Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Seo, Toru; Asakura, Yasuo · 2021 · OpenAlex-citations

DOI: 10.1109/tits.2021.3071512

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Summary

This paper addresses the strategic planning of Shared Autonomous Vehicle (SAV) systems by proposing a unified multi-objective optimization framework that explicitly integrates operational dynamics with infrastructure design. Existing literature often treats strategic decisions (e.g., fleet sizing, road network design) and operational tasks (e.g., routing, ridesharing) separately, failing to capture their interdependencies. The authors argue that a unified approach is necessary to investigate trade-offs between user-side costs (travel time) and system-side costs (operational expenses, infrastructure investment), particularly given the potential for SAVs to reduce vehicle counts and infrastructure requirements through efficient routing and driver-less parking. The study formulates the problem as a Multi-Objective Linear Programming (MOOP) model based on macroscopic dynamic traffic assignment (DTA). The model simultaneously minimizes four objective functions: total traveler travel time, total distance traveled by SAVs, total fleet size, and infrastructure construction cost. Decision variables include SAV routing, passenger assignment, fleet size, link capacity, and node storage capacity. The framework utilizes a time-expanded network to model dynamic flows, incorporating constraints for passenger capacity, traffic congestion, and queue lengths. This linear formulation allows for computationally efficient solutions using standard methods like the weighted sum approach, avoiding the high computational costs associated with mixed-integer programming or agent-based simulations. A key theoretical contribution is the mathematical proof that introducing ridesharing weakly monotonically decreases both user-side and system-side costs. While ridesharing typically increases travel time due to detours in conventional models, this study demonstrates that under optimal SAV operation, the reduction in congestion and fleet size can offset these detours, leading to simultaneous improvements in all objective functions. The model explicitly accounts for empty vehicle travel, ridesharing detours, and passenger waiting times within the DTA framework. The proposed model was evaluated using actual travel records from New York City taxi data. The results validate the model’s ability to serve as a benchmark for strategic planning, providing Pareto efficient solutions that illustrate the trade-offs between different performance metrics. The study concludes that the linear MOOP framework offers a tractable and rigorous method for designing SAV systems, enabling planners to balance social welfare, operational efficiency, and infrastructure costs. This approach facilitates informed decision-making for public or private operators by quantifying the benefits of ridesharing and optimized infrastructure deployment.

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