Self-organized natural roads for predicting traffic flow: a sensitivity study
Abstract
In this paper, we extended road-based topological analysis to both nationwide and urban road networks, and concentrated on a sensitivity study with respect to the formation of self-organized natural roads based on the Gestalt principle of good continuity. Both annual average daily traffic (AADT) and global positioning system (GPS) data were used to correlate with a series of ranking metrics including five centrality-based metrics and two PageRank metrics. It was found that there exists a tipping point from segment-based to road-based network topology in terms of correlation between ranking metrics and their traffic. To our great surprise, (1) this correlation is significantly improved if a selfish rather than utopian strategy is adopted in forming the self-organized natural roads, and (2) point-based metrics assigned by summation into individual roads tend to have a much better correlation with traffic flow than line-based metrics. These counter-intuitive surprising findings constitute emergent properties of self-organized natural roads, which are intelligent enough for predicting traffic flow, thus shedding substantial light on the understanding of road networks and their traffic from the perspective of complex networks.
- Publication:
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Journal of Statistical Mechanics: Theory and Experiment
- Pub Date:
- July 2008
- DOI:
- 10.1088/1742-5468/2008/07/P07008
- arXiv:
- arXiv:0804.1630
- Bibcode:
- 2008JSMTE..07..008J
- Keywords:
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- Physics - Data Analysis;
- Statistics and Probability;
- Physics - Physics and Society
- E-Print:
- 23 pages, 16 figures