GAP ACCEPTANCE BEHAVIOR OF VEHICLES AT UNSIGNALIZED INTERSECTIONS

Abstract

The primary objective of this study is to investigate the gap acceptance behavior of vehicles at unsignalized intersections. To achieve this, data were collected from three different unsignalized T-intersections using video cameras, and a semiautomatic tool, Kinovea, was utilized for data extraction. Two models were developed in this study, namely, the gap acceptance decision model, which classifies the drivers' decision to accept or reject a gap based on certain influencing factors, and the accepted spatial gap estimation model, which estimates the gap accepted by drivers based on these influencing factors. In order to build these models, various machine learning techniques such as logistic regression, support vector machine, random forest and linear regression were employed for classification and regression analysis. Furthermore, feature selection was applied to reduce the dimension of the data. The results showed that the variable gap size had a positive relationship with gap acceptance behavior while the variable speed had a negative relationship. Moreover, the random forest model outperformed other models, achieving higher performance matrices. Conflicting speed was found to be the most important variable in regression analysis, and other variables like waiting time, number of queued vehicles, number of rejected gap, and minor road vehicle type also had some importance in the model development. This study provides valuable insights into gap acceptance behavior at unsignalized intersections and can be useful for traffic planning and management.