The solution of traffic signal timing by using traffic intensity estimation and fuzzy logic

This study aims at calculating the traffic signal timing that suits traffic intensity at intersections studied in the inner city of Ubon Rachathani Provice, Thailand. The mixed models between maximum likelihood estimation and Bayesian inference are presented to estimate traffic intensity. A queuing system is used to generate the performance of traffic flow. A fuzzy logic system is applied to calculate the optimal length of each phase of the cycle. The fortran language is used to produce the computer program for computation. The algorithm of the computer programming is based on EM algorithm, Markov Chain Monte Carlo algorithm, queuing generation and fuzzy logic. The output of traffic signal timing from the fuzzy controller are longer than the traffic signal timing from the conventional controller. Cost function is used to evaluate the efficiency of the traffic controller. The result of the evaluation shows that fuzzy controller is more efficient than a conventional controller

Application of fuzzy logic to improve the Likert scale to measure latent variables

The research studied the process of improving the Likert scale based on fuzzy logic to measure latent variables and to compare the quality of the data as measured by the improved Likert scale with data measured by the Likert scale. Qualitative study and survey study were used as the research methodology. Data analysis included content analysis and statistics comprising the arithmetic mean, standard deviation, standard error, consensus index, and the Kolmogorov–Smirnov test. It was found that the Likert scale could be improved by using Mamdadi fuzzy inference which included four important steps: (1) fuzzification, (2) fuzzy rule evaluation, (3) aggregation, and (4) defuzzification. A comparison of the two different approaches showed that the data measured using the improved Likert scale was more suitable to be analyzed with the arithmetic mean and standard deviation than the data measured using the Likert scale. More importantly, the distribution of data measured by the improved Likert scale was normal with a lower standard error, making it appropriate for data analysis for statistical inference