ILP Experiments in Detecting Traffic Problems

Abstract

Expert systems for decision support have recently been successfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occured (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational background knowledge on the road network is essential. Preliminary results show that ILP can be used to successfully learn to detect traffic problems.

Original document

The different versions of the original document can be found in:

• http://oa.upm.es/14247

• https://link.springer.com/content/pdf/10.1007%2FBFb0026673.pdf

• https://lirias.kuleuven.be/handle/123456789/133593

• http://link.springer.com/content/pdf/10.1007/BFb0026673,

http://dx.doi.org/10.1007/bfb0026673

• http://oa.upm.es/14247,

https://link.springer.com/chapter/10.1007/BFb0026673,

https://dblp.uni-trier.de/db/conf/ecml/ecml98.html#DzeroskiJMM98,

https://rd.springer.com/chapter/10.1007/BFb0026673,

https://trid.trb.org/view.aspx?id=510545,

https://academic.microsoft.com/#/detail/1561145889

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