Algorithm for the Transportation Network Nodes Aggregation Using Fuzzy Logic

Aleksander Król


In many situations, the model of the transportation network contains a very large number of nodes, so the algorithms operating on such a model may be too time-consuming. Therefore there is a need to simplify the model by reducing the number of nodes. The simplest approach using the physical neighbourhood of the nodes and then aggregation of nearby nodes may be insufficient, because it does not take into account the different roles played by the nodes subject to the merger. Some of them have local significance and can be aggregated without disturbing the traffic flows across the whole network. For other nodes traffic flows associated with geographically distant nodes can be much larger than the local flows. Nodes of this kind should not be aggregated with their neighbours. This paper presents an algorithm for grouping the transportation network nodes using fuzzy logic, which processes the qualitative characteristics of nodes.


transportation network, nodes aggregation, fuzzy logic

Full Text:



Bauer D., Daigle J. N., Iliadis I., Scotton P.: Topology aggregation for combined additive and restrictive metrics, Computer Networks 50 2006, pp. 3284–3299.

Bjorke J. T., Nilsen S., Varga M.: Visualization of network structure by the application of hypernodes, International Journal of Approximate Reasoning 51 2010, pp. 275–293.

Börner K., Sanyal S., Vespignani A.: Network science, in: B. Cronin (Ed.), Annual Review of Information Science and Technology, vol. 41, Information Today, Inc./American Society for Information Science and Technology, Medford, NJ 2007, pp. 537–607.

Dupuy G., Stransky V., Cities and highway networks in Europe, Journal of Transport Geography Vol. 4, No. 2 1996, pp. 107-121.

Gavriliouk E. O.: Aggregation in hub location problems, Computers & Operations Research 36 2009, pp. 3136 – 3142.

Hartigan J. A., Wong M. A.: A K-Means Clustering Algorithm, Applied Statistics, Vol. 28, No. 1 1979, pp. 100-108.

Hoeppner F., Klawonn F., Kruse R., Runkler T.: Fuzzy cluster analysis. Methods for Classification, Data Analysis and Image Recognition, John Wiley & Sons, Chichester 1999.

Kashan A. H. et al. : A particle swarm optimizer for grouping problems, Inform. Sci. 2013, in pront,

Król A., Pamuła T.: Using a genetic algorithm for the design of an optimal transport network, Probl. Transp. 2009 vol. 4 z. 4, pp. 107-113.

Piegat A.: Modelowanie i sterowanie rozmyte, Akademicka Oficyna Wydawnicza EXIT, Warszawa 1999.

Schaeffer S. E.: Graph clustering, Computer Science Review 1 2007, pp. 27 – 64.


  • There are currently no refbacks.