Fuzzy Ontology-Based System for Driver Behavior Classification

Abstract

Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system’s knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user’s driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers’ behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal.

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

“…Some of them are multinomial naïve Bayes (MNB), decision tree ID3 (DTID3), decision tree C45 (DTC45), decision tree C50 (DTC50), neural network (NN), genetic algorithm (GA), and analytical hierarchy process (AHP). Most of each variable is discretized using the same membership function [3,5,28,29]. However, some combine several fuzzy membership functions [9,10,12].…”

“…Each category from the lowest to the highest value range is represented by a membership function successively linear descending, triangular, and linearly ascending. Combining linear and triangular fuzzy membership functions on most predictor variables increased the model's performance [5]. In this case, driver behavior is predicted using a genetic algorithm, and age is the only variable discretized using a fuzzy trapezoidal membership function.…”

“…Discretization is a preprocessing technique to improve the knowledge extraction process of continuous-type data and is also helpful for improving the model [1][2][3]. This process is essential in some statistical machine-learning methods where continuous data must be processed or handled [4][5][6][7][8]. For example, when the value of the set of predictor variables is a continuous or real number, there will likely be very few observations that will have the same value.…”

“…As a result, the number of antecedents in the classification rules is few. Implementing fuzzy discretization in several classification methods has increased the classification method's performance [4,5,8,9], including naïve Bayes [10][11][12].…”

“…The other name is non-parametric naïve Bayes [30,31]. However, in some cases, these naïve Bayes methods did not obtain the classification performance satisfactorily [5,32], especially in corn plant disease classification [15,16].…”