Performance Assessment of Long Combination Vehicles using Naturalistic Driving Data

Abstract

The deployment of long combination vehicles (LCVs) is currently in progress in Sweden. LCV refers to heavy vehicles that are longer than 25.25 m, which is the conventional length limit in Swedish regulations. LCVs reduce operational costs, improve fuel efficiency and reduce CO 2 emission per ton-km. Despite their numerous advantages, a question that still revolves around these vehicles is how they perform on the road. Although this question has been answered using simulations, an analysis using real traffic data is still missing. This thesis assesses the performance of LCVs using naturalistic driving data (NDD). The performance assessment is done using Performance-based standards (PBS) measures. PBS is a regulatory scheme for heavy vehicles, such as LCVs, that includes performance measures with a quantified required level of performance. The main PBS measures used in this thesis are rearward amplification, low-speed swept path, high-speed transient offtracking, and high-speed steady-state offtracking. Rearward amplification represents the amplification of motions in the rear end of a vehicle combination, which relates to its stability, and the remaining three are indicative of the space that the vehicles occupy in different scenarios. The steering reversal rate is also employed to compute the cognitive workload of the drivers in low-speed scenarios. Two LCV combinations are considered for analysis, namely an A-double composed of a tractor-semitrailer-dolly-semitrailer/tractor-semitrailer-full trailer and a DuoCAT composed of a truck hauling two centre-axle trailers. Four scenarios are of interest to this thesis: lane changes, manoeuvring through roundabouts, turning in intersections and negotiating tight curves. The thesis presents three contributions outlining the analysis methodologies, followed by a discussion of the results obtained from the analysis. The first contribution involves developing an algorithm to extract lane changes from the NDD of LCVs. The algorithm is used against the data obtained from A-doubles. The results indicate that A-doubles adhere to proposed safety limits during lane changes. The second