TBI: Transient Hierarchical Modeling of Large-Scale Vehicle Sharing Systems

Due to rush-hour effects, transient analysis may be necessary to model and optimize vehicle sharing systems. However, existing methods for transient modeling face computational challenges at scale. To solve this, we present trajectory-based iteration (TBI), a method for the efficient decomposed modeling of vehicle-sharing systems, and we extend it to a scalable method for non-convex, transient optimization problems. The proposed TBI method works by iteratively solving spatial submodels, then periodically updating the vehicle flows between submodels. We then present experimental results where our method results in a 37% improvement in runtime for solving fluid models for mobility systems. Then, we present a case study in which our optimization method results in an estimated 22% improvement in the objective function for a vehicle-sharing system with offline rebalancing.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-09-25
DOI
https://doi.org/10.1109/tits.2025.3610685
Citations
1
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.68
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article

TBI: Transient Hierarchical Modeling of Large-Scale Vehicle Sharing Systems

Daphné Tuncer, Matthew Sheldon, Giuliano Casale
1 citations
IEEE Transactions on Intelligent Transportation Systems
Transportation and Mobility Innovations
0.68
article

TBI: Transient Hierarchical Modeling of Large-Scale Vehicle Sharing Systems

Daphné Tuncer, Matthew Sheldon, Giuliano Casale
article en
1 citations

Abstract

Due to rush-hour effects, transient analysis may be necessary to model and optimize vehicle sharing systems. However, existing methods for transient modeling face computational challenges at scale. To solve this, we present trajectory-based iteration (TBI), a method for the efficient decomposed modeling of vehicle-sharing systems, and we extend it to a scalable method for non-convex, transient optimization problems. The proposed TBI method works by iteratively solving spatial submodels, then periodically updating the vehicle flows between submodels. We then present experimental results where our method results in a 37% improvement in runtime for solving fluid models for mobility systems. Then, we present a case study in which our optimization method results in an estimated 22% improvement in the objective function for a vehicle-sharing system with offline rebalancing.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(11)
Institut Polytechnique de Paris (FR), Imperial College London (GB)
Openalex Percentile: Top 25%
Transportation and Mobility Innovations
0.68
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