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Transportation

, Volume 46, Issue 6, pp 1975–1996 | Cite as

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

  • Florian Dandl
  • Michael Hyland
  • Klaus Bogenberger
  • Hani S. MahmassaniEmail author
Article

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.

Keywords

Autonomous vehicles Mobility service Autonomous mobility on-demand Demand forecasting Fleet operations 

Notes

Acknowledgements

Partial funding for the first author is provided by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety through the project “City2Share”. Partial funding for this work is provided through an Eisenhower Fellowship awarded by the US Department of Transportation to the second author. Additional funding is provided by the Northwestern University Transportation Center. The authors remain responsible for all findings and opinions presented in the paper.

Author contribution

All authors contributed to all aspects of the study from conception and design, to data collection, to analysis and interpretation of results, and manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Intelligent Transportation SystemsBundeswehr University MunichNeubibergGermany
  2. 2.Department of Civil and Environmental Engineering, Institute of Transportation StudiesUniversity of California-IrvineIrvineUSA
  3. 3.William A. Patterson Distinguished Chair in TransportationNorthwestern UniversityEvanstonUSA

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