Implementation-Based Incentive Design for Autonomous Mobility-on-Demand and Transit Systems
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 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.
Related
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BibTeX
@misc{li2026implementationbased,
title = {{Implementation-Based Incentive Design for Autonomous Mobility-on-Demand and Transit Systems}},
author = {Xinling Li and Runyu Zhang and Gioele Zardini},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2605.18687},
eprint = {2605.18687},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.18687},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Implementation-Based Incentive Design for Autonomous Mobility-on-Demand and Transit Systems
AU - Xinling Li
AU - Runyu Zhang
AU - Gioele Zardini
PY - 2026
DA - 2026/11/06
PB - arXiv
DO - 10.48550/arxiv.2605.18687
UR - https://arxiv.org/abs/2605.18687
AB - 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.
ER -