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A quantitative analysis of potential impacts of automated vehicles in Austria using a dynamic integrated land use and transport interaction model

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  • Emberger, Guenter
  • Pfaffenbichler, Paul

Abstract

Digitisation and automation are expected to change the transport system and settlement structures in a disruptive way. Due to new developments in sensor and communication technology different business models of automated vehicles (AV) - such as private AV, car-sharing-AV, ride-sharing-AV and public transport-AV – are likely to enter the transport market. Further, different penetration rates of AVs, extension of user groups (elderly and young, people without driving licenses etc.), different cost scenarios of AV veh-kms, parking regimes/fees, etc. will have significant impact on future transport demand. The objective of the work presented in this paper was to develop a simulation-based approach to analyse the potential impacts of different vehicle automation scenarios in Austria.

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  • Emberger, Guenter & Pfaffenbichler, Paul, 2020. "A quantitative analysis of potential impacts of automated vehicles in Austria using a dynamic integrated land use and transport interaction model," Transport Policy, Elsevier, vol. 98(C), pages 57-67.
    • Handle: RePEc:eee:trapol:v:98:y:2020:i:c:p:57-67
    DOI: 10.1016/j.tranpol.2020.06.014
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    References listed on IDEAS

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    1. Paul Pfaffenbichler & Günter Emberger & Simon Shepherd, 2008. "The Integrated Dynamic Land Use and Transport Model MARS," Networks and Spatial Economics, Springer, vol. 8(2), pages 183-200, September.
    2. Wadud, Zia & MacKenzie, Don & Leiby, Paul, 2016. "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 1-18.
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    5. Correia, Gonçalo Homem de Almeida & van Arem, Bart, 2016. "Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 64-88.
    6. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
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    Cited by:

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    2. Wang, Song & Li, Zhixia & Wang, Yi & Wyatt, Daniel Aaron, 2024. "How effective is automated vehicle education? – A Kentucky case study revealing the dynamic nature of education effectiveness," Transport Policy, Elsevier, vol. 147(C), pages 140-157.
    3. You Kong & Jihong Ou & Longfei Chen & Fengchun Yang & Bo Yu, 2023. "The Environmental Impacts of Automated Vehicles on Parking: A Systematic Review," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    4. Xu, Yuanxian & Dong, Jianjun & Ren, Rui & Yang, Kai & Chen, Zhilong, 2022. "The impact of metro-based underground logistics system on city logistics performance under COVID-19 epidemic: A case study of Wuhan, China," Transport Policy, Elsevier, vol. 116(C), pages 81-95.
    5. Rubén Cordera & Soledad Nogués & Esther González-González & José Luis Moura, 2021. "Modeling the Impacts of Autonomous Vehicles on Land Use Using a LUTI Model," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    6. Liliana Andrei & Mihaela Hermina Negulescu & Oana Luca, 2022. "Premises for the Future Deployment of Automated and Connected Transport in Romania Considering Citizens’ Perceptions and Attitudes towards Automated Vehicles," Energies, MDPI, vol. 15(5), pages 1-23, February.
    7. Sarri, Paraskevi & Kaparias, Ioannis & Preston, John & Simmonds, David, 2023. "Using Land Use and Transportation Interaction (LUTI) models to determine land use effects from new vehicle transportation technologies; a regional scale of analysis," Transport Policy, Elsevier, vol. 135(C), pages 91-111.

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