Impact of Mobility as a Service on Transit Access

Liu, Xiaoyue Cathy; Zhou, Yirong; Haghighi, Nima; University of Utah

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Impact of Mobility as a Service on Transit Access

2022-03-01
By Liu, Xiaoyue Cathy ; Zhou, Yirong ; Haghighi, Nima

[PDF-1.97 MB]

English

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Details:

Creators:

Liu, Xiaoyue Cathy ; Zhou, Yirong ; Haghighi, Nima

Liu, Xiaoyue Cathy ; Zhou, Yirong ; Haghighi, Nima Less -
Corporate Creators:

University of Utah
Corporate Contributors:

Mountain-Plains Consortium ; North Dakota State University ; United States. Department of Transportation. University Transportation Centers (UTC) Program

Mountain-Plains Consortium ; North Dakota State University ; United States. Department of Transportation. University Transportation Centers (UTC) Program Less -
Subject/TRT Terms:

[+]

Access Demand Responsive Transportation Impact Mobility Modal Split Public Transit Ridesourcing Travel Demand Vehicle Miles Of Travel
Publication/ Report Number:

MPC-22-453
Resource Type:

Tech Report
Geographical Coverage:

Utah ; United States

Utah ; United States Less -
Edition:

Final report
Corporate Publisher:

Mountain-Plains Consortium
Abstract:

The emergence of a new mode, Mobility as a Service (MaaS), has, to date, been most often characterized as the ridehailing mode provided by companies such as Uber and Lyft. This study focuses on MaaS as a transit access mode. This mode is also referred to as microtransit. For this research, we describe the technical modeling steps required to account for this new transit access mode. Using Wasatch Front Travel Demand Model as a case study, this project details the steps involved so that this work can be carried forward. Further, using the microtransit pilot project launched in Salt Lake City, Utah, we applied big data techniques to model the spatio-temporal pattern of microtransit activities. This work is significant as the research period has undergone the impact of COVID-19, and it represents the first of its kind to offer insights into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.
Format:

PDF
Funding:

MPC-608
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:d4f0bcd3f0e2dc2e6369d4a8e3c16b804907edb6293ea276f58f78d551e10979
Download URL:

https://rosap.ntl.bts.gov/view/dot/61695/dot_61695_DS1.pdf
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Impact of Mobility as a Service on Transit Access

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