Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/34259
Title: Impacts of load distribution and lane width on pavement rutting performance for automated vehicles
Authors: YEGANEH, Ali 
VANDOREN, Bram 
PIRDAVANI, Ali 
Issue Date: 2021
Publisher: Taylor & Francis
Source: International Journal of Pavement Engineering, 23 (12) , p. 4125-4135
Abstract: Over the recent years, considerable attention has been drawn to intelligent driving technologies and particularly to automated vehicles (AVs). The deployment of AVs would provide the opportunity to have more control over the dynamics of the vehicle, including its lateral movement, which can affect the pavement’s long-term rutting performance. The controlled lateral movement of the AVs may also imply a reduced lane width. This paper evaluates the impacts of dedicating a reduced lane width to AVs on pavement rutting performance, considering two lateral movement modes for AVs; zero wander and uniform-wander. A finite element model was developed using ABAQUS software. The rutting simulation results of this study showed that the abrupt changes in the loading schemes of the zero- and uniform-wander modes cause considerable accumulated rutting in the edges of the loading areas. This is significantly greater than the total rutting induced by the human-driven vehicles (HDVs) following the normal-wander mode, which causes a compensated rutting behaviour by a gradual increase in loading time. Furthermore, the comparison between rutting depths in different lane widths reveals that when dedicating the narrower lane for AVs with a uniform-wander distribution, the pavement’s total rutting depth would remarkably increase compared to the wider lanes.
Keywords: Automated vehicle;pavement rutting performance;wander effect;finite element model;lane width
Document URI: http://hdl.handle.net/1942/34259
ISSN: 1029-8436
e-ISSN: 1477-268X
DOI: 10.1080/10298436.2021.1935938
ISI #: 000658558200001
Rights: 2021 Informa UK Limited, trading as Taylor & Francis Group
Category: A1
Type: Journal Contribution
Validations: ecoom 2022
Appears in Collections:Research publications

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