Disaggregate Gap-Acceptance Model for Unsignalized T-Intersections

Abstract

This paper develops a system of disaggregate models that accounts for the effect of intersection, driver, and traffic characteristics on gap acceptance for left-turn maneuvers at urban T-intersections controlled by stop signs on the minor roads. The waiting time for each driver is first modeled using the hazard function. The binary probit model is then used to find the drivers' probabilities of accepting or rejecting a gap. These probabilities are used to calculate the critical gaps of individual drivers. The expected waiting time is included in the model as an explanatory variable. A multiple regression model is used for predicting the intersection mean critical gap. To estimate the parameters of the models, disaggregate data were collected by observing and interviewing drivers at 15 urban T-intersections in the Greater Amman area. The results show that the distribution of critical gaps is influenced by driver socioeconomic characteristics, expected waiting time, time of day, and trip purpose. The mean critical gap is influenced by total opposing traffic flow, number of major-approach lanes, presence of a median with a left-turn lane, maneuver type, speed of major road, and time of day. The proposed methodology can be used to calculate the critical gaps of individual drivers and, in turn, the mean critical gap at a specified intersection that is needed for delay and capacity analysis.