Generation of Spatially Embedded Random Networks to Model Complex Transportation Networks

TL;DR

This paper introduces a methodology for generating spatially embedded random networks that preserve spatial properties, aiding in the analysis of complex transportation networks by providing insights into their structure and risk estimation.

Contribution

It presents a novel approach combining spatial point processes and hybrid connection models to create random networks that retain spatial characteristics, enhancing transportation network analysis.

Findings

Successfully applied to Swiss road network data

Generated networks match real network structural statistics

Provides a new tool for risk and structural analysis

Abstract

Random networks are increasingly used to analyse complex transportation networks, such as airline routes, roads and rail networks. So far, this research has been focused on describing the properties of the networks with the help of random networks, often without considering their spatial properties. In this article, a methodology is proposed to create random networks conserving their spatial properties. The produced random networks are not intended to be an accurate model of the real-world network being investigated, but are to be used to gain insight into the functioning of the network taking into consideration its spatial properties, which has potential to be useful in many types of analysis, e.g. estimating the network related risk. The proposed methodology combines a spatial non-homogeneous point process for vertex creation, which accounts for the spatial distribution of vertices, considering clustering effects of the network and a hybrid connection model for the edge creation. To illustrate the ability of the proposed methodology to be used to gain insight into a real world network, it is used to estimate standard structural statistics for part of the Swiss road network, and these are then compared with the known values.