Classification of Gaps at Uncontrolled Intersections and Midblock Crossings Using Support Vector Machines

Abstract

Gap acceptance predictions provide very important inputs for performance evaluation and safety analysis of uncontrolled intersections and pedestrian midblock crossings. The focus of this paper is on the application of support vector machines (SVMs) in understanding and classifying gaps at these facilities. The SVMs are supervised learning techniques originating from statistical learning theory and are widely used for classification and regression. In this paper, the feasibility of the SVM in analyzing gap acceptance is examined by comparing its results with existing statistical methods. To accomplish that objective, SVM and binary logit models (BLMs) were developed and compared by using data collected at three types of uncontrolled intersections. SVM performance was found to be comparable with that of the BLM in all cases and better in a few. Also, the categorical statistics and skill scores used for validating gap acceptance data revealed that the SVM performed reasonably well. Thus, the SVM technique can be used to classify and predict accepted and rejected gap values according to speed and distance of oncoming vehicles. This technique can be used in advance safety warning systems for vehicles and pedestrians waiting to cross major stream vehicles.