Passenger car equivalents for heavy vehicles are required to carry out capacity calculations and perform operational analysis of any road entity (roadway segments or intersections). At single-lane roundabouts, the constraints to the vehicular trajectories imposed by the curvilinear geometric design and the driver's gap acceptance behaviour are expected to produce an impact of the heavy vehicles on the quality of traffic flow different from that produced on freeways and two-lane highways or other at-grade intersections. This is also because entering flow is opposed by the circulating flow which has priority and travels in an anticlockwise direction on a single-lane path around the central island. This paper addresses the question of how to estimate the passenger car equivalents for heavy vehicles on single-lane roundabouts. First, a comparison was performed between the empirical capacity functions based on a meta-analytic estimation of the critical and the follow up headways and the simulation outputs manually obtained for a single-lane roundabout built in Aimsun microscopic simulator. A genetic algorithm-based calibration procedure, therefore, was used to reach a better convergence between the simulation outputs and the empirical capacities. Based on the calibrated model, the passenger car equivalents were determined by comparing the capacity functions built for a fleet of passenger cars with the capacity functions calculated for different percentages of heavy vehicles. Differently from HCM 2010 which assumes a heavy vehicle to be equivalent to two passenger cars and sets as 2.0 the passenger car equivalents for heavy vehicles for roundabouts, a higher PCE effect would be expected on the quality of traffic conditions when the traffic stream contains a high number of heavy vehicles; this effect should be accounted for when calculating capacity and level-of-services.
|Number of pages||15|
|Journal||Expert Systems with Applications|
|Publication status||Published - 2017|
All Science Journal Classification (ASJC) codes
- General Engineering
- Computer Science Applications
- Artificial Intelligence