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Mobility impacts of early forms of automated driving – A system dynamic approach

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  • Puylaert, S.
  • Snelder, M.
  • van Nes, R.
  • van Arem, B.

Abstract

Modern cars are increasingly being equipped with automated driving functions. For governments it is important to gain insight in the mobility impacts of automated vehicles. This is important as the introduction of automated vehicles affects current investment decisions about infrastructure projects and other policy measures like road pricing. Quantitative literature with respect to the impact of automated vehicles focuses mostly on capacity implications. Literature about large scale mobility impacts is mainly qualitative. This paper introduces a System Dynamics model (SD-model) to quantitatively explore the impacts of early forms of automated vehicles (level 1, 2 and 3) on mobility. The model is explorative and can be used to evaluate different scenarios in a short time. This model is applied in a case study for the Netherlands to assess the impact of automated vehicles on mode choice, time of day choice and travel times on characteristic relations in the Netherlands. In contrast to other studies the SD-model is able to simulate the effects of AVs over time, can simulate mixed automated vehicle types and has a constant feedback between the assignment and the demand side of the model. A scenario for autonomous driving and a scenario for cooperative driving is considered. The simulations show that car traffic will increase and the level of congestion does not necessarily decrease and might even increase on some relations, especially in the autonomous scenario. Furthermore, in the cooperative scenario the increase in number of trips by car is larger, the average speeds are higher and there is less congestion compared to the autonomous scenario.

Suggested Citation

  • Puylaert, S. & Snelder, M. & van Nes, R. & van Arem, B., 2018. "Mobility impacts of early forms of automated driving – A system dynamic approach," Transport Policy, Elsevier, vol. 72(C), pages 171-179.
    • Handle: RePEc:eee:trapol:v:72:y:2018:i:c:p:171-179
    DOI: 10.1016/j.tranpol.2018.02.013
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. 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.
    3. Ngoduy, D. & Hoogendoorn, S.P. & Liu, R., 2009. "Continuum modeling of cooperative traffic flow dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2705-2716.
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    2. Yilun Chen & Nirajan Shiwakoti & Peter Stasinopoulos & Shah Khalid Khan, 2022. "State-of-the-Art of Factors Affecting the Adoption of Automated Vehicles," Sustainability, MDPI, vol. 14(11), pages 1-29, May.
    3. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    4. Jo, Young & Kim, Jungin & Oh, Cheol & Kim, Ikki & Lee, Gunwoo, 2019. "Benefits of travel time savings by truck platooning in Korean freeway networks," Transport Policy, Elsevier, vol. 83(C), pages 37-45.
    5. 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.

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