Where Should Robotaxis Operate? Strategic Network Design for Autonomous Mobility-on-Demand

2026-02-22

AI Summary

This study addresses the problem of selecting operational subnetworks for autonomous mobility-on-demand (AMoD) systems in urban road networks, requiring joint optimization of infrastructure deployment and fleet scheduling to meet service quality requirements. The authors formulate subnetwork selection and passenger routing as a strategic network design problem and propose a path-based mixed-integer programming model. To efficiently solve large-scale instances, they develop a column generation algorithm. The key innovation lies in the first integrated optimization of AMoD infrastructure location and fleet control, supporting path-level constraints—such as turn restrictions—and robust optimization under box uncertainty. Experiments using real-world data from Manhattan demonstrate that the approach yields stable, interpretable operational subnetworks and effectively quantifies the trade-off between infrastructure investment and vehicle utilization time.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

The emergence of Autonomous Mobility-on-Demand (AMoD) services creates new opportunities to improve the efficiency and reliability of on-demand mobility systems. Unlike human-driven Mobility-on-Demand (MoD), AMoD enables fully centralized fleet control, but it also requires appropriate infrastructure, so that vehicles can operate safely only on a suitably instrumented subnetwork of the roads. Most existing AMoD research focuses on fleet control (matching, rebalancing, ridepooling) on a fixed road network and does not address the joint design of the service network and fleet capacity. In this paper, we formalize this strategic design problem as the Autonomous Mobility-on-Demand Network Design Problem (AMoD-NDP), in which an operator selects an operation subnetwork and routes all passengers, subject to infrastructure and fleet constraints and route-level quality-of-service requirements. We propose a path-based mixed-integer formulation of the AMoD-NDP and develop a column-generation-based algorithm that scales to city-sized networks. The master problem optimizes over a restricted set of paths, while the pricing problem reduces to an elementary shortest path with resource constraints, solved exactly by a tailored label-correcting algorithm. The method provides an explicit certificate of the optimality gap and extends naturally to a robust counterpart under box uncertainty in travel times and demand. Using real-world data from Manhattan, New York City, we show that the framework produces stable and interpretable operation subnetworks, quantifies trade-offs between infrastructure investment and fleet time, and accommodates additional path-level constraints, such as limits on left turns as a proxy for operational risk. These results illustrate how the proposed approach can support strategic planning and policy analysis for future AMoD deployments.

Problem

• Autonomous Mobility-on-Demand
• Network Design
• Operation Subnetwork
• Fleet Capacity
• Quality-of-Service

Innovation

• Autonomous Mobility-on-Demand
• Network Design
• Column Generation
• Robust Optimization
• Path-based Formulation