Modeling Transit in a Fully Integrated Agent-Based Framework: Methodology and Large-Scale Application
<|reference_start|>Modeling Transit in a Fully Integrated Agent-Based Framework: Methodology and Large-Scale Application: This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficiently integrates advanced travel and freight demand modeling, dynamic traffic and transit assignment, and multimodal transportation simulation within a unified framework. We focus on POLARIS's transit routing, assignment, and simulation components, detailing its structural design and essential terminologies. We demonstrate how the model integrates upstream decision-making processes - activity generation, location and timing choices, and mode selection, particularly for transit-inclusive trips - followed by routing, assignment decisions, and the movement of travelers and vehicles within a multimodal network. This integration enables modeling of interactions among all agents, including travelers, vehicles, and transportation service providers. The study reviews literature on transportation system modeling tools, describes the transit modeling framework within POLARIS, and presents findings from large-scale analyses of various policy interventions. Results from numerical experiments reveal that measures such as congestion pricing, transit service improvements, first-mile-last-mile subsidies, increased e-commerce deliveries, and vehicle electrification significantly impact transit ridership, with some interactions between these levers exhibiting synergistic or canceling effects. The case study underscores the necessity of integrating transit modeling within a broader multimodal network simulation and decision-making context.<|reference_end|>
@article{verbas2024modeling,
    title={Modeling Transit in a Fully Integrated Agent-Based Framework:
  Methodology and Large-Scale Application},
    author={Omer Verbas and Taner Cokyasar and Pedro Veiga de Camargo and Krishna
  Murthy Gurumurthy and Natalia Zuniga-Garcia and Joshua Auld},
    journal={arXiv preprint arXiv:2408.05176},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05176},
    primaryClass={eess.SY cs.SY}
}