Impacts of automated passenger cars on the capacity of a freeway basic section: applicability in the determination of vehicle adjustment factors in mixed traffic

The highway capacity manual(HCM)provides a formula to calculate the heavy vehicle adjustment factor(fHV)as a function of passenger car equivalent factors for the heavy vehicle(ET).However,a significant drawback is that the methodology was established solely based on human-driven passenger cars(HDPC)and human-driven heavy vehicles(HDHV).Due to automated passenger cars(APCs),a new adjustment factor(fAV)might be expected.This study simulated traffic flows at different percentages of HDHVs and APCs to investigate the impacts of HDHVs and APCs on freeway capacity by analyzing their influence on fHV and fAV values.The simulation determined observed adjustment factors at different percentages of HDHVs and APCs(fobserved).The HCM formula was used to calculate(fHCM).Modifications to the HCM formula are proposed,and vehicle adjustment factors due to HDHVs and APCs were calculated(fproposed).Results showed that,in the presence of APCs,while fobserved and fHCM were statistically significantly different,fobserved and fproposed were statistically equal.Hence,this study recommends using the proposed formula when determining vehicle adjustment factors(fproposed)due to HDHVs and APCs in the traffic stream.

Estimation of Passenger Car Equivalents for Basic Freeway Sections at Different Traffic Conditions

Passenger car equivalent (PCE) is an important factor which is used to convert traffic volumes containing proportions of heavy good vehicles (HGVs) to a unify measure containing only passenger cars units (PCU). This paper uses large data base of real traffic raw data extracted from loop detector before being aggregated to estimate PCEs. These detectors are located on the M25 and the M42 motorway sites in the United Kingdom. The selected sites represent basic freeway segments as they are far away from the influence of entrance (on ramp) and exit (off ramp) sections. The data are filtered properly so as to estimate passenger car equivalents (PCEs) using lagging headway method for close following situations at different speed ranges. The results suggest that for the same location, the equivalency factors are varies significantly based traffic speed. However, it is proved that such variation with traffic speed is influenced by the differences in lengths between HGVs and cars. Regression models have also been developed linking the PCEs with traffic speed.

Queue clearance rate method for estimating passenger car equivalents at signalized intersections

This study explored the use of queue clearance rate method for estimating passenger car equivalent （PCE） at signalized intersections. PCE was estimated based on the assumption that the rate at which a queue of vehicles clears the intersection is a function of its composition. Results of this method were compared with the results estimated by some popular techniques. A fourqegged intersection was simulated in VISSIM software and different techniques were used to convert the traffic mix into a uniform one. Parameters of VISSIM were modified to closely reflect the traffic behaviour under heterogeneous traffic conditions. All approaches of the intersection were loaded to saturated conditions and accuracy of estimated PCEs were established by comparing converted flow （PCE/h） with the capacity of an all-car traffic stream. Method based on saturation flow delivered the best result, but its use was limited to traffic composed only of two types of vehicles. Results of regression and optimization techniques were almost similar and the converted flow was close to the capacity of all-car stream. However, accuracy of these methods strongly relied on the correct measurement of saturation flow. Queue clearance rate method did not require value of saturation flow and yielded good estimates of PCE throughout the simu- lation runs. The maximum difference between the converted flow and capacity estimated with all car situations was found to be less than 10% in all cases considered in this study.