Measuring health of highway network configuration against dynamic Origin-Destination demand network using weighted complex network analysis

Abstract

Ideal configuration or layout of highways should resemble the actual demands for the roads represented by Origin-Destination (OD) information. It would be beneficial if existing highways can be evaluated for their configurational fitness against the current demands, and newly planned highways can carefully be designed in terms of their layouts and topologies that would reflect the demands. Analysis techniques used for complex networks in the matured field of network theory can be applied for the highway layout health monitoring against the current OD information. This paper proposes a methodology of measuring the fitness of existing highways by comparing their structural configuration against conceptual OD networks using well-established techniques in network theory for complex networks. In the first phase, this paper conducts an empirical analysis and finds that both structural highway network and OD network follow the “power law” distribution as they are weighted by capacity and traffic volume respectively. It is also found that the power law coefficient of the OD network dynamically changes throughout the day and week. In the second phase, a noble methodology of weighting and measuring the health, of structural highway networks against OD networks by means of comparing their power law coefficients is proposed. It is found that the proposed method is effective at detecting deviations from ideal structural configurations associated with actual demands.

What Impact Will the New-Built Metro Bring to the Transportation of Second-Tier Cities? From the Perspective of a Multilayer Complex Network

“…Liu et al [16] took travel time as the weight to analyze the connectivity of the Wuhan urban rail transit network. Tak et al [17] weighted a highway network by traffic volume and proposed an actual demands-based method to detect deviations from ideal structural configurations. However, considering second-tier cities with a new-built metro, there is barely any passenger flow data.…”

Complex Network Modelling of Origin–Destination Commuting Flows for the COVID-19 Epidemic Spread Analysis in Italian Lombardy Region

“…For instance, the airline traffic among different urban areas in the world shows p(k) = k −α f (k/k x ), where α = 2 and f (k/k x ) is an exponential cutoff function which takes into account the physical constraints on the maximum number of connections that a single airport can handle [18,25]. The authors in [15] used the complex network theory to to assess the fitness of the current physical transportation network against the conceptual road network by using the origin-destination movement matrix showing a power law relation between the frequency of a degree and the degree of a node in the OD network. In general, the scale-free properties of a wide range of degree and strength values clearly indicate the heterogeneous behavior of both topology and dynamic flows on the infrastructure transportation networks.…”

“…The main rationale of this study is that the connection patterns of the commuting flow networks could offer a wide-ranging overview of the spreading process in the region where the epidemic started in Italy. Indeed, the origin-destination matrices have been often used to analyze traffic flow and traffic demand and model the network structure in the long run for different purposes [14,15]. The main objective of this study is to quantify the potential contribution of each category of travellers to the spread of the epidemic process in order to assign a quantitative score to each of them.…”

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

“…The other is focused on spatial distribution of OD flow in geographical space. The OD flow attributes can be achieved by complex network and machine learning methods [31][32][33][34], and spatial distributions can be yielded by a spatial statistics-based method and a density-based method [35,36].…”