Developing A Fuzzy Rule Based Trip Generation using High Frequency Data

Abstract

Calculating trips from each traffic zone is one of the essential steps in the four-step model. Multiple linear regression (MLR) is the most popular among the various methods available for calculating trips. The main limitation of this method is its reliance on independent variables related to the zone. Due to the assumptions in this method, future predictions are also subject to the question of accuracy. Conversely, updating these independent variables requires additional time and resources for conducting selected types of surveys, such as home visit surveys (HVS). Using high-frequency data (HFD) that is freely available and is updated frequently, this paper estimates trip generation using fuzzy logic to fill in the gap. The fuzzy model was created using 2013 HVS data and updated the data with 2013 for validation purposes and 2019 as a prediction year. The research area chosen for this purpose is Thimbirigasyaya DSD in Western Province, Sri Lanka. According to the results of this study, a fuzzy rule-based model can be used when there are no exact data available, and the available high-frequency data shows a non-linear relationship with the dependent variable.