Examining parking choices of connected and autonomous vehicles
File(s)
Author(s)
Type
Journal Article
Abstract
Raising parking charges is a measure that restricts the use of private vehicles. With the introduction of connected and autonomous vehicles (CAVs), the demand for parking has the potential to reduce as CAVs may not park at ‘pay to park’ areas as they are able to “cruise” or return home. However, it might not be financially feasible for them to return to their origin if the destination region is far away. Therefore, the question is: how could we develop parking policies in the CAVs era? To determine the best parking strategy for CAVs, four scenarios were tested in this paper: (i) enter and park within the destination area, (ii) enter, drop off, and return to the origin, (iii) enter, drop off, and return to outside parking and (iv) enter and drive around. Since real-world parking demand data for CAVs are not available, a simulation model of the road network in Santander (Spain) was employed to collect data on both CAV operations (e.g., conservative versus aggressive behaviors) and parking choices. Multinomial logistic regression model was used to identify the best parking option for CAVs. Performance indicators such as traffic, emissions, and safety were employed to compare the performance of a range of parking alternatives. It was found that the balanced scenario (i.e., combination of all parking choices) performs better with the greatest change in delay (around 32%). With 100% CAV market penetration, traffic crashes were reduced by 67%. This study will help local authorities formulate parking policies so that CAVs can park efficiently.
Date Issued
2023-11
Date Acceptance
2023-05-01
Citation
Transportation Research Record, 2023, 2677 (11), pp.589-601
ISSN
0361-1981
Publisher
SAGE Publications
Start Page
589
End Page
601
Journal / Book Title
Transportation Research Record
Volume
2677
Issue
11
Copyright Statement
© National Academy of Sciences:
Transportation Research Board 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 Lficense (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Transportation Research Board 2023. This article is distributed under the terms of the Creative Commons Attribution 4.0 Lficense (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000984184500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
automated
CHILD PEDESTRIAN INJURY
connected
Engineering
Engineering, Civil
management
MODEL
operations
parking rationing
planning and analysis
SAFETY
Science & Technology
Technology
Transportation
Transportation Science & Technology
vehicles
Publication Status
Published
Date Publish Online
2023-05-03