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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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 languageEnglish (US)
Article number103071
JournalTransportation Research Part E: Logistics and Transportation Review
StatePublished - 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


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


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