An Exploratory Study of Vehicle Class Headway Ratios as Passenger Car Equivalence Values Using Three Stage Least Squares Estimation

Published in Journal of the Transportation Research Board, 2010

Recommended citation: Van Boxel, D. (2010). "An Exploratory Study of Vehicle Class Headway Ratios as Passenger Car Equivalence Values Using Three Stage Least Squares Estimation" Journal of the Transportation Research Board. https://trid.trb.org/view/909366

Abstract: The current Highway Capacity Manual (HCM) method for accounting for trucks on basic freeway sections uses values derived from a microscopic simulation of equivalent delay. Although this method can provide passenger car equivalent (PCE) values separately for single unit and combination trucks, the HCM in equation 23-3 (1) aggregates single unit and combination trucks into one class while adding an additional class for recreational vehicles. An alternative method such as headway ratios, however, may improve on the HCM method by having a strong theoretical density basis as well as using real data to determine more robustly PCEs for each truck class. Three stage least squares estimates specific class headways from which one calculates the ratio as the PCE value. This study uses high resolution freeway traffic data from I-65 outside Indianapolis to substantiate the plausibility of this method. Using this method produces some variation in Level of Service (LOS) from the traditional method. Further, extrapolation of traffic conditions may change LOS determinations. The study also explores transferability and regional stability of PCEs derived from these headway models using two data sources, an Indiana Microloop and an Ohio WiM station. The likelihood ratio test indicated that two headway models developed from these data sets are statistically different. Therefore, one cannot naively combine headway data sets. The regions used in this study were somewhat different, but even intrastate regions may exhibit significant variation. Next, there may be a certain data collection method that creates more accurate models. WiM stations, for example, have excellent time stamp precision. Other factors would affect which data collection methodology is best, but a uniform approach is key.