An empirical study of vehicular headways in urban areas

Abstract

Conventional statistical distribution models (e.g., Poisson) are known to perform satisfactorily in predicting the number of crossing opportunities in a traffic stream of certain characteristics. However, in many traffic studies, one is interested in estimating the number of crossing opportunities under traffic and roadway conditions that are not typical of any known statistical distribution. A typical example is the number of crossing opportunities across a two-way, multilane traffic stream within a signalized corridor. There does not appear to be a single distribution capable of adequately describing the vehicular headway distribution under a variety of traffic and roadway conditions.This paper demonstrates that carefully developed regression models can be used to accurately predict the number of crossing opportunities in a traffic stream under various roadway and traffic conditions. Compared with conventional statistical functions, the regression models are easy to use, transferable, and volume-based, thus allowing the user to predict the number of crossing opportunities using readily available traffic count data. It was found that, depending on the traffic conditions under consideration, conventional statistical distributions, such as the Poisson function, can underestimate the number of crossing opportunities by approximately one third. Typical applications of these models are in the area of development of warrants for vehicular and pedestrian traffic control devices. Key words: crossing opportunities, vehicular headway, arrival patterns, empirical models.