Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban Transit Lines with Perceived Headway Consideration and Passenger Flow Allocation
<|reference_start|>Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban Transit Lines with Perceived Headway Consideration and Passenger Flow Allocation: This study addresses the urban transit pattern design problem, optimizing stop sequences, headways, and fleet sizes across multiple routes simultaneously to minimize user costs (composed of riding, waiting, and transfer times) under operational constraints (e.g., vehicle capacity and fleet size). A destination-labeled multi-commodity network flow (MCNF) formulation is developed to solve the problem at a large scale more efficiently compared to the previous literature. The model allows for flexible pattern options without relying on pre-defined candidate sets and simultaneously considers multiple operational strategies such as express/local services, short-turning, and deadheading. It evaluates perceived headways of joint patterns for passengers, assigns passenger flows to each pattern accordingly, and allows transfers across patterns in different directions. The mixed-integer linear programming (MILP) model is demonstrated with a city-sized network of metro lines in Chicago, USA, achieving near-optimal solutions in hours. The total weighted journey times are reduced by 0.61% and 4.13% under single-route and multi-route scenarios respectively. The model provides transit agencies with an efficient tool for comprehensive service design and resource allocation, improving service quality and resource utilization without additional operational costs.<|reference_end|>
@article{ng2024joint,
    title={Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban
  Transit Lines with Perceived Headway Consideration and Passenger Flow
  Allocation},
    author={Max T.M. Ng, Draco Tong, Hani S. Mahmassani, Omer Verbas, Taner
  Cokyasar},
    journal={arXiv preprint arXiv:2409.19068},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19068},
    primaryClass={eess.SY cs.SY math.OC}
}