Self-organized Natural Roads for Predicting Traffic Flow: A Sensitivity Study
Bin Jiang, Sijian Zhao, Junjun Yin

TL;DR
This study investigates how self-organized natural roads, based on the Gestalt principle, can predict traffic flow more effectively, revealing a tipping point and surprising insights into network topology and traffic correlation.
Contribution
It introduces a sensitivity analysis of natural roads formation, highlighting the impact of selfish strategies and point-based metrics on traffic prediction accuracy.
Findings
Significant correlation improvement with selfish natural roads.
Existence of a tipping point from segment-based to road-based topology.
Point-based metrics outperform line-based metrics in traffic correlation.
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 big 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…
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