Joint design of multimodal transit networks and shared autonomous mobility fleets

Pinto, Helen K.R.F.; Hyland, Michael; Mahmassani, Hani S.; Verbas, Ömer

doi:10.1016/j.trc.2019.06.010

Cited by 80 publications

(22 citation statements)

References 35 publications

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“…a group of households share an autonomous vehicle (e.g., Allahviranloo & Chow, 2019;Masoud & Jayakrishnan, 2017). In integrated systems, the SAV services act as complimentary to existing PT for first and last mile service and may replace PT operation in certain areas of low demand (Moorthy et al, 2017;Salazar et al, 2018;Shen et al, 2018;Pinto et al, 2019). Special cases include SAV services inside university campuses and industries (Kim et al, 2017;Miller & How, 2017;Pimenta et al, 2017).…”

Section: Sav Typology and Characteristicsmentioning

confidence: 99%

“…Ma et al (2017) use a virtual link in their optimization algorithm to determine optimal fleet size required. Pinto et al (2019) have formulated a bi-level joint transit network redesign and mobility service fleet size determination problem. The lower level consists of a dynamic combined mode choice-traveler assignment problem and the upper level consists of a modified transit network frequency setting problem with SAV service fleet size as a decision variable.…”

Section: Traffic Assignmentmentioning

confidence: 99%

See 1 more Smart Citation

Shared autonomous vehicle services: A comprehensive review

Narayanan

Chaniotakis

Antoniou

2020

Transportation Research Part C: Emerging Technologies

488

139

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The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Landuse, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.

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Section: Sav Typology and Characteristicsmentioning

confidence: 99%

Section: Traffic Assignmentmentioning

confidence: 99%

Shared autonomous vehicle services: A comprehensive review

Narayanan

Chaniotakis

Antoniou

2020

Transportation Research Part C: Emerging Technologies

488

139

View full text Add to dashboard Cite

show abstract

“…They performed an agent-based simulation to evaluate an SAV-PT integrated system and suggested that replacing little-used bus routes with SAVs would significantly increase the efficiency of the integrated system. Subsequent studies have been conducted for further development of PT-SAV integration [45,46], and there is growing interest in this topic. The commonality of these recent studies was that they proposed integrated systems and then compared the results before and after applying the systems for testing the efficiency in terms of travelers' waiting times.…”

Section: The Impact On Public Transit Systemsmentioning

confidence: 99%

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

et al. 2021

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When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

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“…Their case study is based on New York City and the existing public transit system. Pinto et al (2020) focus on the allocation of resources between public transit fleets and shared-use autonomous mobility services. They choose the frequency of the public transit routes and size of the fleet in a bilevel problem under a constrained budget.…”

Section: Related Workmentioning

confidence: 99%

Resiliency of on-demand multimodal transit systems during a pandemic

Auad

Dalmeijer

Riley

et al. 2021

Transportation Research Part C: Emerging Technologies

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During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies. This paper studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. The case study relies on an optimization pipeline that provides an end-to-end ODMTS solution by bringing together methods for demand estimation, network design, fleet sizing, and real-time dispatching. These methods are adapted to work in a multimodal setting and to satisfy practical constraints. In particular, a limit is imposed on the number of passenger transfers, and a new network design model is introduced to avoid the computational burden stemming from this constraint. Real data from the Metropolitan Atlanta Rapid Transit Authority (MARTA) is used to conduct the case study, and the results are evaluated with a high-fidelity simulation. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios.

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Joint design of multimodal transit networks and shared autonomous mobility fleets

Cited by 80 publications

References 35 publications

Shared autonomous vehicle services: A comprehensive review

Shared autonomous vehicle services: A comprehensive review

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

Resiliency of on-demand multimodal transit systems during a pandemic

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