Yao, Y, Carsten, O orcid.org/0000-0003-0285-8046 and Hibberd, D (2020) Predicting Compliance with Speed Limits using Speed Limit Credibility Perception and Risk Perception Data. Transportation Research Record, 2674 (9). pp. 450-461. ISSN 0361-1981
Abstract
The link between attitudes and behavior shows that driving behavior can be predicted by personal characteristics and individual attitudes, as has been shown in previous research. This study aimed to predict the level of compliance with speed limits by individual drivers by using attitudes data including speed limit credibility perception and risk perception on eight rural single carriageway layouts. This study investigated how the road layout and roadside environment affect speed limit credibility perception and risk perception, and investigated which machine learning algorithm can be used to predict driving behavior based on experimental evidence. This study was carried out in a well-controlled experimental design by using a questionnaire and a driving simulator. The simulated road environment only considered rural single carriageway which has higher risk factors than other road types. The results show that a boosted decision tree algorithm can establish a driving behavior model based on drivers’ credibility perception and risk perception. This result can be used to predict driving behavior in advance for in-vehicle warning system design.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, SAGE Publications. This is an author produced version of an article published in Transportation Research Record. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Apr 2021 14:42 |
Last Modified: | 09 Apr 2021 09:07 |
Status: | Published |
Publisher: | SAGE Publications |
Identification Number: | 10.1177/0361198120929696 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172788 |