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Research PaperResearchia:202605.19031

Implementation-Based Incentive Design for Autonomous Mobility-on-Demand and Transit Systems

Xinling Li

Abstract

Achieving a socially desirable operating point for a multimodal transportation system is challenging when Autonomous Mobility-on-Demand (AMoD) and Public Transit (PT) operators pursue selfish objectives alongside endogenous passenger choices. Existing equilibrium-based regulation models typically search over municipal policies to predict the induced operator equilibrium, creating strong behavioral assumptions, equilibrium-selection issues, and difficult bilevel optimization problems. This paper ...

Submitted: May 19, 2026Subjects: Mathematics; Mathematics

Description / Details

Achieving a socially desirable operating point for a multimodal transportation system is challenging when Autonomous Mobility-on-Demand (AMoD) and Public Transit (PT) operators pursue selfish objectives alongside endogenous passenger choices. Existing equilibrium-based regulation models typically search over municipal policies to predict the induced operator equilibrium, creating strong behavioral assumptions, equilibrium-selection issues, and difficult bilevel optimization problems. This paper proposes an implementation-based alternative. Rather than asking which municipal action induces the best equilibrium, we ask: given a target operating profile, what minimum realized transfer makes unilateral deviation unattractive for each operator? Using k-implementation theory, this payment decomposes into two unilateral deviation gains: one for the AMoD operator and one for PT. Calculating this payment requires computing three distinct objects: the social target, the AMoD best response, and the PT best response. This is nontrivial because each represents a large-scale network optimization problem complicated by endogenous mode choice and congestion. To address this, we develop tailored mathematical formulations and algorithms for each oracle. For the social target, we derive a decomposition and entropy-regularized mixed-integer convex formulation balancing social optimality and implementability. For the AMoD oracle, we derive an exact reformulation, a convex relaxation providing a global upper bound, and a sequential convex approximation for feasible lower bounds. For the PT oracle, we develop a mixed-integer convex relaxation and characterize its exactness condition. A NYC case study shows the framework computes tight implementation-payment bounds and reveals how the dominant source of incentive misalignment shifts with congestion.


Source: arXiv:2605.18687v1 - http://arxiv.org/abs/2605.18687v1 PDF: https://arxiv.org/pdf/2605.18687v1 Original Link: http://arxiv.org/abs/2605.18687v1

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Date:
May 19, 2026
Topic:
Mathematics
Area:
Mathematics
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