Examining Headway Distribution Models with Urban Freeway Loop Event Data

Abstract

Vehicle headway distribution is fundamental for several important traffic research and simulation issues. Many headway models have been developed over the past decades. Each has its own strength and weakness. Selection of the most suitable model for a certain traffic condition remains an open issue. A comprehensive study of the performance of typical headway distribution models on urban freeways is presented. With the advanced loop event data analyzer system, many accurate headway observations were obtained from I-5 in the area of Seattle, Washington. These headway data were used to calibrate and examine the performance of various headway models. The goodness of fit for several most commonly used headway distribution models was investigated by using headways observed on regular lanes and high-occupancy-vehicle (HOV) lanes from different time periods of day. To evaluate the performance of these headway models, the analytical Kolmogorov-Smirnov test statistic and visualized comparison curves were used to measure and reflect their overall goodness of fit to the collected headway data. Although each model has its own practicability to a certain extent, the test results showed that the double-displaced negative exponential distribution model provided the best fit to these urban freeway headway data, especially for HOV lanes at wideranging flow levels. The shifted lognormal distribution also fits the general purpose lane headways very well. As a byproduct, a new standard parameter estimation method was developed for calibrating complex multiparameter headway models.