Abstract
This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs’ dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.
Highlights
S HARED autonomous vehicle (SAV) systems may be an efficient transportation mode in the future [1], [2]
The objective functions of the multi-objective optimization problems (MOOP) are total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; both of the user-side cost and the system- or social-side cost are taken into account
The results might not be directly useful for operational decision making of SAV systems; instead, they can be useful for strategic decision making
Summary
S HARED autonomous vehicle (SAV) systems may be an efficient transportation mode in the future [1], [2]. In long-term strategic levels, fleet sizing [1], [3]–[5], road network design and autonomous vehicle lane deployment [6], and parking space allocation [7] need to be solved These problems are important as their solution may have strong impact to the entire society. This study proposes a unified MOOP framework for strategic planning of SAV systems (e.g., fleet sizing, infrastructure design/update) that explicitly and endogenously considers dynamic operational aspects of the systems (e.g., routing and ridesharing). Recent studies have proposed methods for efficient optimization of SAV routing, etc., [18]–[21], but they have not been applied to MOOPs
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