Detecting Traffic Problems with ILP

Saso Dzeroski1, Nico Jacobs2, Martin Molina3, Carlos Moure3,
Stephen Muggleton4, Wim Van Laer2

1 J. Stefan Institute, Jamova 39, SI-1000 LiubUana, Slovenia
2 K.U.Leuven, Celestijnenlaan 200A, B-3001 Heverlee, Belgium
3 Universidad Politecnica de Madrid, E-28660 Boadilla del Monte, Madrid, Spain
4 Department of Computer Science, University of York, York, YO1 5DD, UK



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. 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.
References
1.	Barcelo, J., Ferrer J.L., and Montero, L. (1989). AIMSUN: Advanced Interactive
Microscopic Simulator for Urban Networks. Vol I: System Description, and Vol II:
Users Manual. Departamento de Estadistica e Investigacion Operativa, Facultad
de Informatica, Universidad Politecmca de Cataluna, Barcelona, Spain.
2.	Blockeel, H., and De Raedt, L. (1997). Lookahead and discretization in ILP. In Proc.
7th Intl. Workshop on Inductive Logic Programming, pages 7784, Springer, Berlin.
3.	Clark, P. and Boswell, R. (1991). Rule induction with CN2: Some recent improvements. 
In Proc. Fifth European Working Session on Learning, pages 151163.
Springer, Berlin.
4.	Guena, J., Ambrosino, G., and Boero M. (1992). A general knowledge-based architecture 
for traffic control: The KITS approach. In Proc. Intl. Conf. on Artificial
Intelligence Applications in Thansportation Engineering. San Buenaventura, CA.
5.	Cuena, J., Hernandez, J., and Molina, M. (1995). Knowledge-based models for adaptive 
traffic management systems. Thansportation Research: Part C, 3(5): 311-337.
6.	De Raedt, L. (1997). Logical settings for learning. Artificial Intelligence.
7.	De Raedt, L., and Dehaspe, L. (1997). Clausal discovery. Machine Learning, 26:
99146.
8.	De Raedt, L., and Van Laer, V. (1995). Inductive constraint logic. Proc. Sixth International 
Workshop on Algorithmic Learning Theory, pp. 80-94. Berlin: Springer.
9.	Deeter, D.L., and Ritchie, S.G. (1993). A prototype real-time expert system for surface 
street traffic management and control. In Proc. 3rd Intl. Conf. on Applications
of Advanced Technologies in Thansportation Engineering, Seattle, WA.
10.	Dzeroski, S., Jacobs, N., Molina, M., Moure, C. (1998). ILP experiments in detecting 
traffic problems. In Proc. Eleventh European Conference on Machine Learning.
Springer, Berlin. To appear.
11.	Muggleton, S. (1992). Inverse entailment and PROGOL. New Generation Computing 13: 245286.
12.	Quinlan, J.R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann,
San Mateo, CA.
13.	Roberts, S., Van Laer, W., Jacobs, N., Muggleton, S., Broughton, J. (1998) A
comparison of ILP and popositional systems on propositional traffic data. In Proc.
Eighth International Conference on Inductive Logic Programming. Springer, Berlin.
This volume.
