Machine-learning approach for estimating passenger car equivalent factors using crowdsourced data

Abstract

Passenger car equivalent (PCE) factors are used by the Highway Capacity Manual (HCM) to convert truck volumes to equivalent passenger car volumes and are typically calculated using multi-class volumes collected from traffic sensors. However, this requires costly sensor installations that provide limited spatial coverage. Therefore, this study proposes a novel approach to estimate PCE volumes using crowdsourced and open data. A multi-class volume estimation model (TS-SAE-XGB) is proposed to estimate passenger car and truck volumes, and single unit truck ratios. These parameters are input to a PCE interpolation algorithm which estimates PCE values using HCM methods. A spatial leave-one-out cross validation was conducted to compare the proposed model against five other machine learning models when estimating PCE values. The TS-SAE-XGB model estimated PCE and heavy vehicle factors with a MAPE of 6.22% and 3.03%, respectively, providing transportation professionals a practical method of estimating freeway PCE values where sensors are unavailable.