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

TL;DR

A path-based mixed-integer formulation of the AMoD-NDP is proposed and a column-generation-based algorithm is developed that scales to city-sized networks and provides an explicit certificate of the optimality gap and extends naturally to a robust counterpart under box uncertainty in travel times and demand.

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.

Figures (5)

• Figure 1: -based algorithm to solve the based on path-based formulation. The algorithm relies on a -based decomposition that decomposes \ref{['form:path']} into a master and pricing problem. The master and pricing problem is solved iteratively until the convergence condition is satisfied.
• Figure 2: Example of solution under a fixed fleet-time and infrastructure budget. From left to right: base road network, optimal operation subnetwork, and optimal vehicle flow distribution on the operation subnetwork.
• Figure 3: Design solutions with one month of demand data. (a) Edge instrumentation frequency over 31 daily designs. (b) Robust operation subnetwork obtained from the robust under demand uncertainty.
• Figure 4: Sensitivity of normalized profit to fleet-time and infrastructure budget limits. The circled regions highlight regimes in which performance is limited by different constraints.
• Figure 5: Ratio of served demand (left) and profit (right) with and without the left-turn constraint, for different values of the left-turn budget $LT$. The box plots show variability across the 31 days of May 2024.

Theorems & Definitions (31)

• Definition 1: Base Road Network
• Definition 2: Demand
• Definition 3: Path
• Remark 1
• Definition 4: Demand-satisfactory Path
• Definition 5: AMoD--NDP
• Lemma 1: Irrelevance of intercepts
• proof
• Lemma 2: Equivalence without path constraints
• proof