Implementation of mamdani fuzzy implication in predicting traffic volume and duration of green lights on an intersection

Abstract

Abstract Traffic congestion at an intersection could be caused by the volume of vehicles that exceed the road capacity, the duration of the green light that is fixed, and so on. The volume of vehicles per unit time at an intersection cannot be known with certainty. Therefore, we need to predict it using fuzzy logic, specifically the Mamdani fuzzy implications. The problems are as follows: how are the input variables to be analyzed with Mamdani fuzzy implications; how are the prediction results, and how is the accuracy based on MAPE. The case study was conducted at two intersections in Semarang City. The tests were carried out using Matlab and manual calculations. The input variables in traffic volume prediction are MC, LV, HV, and time. While the input in the prediction of the duration of the green light is the number of motorcycles and cars. Based on the predictions, there are 74 vehicles (per hour) at the Kaligarang intersection in the east-north direction, there are 111 vehicles at the Kariadi intersection in the south-north direction, and the predictions are good and very accurate (measured by MAPE). The duration of the green light at the Kaligarang intersection on the west approach is 86 seconds, the duration on the Kariadi intersection on the north approach is 81 seconds, and the predictions are good and very accurate.