference Series in Mathematics, (1997)
41.
Agrawal, R., Gehrke, J., Gunopulos, D.: Automatic subspace clustering of high dimensional Data. Data Min. Knowl. Disc. 11(1), 5–33 (2005)MathSciNetCrossRef
42.
Boyd, S., Vandenberghe L.: ‘Convex optimization’. Cambridge University Press
43.
Marchal F.: ‘Contribution to dynamic transportation models’. PhD Thesis, University of Cergy-Pontoise, (2001)
44.
De Palma, A., Marchal, F.: Real cases applications of the fully dynamic METROPOLIS tool-box: an advocacy for large-scale macroscopic transportation systems. Netw. Spat. Econ. 2(4), 347–369 (2002)
45.
Jolliffe, I.T.: ‘Principal component analysis’ 2nd edn Springer Press (2002)
Metadata
Title
Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization
Authors
Yufei Han
Fabien Moutarde
Publication date
15-08-2014
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 1/2016
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-014-0099-7

Other articles of this Issue 1/2016

International Journal of Intelligent Transportation Systems Research 1/2016 Go to the issue

OriginalPaper

Modeling Traffic at Sags

OriginalPaper

Risk of Traffic Incident Delay in Routing and Scheduling of Hazardous Materials

OriginalPaper

Recent Advances on Context-Awareness and Data/Information Fusion in ITS

OriginalPaper

Semantically Enriched Multi-Modal Routing

Premium Partners

    Image Credits