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Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore

Author

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  • Oh, Simon
  • Seshadri, Ravi
  • Azevedo, Carlos Lima
  • Kumar, Nishant
  • Basak, Kakali
  • Ben-Akiva, Moshe

Abstract

The advent of autonomous vehicle technologies and the emergence of new ride-sourcing business models has spurred interest in Automated Mobility-on-Demand (AMOD) as a prospective solution to meet the challenges of urbanization. AMOD has the potential of providing a convenient, reliable and affordable mobility service through more competitive cost structures enabled by autonomy (relative to existing services) and more efficient centralized fleet operations. However, the short and medium-term impacts of AMOD are as yet uncertain. On the one hand, it has the potential to alleviate congestion through increased ride-sharing and reduced car-ownership, and by complementing mass-transit. Conversely, AMOD may in fact worsen congestion due to induced demand, the cannibalization of public transit shares, and an increase in Vehicle-Kilometers Traveled (VKT) because of rebalancing and empty trips. This study attempts to systematically examine the impacts of AMOD on transportation in Singapore through agent-based simulation, modeling demand, supply and their interactions explicitly. On the demand side, we utilize an activity-based model system, that draws on data from a smartphone-based stated preferences survey conducted in Singapore. On the supply side, we model the operations of the AMOD fleet (including the assignment of requests to vehicles and rebalancing), which are integrated within a multimodal mesoscopic traffic simulator. Comprehensive simulations are conducted using a model of Singapore for the year 2030 and yield insights into the impacts of AMOD in dense transit-dependent cities from the perspective of the transportation planner, fleet operator, and user. The findings suggest that an unregulated introduction of AMOD can cause significant increases in network congestion and VKT, and have important policy implications that could potentially inform future deployments of AMOD.

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  • Oh, Simon & Seshadri, Ravi & Azevedo, Carlos Lima & Kumar, Nishant & Basak, Kakali & Ben-Akiva, Moshe, 2020. "Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 367-388.
    • Handle: RePEc:eee:transa:v:138:y:2020:i:c:p:367-388
    DOI: 10.1016/j.tra.2020.06.004
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    References listed on IDEAS

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    7. Aggelos Soteropoulos & Martin Berger & Mathias Mitteregger, 2021. "Compatibility of Automated Vehicles in Street Spaces: Considerations for a Sustainable Implementation," Sustainability, MDPI, vol. 13(5), pages 1-32, March.
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    10. 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).
    11. Mori, Kentaro & Miwa, Tomio & Abe, Ryosuke & Morikawa, Takayuki, 2022. "Equilibrium analysis of trip demand for autonomous taxi services in Nagoya, Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 476-498.
    12. Jihane El Ouadi & Hanae Errousso & Nicolas Malhene & Siham Benhadou, 2022. "On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 3893-3932, December.
    13. Di Ao & Jing Gao & Zhijie Lai & Sen Li, 2021. "Regulating Transportation Network Companies with a Mixture of Autonomous Vehicles and For-Hire Human Drivers," Papers 2112.07218, arXiv.org, revised Dec 2023.
    14. Borge-Diez, David & Icaza, Daniel & Açıkkalp, Emin & Amaris, Hortensia, 2021. "Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share," Energy, Elsevier, vol. 237(C).
    15. Giovanni Calabro' & Andrea Araldo & Simon Oh & Ravi Seshadri & Giuseppe Inturri & Moshe Ben-Akiva, 2021. "Adaptive Transit Design: Optimizing Fixed and Demand Responsive Multi-Modal Transportation via Continuous Approximation," Papers 2112.14748, arXiv.org, revised Jan 2023.
    16. Peiyu Jing & Ravi Seshadri & Takanori Sakai & Ali Shamshiripour & Andre Romano Alho & Antonios Lentzakis & Moshe E. Ben-Akiva, 2023. "Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation," Papers 2305.07318, arXiv.org.

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