Semi-on-Demand Hybrid Transit Route Design with Shared Autonomous Mobility Services
<|reference_start|>Semi-on-Demand Hybrid Transit Route Design with Shared Autonomous Mobility Services: This study examines the route design of a semi-on-demand hybrid route directional service in the public transit network, offering on-demand flexible route service in low-density areas and fixed route service in higher-density areas with Shared Autonomous Mobility Service (SAMS). The study develops analytically tractable cost expressions that capture access, waiting, and riding costs for users, and distance-based operating and time-based vehicle costs for operators. Two formulations are presented for strategic and tactical decisions in flexible route portion, fleet size, headway, and vehicle size optimization, enabling the determination of route types between fixed, hybrid, and flexible routes based on demand, cost, and operational parameters. The practical applications and benefits of semi-on-demand feeders are demonstrated with numerical examples and a large-scale case study in the Chicago metropolitan area. Findings reveal scenarios in which flexible route portions serving passengers located further away reduce total costs, particularly user costs. Lower operating costs in lower-demand areas favor more flexible routes, whereas higher demand densities favor more traditional line-based operations. On two studied lines, a current cost forecast favors smaller vehicles with flexible routes, but operating constraints and higher operating costs would favor bigger vehicles with hybrid routes. The study provides an analytical tool to design SAMS as directional services and transit feeders, and tractable continuous approximation formulations for future research in transit network design.<|reference_end|>
@article{ng2024semi-on-demand,
    title={Semi-on-Demand Hybrid Transit Route Design with Shared Autonomous
  Mobility Services},
    author={Max T.M. Ng, Florian Dandl, Hani S. Mahmassani, Klaus Bogenberger},
    journal={arXiv preprint arXiv:2403.15804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15804},
    primaryClass={eess.SY cs.SY math.OC}
}