Spatio-temporal Pricing and Fleet Management under Mixed Autonomy

Ignacio Úbeda () and Gunther Gust ()
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Ignacio Úbeda: University of Würzburg, Center for Artificial Intelligence and Data Science (CAIDAS)
Gunther Gust: University of Würzburg, Center for Artificial Intelligence and Data Science (CAIDAS)

A chapter in Artificial Intelligence, Data, and Decision-Making, 2026, pp 363-372 from Springer

Abstract: Abstract Mobility-on-Demand services will soon have the opportunity to integrate autonomous vehicles into their operations. During the transition towards full automation, MoD systems will operate a mixed fleet, where human-driven (HVs) and autonomous vehicles (AVs) coexist. In such scenarios, the AVs fully complies with the operator’s decisions while HVs must be incentivized through wages–i.e. the size and locational distribution of the human fleet is impacted by the wage decisions of the operator. In this paper, we present a data-driven information system to manage such mixed fleets. The system decides on trip prices, relocation of AVs, and wages of the HVs. Thereby, it takes into account the effect of wage decisions on the behavior of HVs. Validation of the system is conducted through a case study based on observed trip demand data in New York City. Our study demonstrates the importance of accounting for wage-dependent HVs’ supply in this new scenario.

Keywords: mobility-on-demand; autonomous vehicles; mixed fleet; spatio-temporal pricing; model predictive control (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08480-4_23

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DOI: 10.1007/978-3-032-08480-4_23

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