Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

Abstract

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

WATI: Warning of Traffic Incidents for Fuel Saving

“…The average detection rate (90.02%) obtained in the three routes is better than the value achieved in four solutions [11][12][13]16]. In addition, only one of the algorithms does not require sensors on the road [17].…”

“…This model allows us to estimate the posterior probability ( | ) of different classes (traffic incident | not traffic incident). Applying Bayes theorem [11], we obtain…”

“…Most of the automatic traffic incident detection systems are based on fixed-detection techniques. These solutions analyze the impact of traffic parameters upstream and downstream of the incident position [3,[11][12][13][14][15][16]. Their major drawbacks are related to the cost and complexity of their installation [17].…”

Learning Classifier Systems for Road Traffic Congestion Detection

“…Instead of just relying on one single technique, some researchers combine several methods, e.g. (Liu et al, 2014) use multiple naïve bayes classifiers. Some of these algorithms are able to learn new patterns and to improve their model at runtime (reinforcement learning), e.g.…”

Sensitivity Analysis Based SVM Application on Automatic Incident Detection of Rural Road in China

“…In 2014, Xiao et al [18] approved a new AID method based on the SVM, which together systematically integrated kernel functions, and made the usage of the AID technology in low traffic volume road section a feasible method. Liu et al [19] proposed an AID algorithm in 2014, which integrated several Bayesian classifiers, proven to obtain a better robustness. Huang et al [20] made progress in machine learning in 2012.…”