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 big surprise, (1) this correlation is significantly improved if a selfish rather than utopian strategy is adopted in forming the selforganized 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 insights into the understanding of road networks and their traffic from the perspective of complex networks.

Understanding Urban Traffic Flow Characteristics From the Network Centrality Perspective at Different Granularities

“…Figure 8 shows that the traditional road centrality and traffic flow exhibit a high correlation, thereby indicating that the centrality index can be used to analyze the road traffic flow. This finding is consistent with the conclusion in the literature (Jiang et al, 2008). The mean values of the correlation coefficient between traffic flow and the degree, betweenness, and PageRank centralities in every period are 0.73, 0.72, and 0.76, respectively.…”

“…In particular, network centrality is an important indicator of road network characteristics and has been broadly used in the analysis of urban traffic flow. Jiang et al (2008) studied the formation of self-organized natural roads from the perspective of complex network, and analyzed the correlation between road network centrality and traffic flow based on the joint principles. Betweenness centrality is utilized to characterize urban traffic flow, and the results demonstrate that the traditional betweenness centrality is unsuitable for analyzing the dynamic process of traffic flow and needs to be further improved (Kazerani and Winter, 2006a).…”

The Impact of Large Urban Structural Elements on Traffic Flow a Case Study of Danwei and Xiaoqu in Shanghai

“…In their paper, roads are merged from road segments of the same name. Later, Jang and his colleagues (Jiang et al 2008) found the self-organized nature of urban roads and extended the idea 'stroke' road to 'natural road' based on the Gestalt principle of 'good continuity'. They claimed that natural road network captures several essential characters of geographic space and navigation at human perception level.…”

Establishment of space syntax to read urban road network; the case of Sari, Iran

“…Scientific studies on this issue bring a new view that a group of network nodes have more structural dates in comparison with a single one (Wagner, 2008) (Tischendorf, L., Fahrig, L., 2000). This subject begins from the logic of a space syntax, that urban road network are working as a body neural network where movement are employed make simple linear elements in the network that could be able to present line movement in the network system to find natural units (Jiang, B., Zhao, S., Yin, J., 2008). Space syntax emerged from the architectural view to an environment one as logic a analyze space.…”