Modeling the joint choice behavior of commuters’ travel mode and parking options for private autonomous vehicles

Fei Xue, Enjian Yao, Elisabetta Cherchi, Gonçalo Homem de Almeida Correia

Research output: Contribution to journalArticlepeer-review


Difficulty in finding parking spaces and high parking fees discourage private car usage. Fully autonomous vehicles (AVs) capable of self-parking away from destinations will likely remove this barrier. Despite extensive survey-based research on AVs in recent years, existing literature has not sufficiently addressed the potential impact of new parking options on the demand for these vehicles. This study explores commuters’ joint choice of travel mode and parking for private autonomous vehicles (PAVs). To this end, a stated choice (SC) experiment was designed and deployed in the city of Beijing, China. Attitudinal statements were also designed to measure four latent variables: perceived ease of use, perceived usefulness, perceived safety, and attitude toward waiting. Using a hybrid choice model framework, the estimation results reveal that the choice of letting the PAV self-park at a non-destination location is significantly influenced by the location of such parking, the potential delay in re-taking the vehicle, and the fuel/energy consumption to and from the non-destination parking place. Attitudes toward AVs also play a crucial role, with perceived safety and perceived usefulness having the greatest impact. Our results can help managers and planners understand how PAVs affect people's travel mode choices and the corresponding parking options and assist them in developing strategies in preparation for the widespread use of AVs.

Original languageEnglish (US)
Article number104471
JournalTransportation Research Part C: Emerging Technologies
StatePublished - Feb 2024


  • Attitudes
  • Automated vehicles
  • Hybrid choice model
  • Parking
  • Stated choice experiment
  • Willingness to pay

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Management Science and Operations Research


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