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Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

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  • Dilshad Mohammed

    (Department of Civil Engineering, University of Duhok, Duhok 42001, Iraq
    Department of Transport, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary
    These authors contributed equally to this work.)

  • Balázs Horváth

    (Department of Transport, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary
    These authors contributed equally to this work.)

Abstract

As autonomous vehicles (AVs) continue to evolve and approach widespread adoption in the near future, the touted benefits of improved fuel efficiency at an individual level come under scrutiny when considering the overall impact on fuel consumption. This research delves into the paradoxical relationship between the promising technology of AVs, their impact on traffic capacities, travel demand, and the subsequent influence on aggregate fuel consumption. While AVs have demonstrated enhanced fuel efficiency when considered as a singular mode of transportation, our study reveals a contrasting trend when scaled to a broader societal context. Through comprehensive analysis of the literature, we discovered that, at lower limits of energy savings achievable by a single AV, the overall fuel consumption increases by a staggering 42% compared to conventional human-driven vehicles. This counterintuitive outcome is a result of the aggregate effect of increased AV usage, leading to higher traffic volumes and travel demands. Conversely, at higher thresholds of energy savings by individual AVs, the percentage of fuel consumption increment diminishes, but remains notable. Even with advanced energy-saving features, the overall fuel quantity still experiences a substantial 30% increase compared to conventional vehicles when scaled up to widespread AV use. Our findings emphasize the importance of considering the holistic impact of AVs on transportation systems and energy consumption. As society transitions towards AV-dominated traffic, policymakers and stakeholders must address the challenges associated with increased travel demand, potential traffic congestion, and the resultant implications on fuel consumption.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1589-:d:1364303
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    References listed on IDEAS

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    1. Almlöf, Erik & Nybacka, Mikael & Pernestål, Anna & Jenelius, Erik, 2022. "Will leisure trips be more affected than work trips by autonomous technology? Modelling self-driving public transport and cars in Stockholm, Sweden," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 1-19.
    2. Sebastian Sigle & Robert Hahn, 2023. "Energy Assessment of Different Powertrain Options for Heavy-Duty Vehicles and Energy Implications of Autonomous Driving," Energies, MDPI, vol. 16(18), pages 1-20, September.
    3. Chen Wang & Yulu Dai & Jingxin Xia, 2020. "A CAV Platoon Control Method for Isolated Intersections: Guaranteed Feasible Multi-Objective Approach with Priority," Energies, MDPI, vol. 13(3), pages 1-16, February.
    4. Dilshad Mohammed & Balázs Horváth, 2023. "Travel Demand Increment Due to the Use of Autonomous Vehicles," Sustainability, MDPI, vol. 15(11), pages 1-20, June.
    5. Samantha Heiberg & Emily Emond & Cody Allen & Dheeraj Raya & Venkataramana Gadhamshetty & Saurabh Sudha Dhiman & Achyuth Ravilla & Ilke Celik, 2023. "Environmental Impact Assessment of Autonomous Transportation Systems," Energies, MDPI, vol. 16(13), pages 1-13, June.
    6. 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.
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