Design of Transit-Centric Multimodal Urban Mobility System with Autonomous Mobility-on-Demand

TL;DR

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.

Abstract

This paper addresses the pressing challenge of urban mobility in the context of growing urban populations, changing demand patterns for urban mobility, and emerging technologies like Mobility-on-Demand (MoD) platforms and Autonomous Vehicle (AV). As urban areas swell and demand pattern changes, the integration of Autonomous Mobility-on-Demand (AMoD) systems with existing public transit (PT) networks presents great opportunities to enhancing urban mobility. We propose a novel optimization framework for solving the Transit-Centric Multimodal Urban Mobility with Autonomous Mobility-on-Demand (TCMUM-AMoD) at scale. The system operator (public transit agency) determines the network design and frequency settings of the PT network, fleet sizing and allocations of 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. A first-order approximation algorithm is introduced to solve the problem at scale. Using a case study in Chicago, we showcase the potential to optimize urban mobility across different demand scenarios. To our knowledge, ours 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.

Figures (11)

• Figure 1: Two route options for a morning local commute from home to the company. Commuters can: i) walk to the bus stop, take the bus and walk to the company, ii) take an AMoD service directly from home to the company.
• Figure 2: Three route options for a morning downtown commute from home to the company. Commuters can: i) walk to the subway station, take the subway and walk to the company, ii) take a bus service to the subway station, take the subway and walk to the company, and iii) take an AMoD service to the subway station, take the subway and walk to the company.
• Figure 3: Example explaining the route separation after introducing shared AMoD services. Trip $k$ indicates a first-mile shared AMoD trip which is shared by commutes $(u_1,v_1)$ and $(u_2, v_2)$. Each commute contains both routes with non-shared AMoD trips $r_1, r_2$ and routes with shared AMoD trips $r_1^k, r_2^k$. Routes $r_1$ ($r_2$) and $r_1^k$ ($r_2^k$) share the same itinerary but different first-mile AMoD services.
• Figure 4: Multidimensional choices of the nested logit model.
• Figure 5: Road and transit networks for the study region. Blue region indicates the study region within the CTA network, blue lines represent road network, green lines denote bus network, red dots stand for bus stops, and rail symbols are rail stations.