Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

Abstract

This study develops a tree augmented naive Bayesian (TAN) classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts' knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE) in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN) based and multilayer feed forward (MLF) neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

“…e study [35] presented a system for automated congestion detection on a road segment based on lane-changing properties. e literature [36] also proposed a Tree-Augmented Naïve Bayesian (TAN) classifierbased algorithm to estimate congestion. Due to lack of suitable datasets and transferability of information, the TAN-based algorithms are not evaluated in this paper.…”