Detecting traffic problems (LAI+KUL+ York+University of Madrid)

Application domain Detecting traffic problems
Data source University of Madrid
Dataset size
Data format Prolog
Systems used TILDE, ICL, C4.5
References (Dzeroski et al. 1998ab )

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. In this paper, we apply three state-of-the art ILP systems to learn how. to detect traffic problems and compare their performance to the performance of a propositional learning system on the same problem.


  1. S. Dzeroski, N. Jacobs, M. Molina, and C. Moure. ILP experiments in detecting traffic problems. In Proc. Tenth European Conference on Machine Learning, 61-66. Springer, Berlin, 1998a.

  2. S. Dzeroski, N. Jacobs, M. Molina, C. Moure, S. Muggleton, and W. Van Laer. Detecting traffic problems with ILP. In Proc. Eighth International Conference on Inductive Logic Programming, 281-290. Springer, Berlin, 1998b.

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