TY - JOUR
T1 - Estimation of traffic flow changes using networks in networks approaches
AU - Hackl, Jürgen
AU - Adey, Bryan T.
N1 - Funding Information:
The work presented here has received funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under grant agreement no. 603960, and from Horizon 2020, the European Union’s Framework Programme for Research and Innovation, under grant agreement no. 636285.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - Diffusion
KW - Infrastructure
KW - Multi-layer networks
KW - Network dynamics
KW - Networks in networks
KW - Simulation
KW - Traffic flow
KW - Transportation
UR - http://www.scopus.com/inward/record.url?scp=85066504762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066504762&partnerID=8YFLogxK
U2 - 10.1007/s41109-019-0139-y
DO - 10.1007/s41109-019-0139-y
M3 - Article
AN - SCOPUS:85066504762
SN - 2364-8228
VL - 4
JO - Applied Network Science
JF - Applied Network Science
IS - 1
M1 - 28
ER -