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.

Handling Continuous Attributes in an Evolutionary Inductive Learner

“…The traffic dataset [21], [22] describes the task of detecting sections of roads where a traffic problem-an accident or a congestion-has occurred at a specific time.…”

“…In [21] and [22], a discretization provided by experts in the field was used for the three numerical arguments of the traffic dataset. Using the same discretization, ECL-GSD obtained results that are slightly superior to those obtained using Fayyad and Irani algorithm [on the accidents dataset, the average accuracy on the test and training sets is 0.92 (0.03) and 0.94 (0.02) and the average simplicity is 5.10 (0.93).…”

How to Upgrade Propositional Learners to First Order Logic: A Case Study

“…Other successful experiments with ICL include: nite element mesh design by 68]; automated acquisition of knowledge on tra c problem detection by 30,33]; and the problem of diterpene structure elucidation from 13 C NMR spectra by 30].…”