Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37083
Title: Pavement rutting performance analysis of automated vehicles: impacts of wander mode, lane width, and market penetration rate
Authors: YEGANEH, Ali 
VANDOREN, Bram 
PIRDAVANI, Ali 
Issue Date: 2022
Publisher: Taylor and Francis
Source: International Journal of Pavement Engineering, , p. 1 -18
Status: Early view
Abstract: The deployment of automated vehicles (AVs) with the gradual market penetration rate increase and different potential lateral movement patterns combined with the lane width effect would lead to different load distribution scenarios, impacting the pavement performance. This study compares pavement rutting damages induced by different load distribution scenarios by setting out different penetration rates (i.e. 0, 20, 40, 60, 80, and 100%), wander modes (i.e. zero-, normal-, uniform-time-, and uniform-frequency-wander), and lane widths (i.e. 3, 3.25, and 3.5 m). A finite element model of a full-depth flexible pavement was developed using ABAQUS software to evaluate the pavement rutting. The results showed that the significance level of differences between rutting damages induced by different wander modes and lane widths is substantially influenced by the AVs’ penetration rate. For instance, in the higher penetration rates, the differences between the rutting performance of different wander modes are more significant than in the lower penetration rates. Furthermore, the lane width effect becomes more significant in the segregated scenario than in the integrated scenarios in normal- and uniform-wander modes. Accordingly, AVs’ penetration rate is a decisive factor in the practical decision-making process in the wander mode determination and lane width design for AVs.
Keywords: Automated vehicle;pavement rutting performance;finite element model;lane width effect;penetration rate;wander effect
Document URI: http://hdl.handle.net/1942/37083
ISSN: 1029-8436
e-ISSN: 1477-268X
DOI: 10.1080/10298436.2022.2049264
ISI #: 000768097500001
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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