Equilibrium analysis of morning commuting and parking under spatial capacity allocation in the autonomous vehicle environment

Xiang Zhang; Wei Liu; Michael Levin; S. Travis Waller

doi:10.1016/j.tre.2023.103071

Equilibrium analysis of morning commuting and parking under spatial capacity allocation in the autonomous vehicle environment

Xiang Zhang
, Wei Liu
, Michael Levin
, S. Travis Waller

Civil, Environmental, and Geo- Engineering

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

Abstract

This study analytically investigates the morning commuting and parking patterns of autonomous vehicles (AVs) under different spatial road capacity allocation schemes (i.e., capacity split between inbound and outbound travel directions). Given that self-driving AV might park far away from commuters’ destination, we investigate equilibrium departure/arrival and parking patterns for AVs subject to the spatial road capacity allocation. We also analyse the system optimum traffic pattern for AV morning commute under a given capacity allocation scheme. Furthermore, we examine optimal capacity allocation strategies under user equilibrium and system optimum AV traffic patterns, respectively, which aim to minimise the total system travel cost. Numerical studies are conducted to illustrate the model and analysis. The results reveal the sensitivity of different efficiency metrics with respect to AV parking supply and road capacity allocation schemes, and provide insights into the infrastructure management with future automated transport.

Original language	English (US)
Article number	103071
Journal	Transportation Research Part E: Logistics and Transportation Review
Volume	172
DOIs	https://doi.org/10.1016/j.tre.2023.103071
State	Published - Apr 2023

Bibliographical note

Funding Information:
We would like to thank the reviewers very much for their useful comments, which helped improve this paper substantially. This study is supported by the National Natural Science Foundation of China (Grant No. 52202380) and the Australian Research Council (Discovery Projects, DP19010287).

Publisher Copyright:
© 2023 Elsevier Ltd

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

Autonomous vehicles
Bottleneck model
Morning commute
Spatial capacity allocation
System optimum
User equilibrium

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10.1016/j.tre.2023.103071

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title = "Equilibrium analysis of morning commuting and parking under spatial capacity allocation in the autonomous vehicle environment",

abstract = "This study analytically investigates the morning commuting and parking patterns of autonomous vehicles (AVs) under different spatial road capacity allocation schemes (i.e., capacity split between inbound and outbound travel directions). Given that self-driving AV might park far away from commuters{\textquoteright} destination, we investigate equilibrium departure/arrival and parking patterns for AVs subject to the spatial road capacity allocation. We also analyse the system optimum traffic pattern for AV morning commute under a given capacity allocation scheme. Furthermore, we examine optimal capacity allocation strategies under user equilibrium and system optimum AV traffic patterns, respectively, which aim to minimise the total system travel cost. Numerical studies are conducted to illustrate the model and analysis. The results reveal the sensitivity of different efficiency metrics with respect to AV parking supply and road capacity allocation schemes, and provide insights into the infrastructure management with future automated transport.",

keywords = "Autonomous vehicles, Bottleneck model, Morning commute, Spatial capacity allocation, System optimum, User equilibrium",

author = "Xiang Zhang and Wei Liu and Michael Levin and \{Travis Waller\}, S.",

note = "Funding Information: We would like to thank the reviewers very much for their useful comments, which helped improve this paper substantially. This study is supported by the National Natural Science Foundation of China (Grant No. 52202380) and the Australian Research Council (Discovery Projects, DP19010287). Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

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doi = "10.1016/j.tre.2023.103071",

language = "English (US)",

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AU - Zhang, Xiang

AU - Liu, Wei

AU - Levin, Michael

AU - Travis Waller, S.

N1 - Funding Information: We would like to thank the reviewers very much for their useful comments, which helped improve this paper substantially. This study is supported by the National Natural Science Foundation of China (Grant No. 52202380) and the Australian Research Council (Discovery Projects, DP19010287). Publisher Copyright: © 2023 Elsevier Ltd

PY - 2023/4

Y1 - 2023/4

N2 - This study analytically investigates the morning commuting and parking patterns of autonomous vehicles (AVs) under different spatial road capacity allocation schemes (i.e., capacity split between inbound and outbound travel directions). Given that self-driving AV might park far away from commuters’ destination, we investigate equilibrium departure/arrival and parking patterns for AVs subject to the spatial road capacity allocation. We also analyse the system optimum traffic pattern for AV morning commute under a given capacity allocation scheme. Furthermore, we examine optimal capacity allocation strategies under user equilibrium and system optimum AV traffic patterns, respectively, which aim to minimise the total system travel cost. Numerical studies are conducted to illustrate the model and analysis. The results reveal the sensitivity of different efficiency metrics with respect to AV parking supply and road capacity allocation schemes, and provide insights into the infrastructure management with future automated transport.

AB - This study analytically investigates the morning commuting and parking patterns of autonomous vehicles (AVs) under different spatial road capacity allocation schemes (i.e., capacity split between inbound and outbound travel directions). Given that self-driving AV might park far away from commuters’ destination, we investigate equilibrium departure/arrival and parking patterns for AVs subject to the spatial road capacity allocation. We also analyse the system optimum traffic pattern for AV morning commute under a given capacity allocation scheme. Furthermore, we examine optimal capacity allocation strategies under user equilibrium and system optimum AV traffic patterns, respectively, which aim to minimise the total system travel cost. Numerical studies are conducted to illustrate the model and analysis. The results reveal the sensitivity of different efficiency metrics with respect to AV parking supply and road capacity allocation schemes, and provide insights into the infrastructure management with future automated transport.

KW - Autonomous vehicles

KW - Bottleneck model

KW - Morning commute

KW - Spatial capacity allocation

KW - System optimum

KW - User equilibrium

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DO - 10.1016/j.tre.2023.103071

M3 - Article

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SN - 1366-5545

VL - 172

JO - Transportation Research Part E: Logistics and Transportation Review

JF - Transportation Research Part E: Logistics and Transportation Review

M1 - 103071

ER -

Equilibrium analysis of morning commuting and parking under spatial capacity allocation in the autonomous vehicle environment

Abstract

Bibliographical note

UN SDGs

Keywords

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