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Journal of Statistical Mechanics: Theory and Experiment Volume 2008 July 2008 Create an alert RSS this journal

Bin Jiang et al J. Stat. Mech. (2008) P07008 doi:10.1088/1742-5468/2008/07/P07008

Self-organized natural roads for predicting traffic flow: a sensitivity study

Bin Jiang, Sijian Zhao and Junjun Yin

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Abstract References Cited By Figures

Figure 1

Figure 1. A notional road network and its connectivity graphs: (a) segment-based connectivity graph, and road-based connectivity graphs with respect to different join principles of (b) every-best-fit, (c) self-best-fit, and (d) self-fit.



Figure 2

Figure 2. Nationwide road networks in Sweden (a) divided into seven regions (b) and Gävle urban street network (c). (Note: the red spot in (b) is the location of the city Gävle.)



Figure 3

Figure 3. The number of natural roads drops as the threshold angle rises: the case of the entire nationwide road network (a) and Gävle urban street network (b).



Figure 4

Figure 4. Distribution of (a) segment connectivity and (b) road connectivity, whose log–log plot shows a straight line (the inset).



Figure 5

Figure 5. Log–log plots of (a) connectivity, (b) control, (c) betweenness, (d) PageRank (d = 0.20), (e) weighted PageRank (d = 0.20), (f) flow (threshold angle = 45), (g) local integration and (h) global integration.



Figure 6

Figure 6. Log–log plots of (a) connectivity, (b) control, (c) betweenness, (d) PageRank (d = 0.95), (e) weighted PageRank (d = 0.95), (f) flow (threshold angle = 45), (g) local integration and (h) global integration.



Figure 7

Figure 7. Correlation coefficient (R square) between traffic flow and (a) five centrality-based metrics, (b) PageRank and (c) weighted PageRank, with respect to the threshold angle 45, based on the principle of every-best-fit and using the case of the Sydost region. (Note: for both PageRank and weighted PageRank, they have a series of PageRank scores with respect to different damping factor d values.)



Figure 8

Figure 8. Correlation coefficient (R square) between traffic flow and (a) five centrality-based metrics, (b) PageRank and (c) weighted PageRank, with respect to the threshold angle, based on the principle of self-best-fit and using the case of the Sydost region. (Note: for both PageRank and weighted PageRank, they have a series of PageRank scores with respect to different damping factor d values. The curves are the averaged result from 20 experiments.)



Figure 9

Figure 9. Correlation coefficient (R square) between traffic flow and (a) five centrality-based metrics, (b) PageRank and (c) weighted PageRank, with respect to the threshold angle 45, based on the principle of self-fit and using the case of the Sydost region. (Note: for both PageRank and weighted PageRank, they have a series of PageRank scores with respect to different damping factor d values. The curves are the averaged result from 20 experiments.)



Figure 10

Figure 10. Correlation coefficient (R square) between traffic flow and (a) five centrality-based metrics, (b) PageRank and (c) weighted PageRank, with respect to the threshold angle 45, based on the principle of every-best-fit and using the case of the Gävle street network and one day traffic flow. (Note: for both PageRank and weighted PageRank, they have a series of PageRank scores with respect to different damping factor d values.)



Figure 11

Figure 11. Correlation coefficient (R square) between traffic flow and (a) five centrality-based metrics, (b) PageRank and (c) weighted PageRank, with respect to the threshold angle 45, based on the principle of self-best-fit and using the case of the Gävle urban street network and one day traffic. (Note: for both PageRank and weighted PageRank, they have a series of PageRank scores with respect to different damping factor d values. The curves are the averaged result from 20 experiments.)



Figure 12

Figure 12. Correlation coefficient (R square) between traffic flow and (a) five centrality-based metrics, (b) PageRank and (c) weighted PageRank, with respect to the threshold angle, based on the principle of self-fit and using the case of the Gävle urban street network and one day traffic. (Note: for both PageRank and weighted PageRank, they have a series of PageRank scores with respect to different damping factor d values. The curves are the averaged result from 20 experiments.)



Figure 13

Figure 13. Log–log plots of local (a) and global (b) integration using the point-based approach (the case of the entire nationwide road network).



Figure 14

Figure 14. Log–log plots of local (a) and global (b) integration using the point-based approach (the case of the Gävle street network).



Figure 15

Figure 15. Correlation coefficient (R square) between traffic flow and point-based centrality metrics; (a) the case of Sydost and (b) the case of Gävle. (Note: local and global integrations in particular.)



Figure 16

Figure 16. Formation of a natural road (green) in the sequence of (a)–(d) using the principle of self-best-fit.




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