Core Concepts
This paper proposes an optimization framework to jointly design the transit network, size the autonomous mobility-on-demand (AMoD) fleet, and determine pricing strategies for a transit-centric multimodal urban mobility system, while considering passengers' mode and route choices.
Abstract
The paper addresses the challenge of urban mobility in the context of growing urban populations and changing demand patterns, by integrating autonomous mobility-on-demand (AMoD) systems with existing public transit (PT) networks.
The key highlights and insights are:
The authors propose a novel optimization framework for solving the Transit-Centric Multimodal Urban Mobility with Autonomous Mobility-on-Demand (TCMUM-AMoD) problem at scale. The system operator (public transit agency) determines the network design and frequency settings of the PT network, fleet sizing and allocations of the AMoD system, and the pricing for using the multimodal system, with the goal of minimizing passenger disutility.
Passengers' mode and route choice behaviors are modeled explicitly using discrete choice models, including a nested logit model that captures the two-level decisions of mode choice and route choice.
A first-order approximation algorithm is introduced to solve the challenging non-linear optimization problem at scale.
The proposed optimization framework is evaluated through a real-world case study in Chicago, demonstrating the potential to optimize urban mobility across different demand scenarios (local and downtown).
This is the first paper to jointly optimize transit network design, fleet sizing, and pricing for the multimodal mobility system while considering passengers' mode and route choices.
Stats
The paper does not provide any specific numerical data or statistics to support the key logics. The analysis is focused on the optimization framework and solution methodology.
Quotes
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