Abstract
Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers’ paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.
Highlights
Mobility and accessibility are essential factors for lifestyle and prosperity
In order to quantify network-related risks, resilience, or optimal intervention strategies, the traffic assignment problem must be solved once but many times with different network topologies (e.g. see (Erath 2011; Vugrin et al 2014; Hackl et al 2018a; Hackl et al 2018b; Schlögl et al 2019)). While addressing such problems have led to a substantial body of work in areas such as geography, economics, and transportation research, complex network theory still plays a minor role. Complex networks made their appearance in transportation research through empirical measures, little research has so far been done to investigate the relationship between dynamic traffic patterns and the underlying structures of the transportation networks (Barrat et al 2008)
Networks in networks A Networks in Networks (NiN) structure is a special type of multi-layer network in which vertices themselves are networks, i.e. GNiN = (GNiN, ENiN) is a network with a set of graphs GNiN acting as vertices and a multiset ENiN ⊆ GNiN × GNiN of edges
Summary
Mobility and accessibility are essential factors for lifestyle and prosperity. People travel to satisfy their needs, by carrying out certain activities at specific places such as work, leisure and learning. Network performance (2019) 4:28 deteriorates as soon as the number of vehicles in the network exceeds a critical accumulation (Daganzo and Geroliminis 2008; Hoogendoorn and Knoop 2012), i.e. vehicles block each other and the flow decreases, leading to spillbacks and gridlock effects This phenomenon is amplified by the fact that even small (unexpected) failures or damage to the infrastructure (i.e. changes in topology) can lead to significant disruptions that are disproportionate to the actual physical damage itself (Vespignani 2010). Transport supply is the service provided at a certain point in time This includes the infrastructure (e.g. road network) and a set of mobile units (e.g. persons, vehicles, goods). In addition to topology, flow characteristics, such as origin-destination demands, capacity constraints, path choice and travel costs, are taken into account to represent the movement of people, vehicles or goods, the network is referred to as transportation network
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