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Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications

Author

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  • Andrea Papu Carrone

    (Technical University of Denmark)

  • Jeppe Rich

    (Technical University of Denmark)

  • Christian Anker Vandet

    (Technical University of Denmark)

  • Kun An

    (Tongji University)

Abstract

In upcoming years, the introduction of autonomous vehicles (AVs) will reshape the transport system. The transition from a regular to an autonomous transport system, however, will take place over many years and lead to a long period with a mixed driving environment where AVs and regular vehicles (RVs) operate side by side. The purpose of this study is to investigate how the utilisation of the road capacity degrades as a function of heterogeneity in congested motorways. The analysis is based on a dedicated traffic simulator, which enables the investigation of complex dynamic spillback from congestion while allowing for different degrees of heterogeneity. The representation of autonomous vehicles is based on a modified intelligent driver model (IIDM) presented by Treiber et al. (Phys Rev E 62(2):1805–1824, 2000) and Treiber and Kesting (Traffic flow dynamics, Springer, Heidelberg, 2013), while the behaviour of drivers of RVs relies on a stochastic version of the IIDM. Three main conclusions stand out. Firstly, it is shown that in an idealised environment in which AVs operate alone, a substantially improved capacity utilisation can be attained. Secondly, when drivers of RVs are mixed with AVs, capacity utilisation degrades very fast as a function of the share of RVs. Thirdly, it is shown that the improved capacity utilisation of AVs comes in the form of reduced travel time and increased throughput, with indications that travel time reductions are the most important. From a strategical planning perspective, the results underline that dedicated lanes are preferable to attain the positive effects of AVs. Specifically, we compare a stylised situation with three lanes with a share of 33% AVs to a situation with two regular lanes and a single dedicated AV lane. The latter represents a tripling in consumer surplus all other things being equal.

Suggested Citation

  • Andrea Papu Carrone & Jeppe Rich & Christian Anker Vandet & Kun An, 2021. "Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications," Transportation, Springer, vol. 48(6), pages 2907-2938, December.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:6:d:10.1007_s11116-020-10154-4
    DOI: 10.1007/s11116-020-10154-4
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    References listed on IDEAS

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    1. Prateek Bansal & Kara M. Kockelman, 2018. "Are we ready to embrace connected and self-driving vehicles? A case study of Texans," Transportation, Springer, vol. 45(2), pages 641-675, March.
    2. Mustapha Harb & Yu Xiao & Giovanni Circella & Patricia L. Mokhtarian & Joan L. Walker, 2018. "Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment," Transportation, Springer, vol. 45(6), pages 1671-1685, November.
    3. Robert E. Chandler & Robert Herman & Elliott W. Montroll, 1958. "Traffic Dynamics: Studies in Car Following," Operations Research, INFORMS, vol. 6(2), pages 165-184, April.
    4. Zhu, Wen-Xing & Zhang, H.M., 2018. "Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 274-285.
    5. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
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    Cited by:

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    3. Chen, Yingda & Li, Keping & Zhang, Lun & Chen, Yili & Xiao, Xue, 2024. "Modeling and analysis of mixed traffic flow capacity and stability considering human-driven vehicle drivers' trust attitude towards intelligent connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    5. Ron Yang, 2022. "(Don’t) Take Me Home: Home Preference and the Effect of Self-Driving Trucks on Interstate Trade," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    6. Dilshad Mohammed & Balázs Horváth, 2024. "Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption," Energies, MDPI, vol. 17(7), pages 1-19, March.
    7. Haokun Song & Fuquan Zhao & Guangyu Zhu & Haoyi Zhang & Zongwei Liu, 2024. "Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario," Sustainability, MDPI, vol. 16(10), pages 1-31, May.
    8. Sajjad Shafiei & Ziyuan Gu & Hanna Grzybowska & Chen Cai, 2023. "Impact of self-parking autonomous vehicles on urban traffic congestion," Transportation, Springer, vol. 50(1), pages 183-203, February.

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