Using Lagging Headways to Estimate Passenger Car Equivalents on Basic Freeway Sections

Abstract

Highway agencies strive to provide the highest possible level of service (LOS) to their users. Traffic density, the main factor affecting the freeway, is determined by converting a mixed traffic stream to an equivalent passenger car stream using passenger car equivalencies (PCEs). Traditionally, equivalent delay and microscopic simulation have been used to estimate PCE values. This study presents an alternative methodology to estimate PCEs on the basis of lagging headways derived from video-recorded traffic data. This methodology allows for site-specific PCE values to be calculated based on prevailing traffic characteristics. In addition, the methodology provides separate PCE values for single-unit trucks (SUTs) and combination trucks (CTs). Compared to the single value provided in the Highway Capacity Manual (HCM), defining two separate truck PCEs provides a more robust description of the equivalent traffic stream. Lagging headway, defined as the distance from the rear bumper of a leading vehicle to the rear bumper of the following vehicle, is the actual space a vehicle consumes while in the traffic stream. This research used three-stage-least-squares (3SLS) regression to generate a model that predicts lagging headways for three vehicle classes; passenger cars (PC), SUT, and CT. The headways are influenced by prevailing traffic flow characteristics of a freeway segment. The 3SLS model was then expanded to predict lagging headways for each of the nine vehicle-following combinations, which were used to calculate class-average lagging headways. The PCE values were calculated as the ratio of the lagging headway of each truck class to that of PC. Forecasting accuracy comparisons showed that the two models provide reliable predictions of vehicle class lagging headways and hence lead to reliable PCE values for SUT and CT.