Modeling Transit in a Fully Integrated Agent-Based Framework: Methodology and Large-Scale Application

TL;DR

The paper addresses the need for integrated transit modeling within a multimodal, agent-based transportation framework. It presents POLARIS, a high-performance platform that combines activity-based demand modeling, freight, multimodal supply, time-dependent intermodal routing, and simulation-based dynamic traffic and transit assignment, all within a single architecture. Key contributions include the Time-Dependent Intermodal A* routing, simulation-based DTA with personalized weighting, and seamless ABM transit integration across driving, walking, micromobility, and TNCs, enabling full interaction among travelers, vehicles, and service providers. Numerical results from a large-scale Chicago-area study show that congestion pricing, transit-service improvements, first-mile/last-mile subsidies, e-commerce growth, and electrification significantly affect transit ridership, with notable synergistic or canceling interactions across levers. Overall, the work demonstrates the practical value of a fully integrated multimodal simulation and decision-making framework for evaluating complex policy interventions at city- to regional-scales.

Abstract

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.

Figures (10)

• Figure 1: POLARIS workflow.
• Figure 2: A sample of six different transit patterns for a given route.
• Figure 3: A sample multimodal network (a) in the real-world (b) as a multimodal graph in POLARIS.
• Figure 4: Running buses in mixed-traffic in POLARIS.
• Figure 5: Impact of congestion pricing on.