On the spatial partitioning of urban transportation networks

Transportation Research Part B: Methodological

Volumn 46, Issue 10, 2012, Pages 1639-1656

  Ji, Yuxuan ^a   Geroliminis, Nikolas ^a  

^a EPFL   (Switzerland)

Author keywords

Macroscopic fundamental diagram; Partitioning; Traffic flow; Urban mobility

Indexed keywords

CLUSTERING ALGORITHMS; IMAGE SEGMENTATION; TRAFFIC CONGESTION; TRANSPORTATION;

ADJUSTMENT ALGORITHMS; MACROSCOPIC FUNDAMENTAL DIAGRAM; PARTITIONING; PARTITIONING MECHANISM; TRAFFIC FLOW; TRANSPORTATION NETWORK; URBAN MOBILITY; URBAN TRANSPORTATION NETWORKS;

URBAN TRANSPORTATION;

ALGORITHM; CLUSTER ANALYSIS; COMPUTER SIMULATION; TRAFFIC CONGESTION; TRANSPORTATION INFRASTRUCTURE; URBAN TRANSPORT;

EID: 84868216794     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trb.2012.08.005     Document Type: Article

References (29)

1. Bishop C.M. Pattern Recognition and Machine Learning 2007, Springer.
2. Buisson C., Ladier C. Exploring the impact of homogeneity of traffic measurements on the existence of macroscopic fundamental diagrams. Transportation Research Record 2009, 2124:127-136.
3. Carmel, D., Roitman, H., Zwerdling, N., 2009. Enhancing Cluster Labeling Using Wikipedia. In: ACM SIGIR. Boston, Massachusetts, USA, pp. 139-146.
4. Cour, T., Benezit, F., Shi, J., 2005. Spectral segmentation with multiscale graph decomposition. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2. Vancouver, Canada, pp. 1124-1131.
5. Daganzo C.F. Urban gridlock: macroscopic modeling and mitigation approaches. Transportation Research Part B 2007, 41(1):49-62.
6. Daganzo C.F., Gayah V.V., Gonzales E.J. Macroscopic relations of urban traffic variables: bifurcations, multivaluedness and instability. Transportation Research Part B 2011, 45(1):278-288.
7. Day W.H.E., Edelsbrunner H. Efficient algorithms for agglomerative hierarchical clustering methods. Journal of Classification 1984, 1:1-24.
8. Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D., 2001. A min-max cut algorithm for graph partitioning and data clustering. In: IEEE International Conference on Data Mining. Vancouver, Canada, pp. 107-114.
9. Gayah V.V., Daganzo C.F. Clockwise hysteresis loops in the macroscopic fundamental diagram: an effect of network instability. Transportation Research Part B 2011, 45(4):643-655.
10. Geroliminis, N., Boyaci, B., 2012. The effect of variability of urban systems characteristics in the network capacity. Transportation Research Part B http://dx.doi.org/10.1016/j.trb.2012.08.001.
11. Geroliminis, N., Daganzo, C.F., 2007. Macroscopic modeling of traffic in cities. In: Transportation Research Record 86th Annual Meeting. Washington, DC, USA.
12. Geroliminis N., Daganzo C.F. Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transportation Research Part B 2008, 42(9):759-770.
13. Geroliminis, N., Haddad, J., Ramezani, M., 2012. Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: A model predictive approach. IEEE Transactions on Intelligent Transportation Systems http://dx.doi.org/10.1109/TITS.2012.2216877.
14. Geroliminis N., Sun J. Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transportation Research Part B 2011, 45(3):605-617.
15. Godfrey J.W. The mechanism of a road network. Traffic Engineering and Control 1969, 11(7):323-327.
16. Haddad J., Geroliminis N. On the stability of traffic perimeter control in two-region urban cities. Transportation Research Part B 2012, 46:1159-1176.
17. Han J., Kamber M. Data Mining: Concepts and Techniques 2006, Morgan Kaufmann.
18. Helbing D., Treiber M., Kesting A., Schonhof M. Theoretical vs. empirical classification and prediction of congested traffic states. European Physical Journal B 2009, 69(4):583-598.
19. Herman R., Prigogine I. A two-fluid approach to town traffic. Science 1979, 204(4389):148-151.
20. Jain, A.K., 2010. Data clustering: 50 years beyond k-means. In: 19th International Conference in Pattern Recognition (ICPR), vol. 31. Istanbul, Turkey, pp. 651-666.
21. Ji, Y., Geroliminis, N., 2011. Exploring spatial characteristics of urban transportation networks. In: The 14th IEEE Conference on Intelligent Transportation Systems (ITSC). Washington, DC, USA, pp. 716-721.
22. Johnson S.C. Hierarchical clustering schemes. Psychometrika 1967, 32(3):241-254.
23. Kerner B.S., Rehborn H. Experimental properties of complexity in traffic flow. Physical Review E 1996, 53(5):R4275-R4278.
24. Keyvan-Ekbatani, M., Kouvelas, A., Papamichail, I., Papageorgiou, M., 2012. Exploiting the fundamnetal diagram of urban networks for feedback-based gating. Transportation Research Part B http://dx.doi.org/10.1016/j.trb.2012.06.008.
25. Lin Y.L., Jiang T., Chao K.M. Efficient algorithms for locating the length-constrained heaviest segments, with applications to biomolecular sequence analysis. Journal of Computer and System Sciences 2002, 65(3):570-586.
26. Mazloumian A., Geroliminis N., Helbing D. The spatial variability of vehicle densities as determinant of urban network capacity. Philosophical Transactions of Royal Society A 2010, 368(1928):4627-4648.
27. Munoz J.C., Daganzo C.F. Structure of the transition zone behind freeway queues. Transportation Science 2003, 37(3):312-329.
28. Rousseeuw P. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 1987, 20(1):53-65.
29. Shi J., Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(8):888-905.