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Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets

Author

Listed:
  • Florian Dandl

    (Bundeswehr University Munich)

  • Michael Hyland

    (University of California-Irvine)

  • Klaus Bogenberger

    (Bundeswehr University Munich)

  • Hani S. Mahmassani

    (Northwestern University)

Abstract

Fleet operators rely on forecasts of future user requests to reposition empty vehicles and efficiently operate their vehicle fleets. In the context of an on-demand shared-use autonomous vehicle (AV) mobility service (SAMS), this study analyzes the trade-off that arises when selecting a spatio-temporal demand forecast aggregation level to support the operation of a SAMS fleet. In general, when short-term forecasts of user requests are intended for a finer space–time discretization, they tend to become less reliable. However, holding reliability constant, more disaggregate forecasts provide more valuable information to fleet operators. To explore this trade-off, this study presents a flexible methodological framework to evaluate and quantify the impact of spatio-temporal demand forecast aggregation on the operational efficiency of a SAMS fleet. At the core of the methodological framework is an agent-based simulation that requires a demand forecasting method and a SAMS fleet operational strategy. This study employs an offline demand forecasting method, and an online joint AV-user assignment and empty AV repositioning strategy. Using this forecasting method and fleet operational strategy, as well as Manhattan, NY taxi data, this study simulates the operations of a SAMS fleet across various spatio-temporal aggregation levels. Results indicate that as demand forecasts (and subregions) become more spatially disaggregate, fleet performance improves, in terms of user wait time and empty fleet miles. This finding comes despite demand forecast quality decreasing as subregions become more spatially disaggregate. Additionally, results indicate the SAMS fleet significantly benefits from higher quality demand forecasts, especially at more disaggregate levels.

Suggested Citation

  • Florian Dandl & Michael Hyland & Klaus Bogenberger & Hani S. Mahmassani, 2019. "Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets," Transportation, Springer, vol. 46(6), pages 1975-1996, December.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:6:d:10.1007_s11116-019-10007-9
    DOI: 10.1007/s11116-019-10007-9
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    References listed on IDEAS

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    3. Rajendran, Suchithra & Srinivas, Sharan, 2020. "Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    4. Konstanze Winter & Oded Cats & Karel Martens & Bart Arem, 2021. "Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking," Transportation, Springer, vol. 48(4), pages 1931-1965, August.
    5. Younghoon Seo & Donghyun Lim & Woongbee Son & Yeongmin Kwon & Junghwa Kim & Hyungjoo Kim, 2020. "Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    6. Fehn, Fabian & Engelhardt, Roman & Dandl, Florian & Bogenberger, Klaus & Busch, Fritz, 2023. "Integrating parcel deliveries into a ride-pooling service—An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    7. Chen, Yao & Liu, Yang & Bai, Yun & Mao, Baohua, 2024. "Real-time dispatch management of shared autonomous vehicles with on-demand and pre-booked requests," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    8. Papaix, Claire & Eranova, Mariya & Zhou, Li, 2023. "Shared mobility research: Looking through a paradox lens," Transport Policy, Elsevier, vol. 133(C), pages 156-167.
    9. Al-Kanj, Lina & Nascimento, Juliana & Powell, Warren B., 2020. "Approximate dynamic programming for planning a ride-hailing system using autonomous fleets of electric vehicles," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1088-1106.
    10. Hyland, Michael & Mahmassani, Hani S., 2020. "Operational benefits and challenges of shared-ride automated mobility-on-demand services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 251-270.
    11. Guo, Xiaotong & Caros, Nicholas S. & Zhao, Jinhua, 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 161-189.

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