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Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040

Author

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  • Bat-Hen Nahmias-Biran

    (The Porter School of the Environment and Earth Sciences, Tel Aviv University, Ramat Aviv 6997801, Israel)

  • Gabriel Dadashev

    (Department of Applied Mathematics, Ariel University, Ariel 4070000, Israel)

  • Yedidya Levi

    (Department of Civil Engineering, Ariel University, Ariel 4070000, Israel)

Abstract

The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions.

Suggested Citation

  • Bat-Hen Nahmias-Biran & Gabriel Dadashev & Yedidya Levi, 2023. "Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040," Sustainability, MDPI, vol. 15(22), pages 1-19, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16037-:d:1281979
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    References listed on IDEAS

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    1. Yutong Cai & Hua Wang & Ghim Ping Ong & Qiang Meng & Der-Horng Lee, 2019. "Investigating user perception on autonomous vehicle (AV) based mobility-on-demand (MOD) services in Singapore using the logit kernel approach," Transportation, Springer, vol. 46(6), pages 2063-2080, December.
    2. repec:cdl:itsdav:qt82w2z91j is not listed on IDEAS
    3. repec:cdl:uctcwp:qt0cg1r4nq is not listed on IDEAS
    4. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    5. Bösch, Patrick M. & Becker, Felix & Becker, Henrik & Axhausen, Kay W., 2018. "Cost-based analysis of autonomous mobility services," Transport Policy, Elsevier, vol. 64(C), pages 76-91.
    6. Nguyen-Phuoc, Duy Q. & Zhou, Meng & Hong Chua, Ming & Romano Alho, André & Oh, Simon & Seshadri, Ravi & Le, Diem-Trinh, 2023. "Examining the effects of Automated Mobility-on-Demand services on public transport systems using an agent-based simulation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
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    1. Agriesti, Serio & Roncoli, Claudio & Nahmias-Biran, Bat-hen, 2025. "A simulation-based framework for quantifying potential demand loss due to operational constraints in automated mobility services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).

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