01557049

Component

https://doi.org/10.3141/2515-04

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.

01587392

15-5600

English

pp 26–33

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369305

Print

Figures (4) ; References (29) ; Tables (6)

Gap acceptance; Midblock crossings; Pedestrian safety; Traffic safety; Unsignalized intersections

Binary logit models; Support vector machines

Highways; Operations and Traffic Management; Safety and Human Factors; I73: Traffic Control

TRIS, TRB, ATRI

Dec 30 2014 1:53PM

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