Infrastructures connecting people. A mechanistic model for terrestrial transportation networks

TL;DR

This paper introduces a network-based methodology and a mechanistic model to analyze and replicate terrestrial transportation infrastructures, revealing underlying socio-political and geographical influences on their structure.

Contribution

It presents a novel inverse engineering approach that generates synthetic transportation networks matching empirical data using a simple decision-making model.

Findings

The model can replicate real transportation network structures.

Adjusting parameters reveals underlying decision-making processes.

The methodology offers insights into socio-political influences on infrastructure development.

Abstract

The structure and evolution of Terrestrial Transportation Infrastructures (TTIs) are shaped by both socio-political and geographical factors, hence encoding crucial information about how resources and power are distributed through a territory. Therefore, analysing pathway, railway or road networks allows us to gain a better understanding of the political and social organization of the communities that created and maintained them. Network science can provide extremely useful tools to address quantitatively this issue. Here, focusing on passengers transport, we propose a methodology for mapping a TTI into a formal network object able to capture both the spatial distribution of the population and the connections provided by the considered mean of transport. Secondly, we present a simple mechanistic model that implements a wide spectrum of decision-making mechanisms which could have driven the creation of links (connections). Thus, by adjusting few parameters, for any empirical system, it is possible to generate a synthetic counterpart such that their differences are minimized. By means of such inverse engineering approach, we are able to shed some light on the processes and forces that moulded transportation infrastructures into their current configuration, without having to rely on any additional information besides the topology of the network and the distribution of the population. An illustrative example is also provided to showcase the applications of the proposed methodology and discuss how our conclusions fit with previously acquired knowledge (and literature) on the topic.