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Title:

Comparison of Speed-Density Models in the Age of Connected and Automated Vehicles

Accession Number:

01857073

Record Type:

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Order URL: http://worldcat.org/issn/03611981

Abstract:

Fundamental diagrams (FDs) present the relationship between flow, speed, and density, and give some valuable information about traffic features such as capacity, congested and uncongested situations, and so forth. On the other hand, high accuracy speed-density models can produce more efficient FDs. Although numerous speed-density models are presented in the literature, there are very few models for connected and autonomous vehicles (CAVs). One of the recent spend-density models that takes into account the penetration rate of CAVs is provided by Lu et al. However, the estimation power of this model has not been tested against other speed-density models, and it has not been applied to high-speed networks such as freeways. Thus, this paper made a comparison between the Lu speed-density model and a well-known speed-density model (Papageorgiou) in freeway and grid networks. Different CAV behaviors (aggressive, normal, and conservative) are evaluated in this comparison. The comparison has been made between two speed-density models using the mean absolute percentage error (MAPE) and a t-test. The MAPE and t-test results show that differences between the two speed-density models are not significant in two case studies and that Lu is a powerful speed-density model to estimate speed compared with a well-known speed-density model. For the sake of comparing the above-mentioned models, this paper investigates the impact of CAVs on capacity based on FDs. The results suggest that the magnitude of the impacts of CAVs on road capacity (capacity increment percentage) which are obtained from two speed-density models are very close to each other. Also, the extent to which CAVs affect road capacity is highly dependent on their behavior.

Supplemental Notes:

Behzad Bamdad Mehrabani https://orcid.org/0000-0001-8585-7879© National Academy of Sciences: Transportation Research Board 2022.

Language:

English

Authors:

Karbasi, Amir Hossein
Mehrabani, Behzad Bamdad

ORCID 0000-0001-8585-7879

Cools, Mario

ORCID 0000-0003-3098-2693

Sgambi, Luca
Saffarzadeh, Mahmoud

Pagination:

pp 849-865

Publication Date:

2023-3

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2677
Issue Number: 3
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Web

Features:

References (33)

Subject Areas:

Highways; Operations and Traffic Management

Files:

TRIS, TRB, ATRI

Created Date:

Aug 30 2022 3:03PM