Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations

Yan, Haonan; Kockelman, Kara M.; Gurumurthy, Krishna Murthy

doi:10.1016/j.trd.2020.102577

Cited by 26 publications

(12 citation statements)

References 25 publications

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“…The results of this study are in line with the findings of Brown et al (2014), Fulton et al (2017, Yan et al (2020), Wadud et al (2016), Zhang et al (2015b) and Lu et al (2018). However, our study adds another assessment dimension on the basis of transition modeling, by providing information on the required phase-in and phase-out of technologies to meet the climate goals.…”

Section: Policy Relevancesupporting

confidence: 91%

“…Using the same methodology, Lu et al (2018) and Zhang et al (2015b) showed that a fleet of ACs with dynamic ride-sharing could result in fewer vehicle miles traveled (VMT) and improvements in environmental impacts. Yan et al (2020) used the same approach for the 7-county Minneapolis-Saint Paul region over a simulation period of 24 h and estimated that when ACs with dynamic ride-sharing are developed as battery electric vehicles (BEVs), empty VMT are reduced by 26 %, overall energy use by 64 %, and overall GHG emissions by 60-80 %. Brown et al (2014) used a modified Kaya Identity to account for ACs and non-ACs and observed that ACs combined with dynamic ride-sharing results in a reduction of fuel demand.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the transition toward a zero emission car fleet: Integrating electrification, shared mobility, and automation

Roca-Puigròs¹,

Marmy²,

Wäger³

et al. 2023

Transportation Research Part D: Transport and Environment

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Section: Policy Relevancesupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Modeling the transition toward a zero emission car fleet: Integrating electrification, shared mobility, and automation

Roca-Puigròs¹,

Marmy²,

Wäger³

et al. 2023

Transportation Research Part D: Transport and Environment

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“…TAZs in the periphery of the region experience greater than 20 min response times. This is starkly different than the uniform response times observed by Yan et al (2020) in Minneapolis-St. Paul. In a practical application, TAZs with aver-age response times greater than 15 min, for example, may not even see equal access to SAVs.…”

Section: Resultscontrasting

confidence: 75%

“…Past work has shown that increased trip-making density also has a marginal positive impact on fleet operations through better trip matching if travelers are able to pool their rides (Gurumurthy & Kockelman, 2020b;Yan et al, 2020). Yan et al (2020) used a small sample for Minneapolis-St Paul, whereas Gurumurthy and Kockelman (2020b) focused on the small region of Bloomington, Illinois, so there is little known on how trip-making density in a sprawling region affects SAV operation. To this end, an alternative scenario with demand served by SAVs is explored.…”

Section: Conventional Vehicle Ownershipmentioning

confidence: 99%

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

Gurumurthy

Auld

Kockelman

2021

JTLU

Self Cite

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With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.

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“…When developing ridepooling programs and strategies, promotion policies that target dense urban areas, busy roads, and peak-hour travel should be implemented during the early stages ( 61 ). Governments can work with ridesourcing platforms to promote ridepooling services as an available travel option and adapt appropriate matching windows for riders, particularly in areas with severe traffic congestion ( 21 ).…”

Section: Policy Strategiesmentioning

confidence: 99%

Strangers On This Road We Are On: A Literature Review of Pooling in On-Demand Mobility Services

Hansen

Sener

2022

Transportation Research Record: Journal of the Transportation R

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Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upper-class backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone.

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Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations

Cited by 26 publications

References 25 publications

Modeling the transition toward a zero emission car fleet: Integrating electrification, shared mobility, and automation

Modeling the transition toward a zero emission car fleet: Integrating electrification, shared mobility, and automation

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

Strangers On This Road We Are On: A Literature Review of Pooling in On-Demand Mobility Services

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