| Description |
1 online resource (19 pages) : color illustrations. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
NREL/PR ; 5400-75682 |
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NREL/PR ; 5400-75682.
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| Note |
Slideshow presentation. |
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"Presented by Dustin Weigl." |
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"2020 Transportation Research Board Annual Meeting, Washington, D.C., January 13, 2020." |
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"Funding provided by by the DOE Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program"--Page 4 of cover. |
| Funding |
DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed on September 30, 2020). |
| Summary |
In the past decade, Transportation Network Companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of the TNC mode has come with a fair share of criticism for its negative externalities such as increasing vehicle miles traveled and congestion in cities. Ride pooling (or ridesharing), in which all or a part of an individual (or group)'s trip is shared with another individual (or group)'s trip, has the potential to reduce these externalities. Ride pooling is an effective solution to reduce congestion and travel cost, but pooled rides from TNCs still represent a small percentage of their total trips served (and miles driven), relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter sharing a TNC trip. In this study, the newly available Chicago transportation network provider data was explored to identify the extent to which different socio-economic, spatio-temporal, and trip characteristics impact willingness-to-share (WTS) in ride-hailing trips. Multivariate linear regression and machine learning models were employed to understand and predict WTS based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pick-up locations, and airport trips as the most important predictors of WTS. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from TNC modes. |
| Subject |
Ridesharing -- United States.
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Transportation -- Environmental aspects -- United States.
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Covoiturage -- États-Unis.
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Ridesharing. (OCoLC)fst01097753
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Transportation -- Environmental aspects.
(OCoLC)fst01155056
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United States. (OCoLC)fst01204155 https://id.oclc.org/worldcat/entity/E39PBJtxgQXMWqmjMjjwXRHgrq
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| Indexed Term |
ride sharing |
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ride-hailing |
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transportation network company |
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willingness to share |
| Added Author |
National Renewable Energy Laboratory (U.S.), issuing body.
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| Standard No. |
1669392 OSTI ID |
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0000-0002-8173-0923 |
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0000-0003-1603-1883 |
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0000-0002-2174-5066 |
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0000-0003-4641-5415 |
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0000-0002-6140-7352 |
| Gpo Item No. |
0430-P-09 (online) |
| Sudoc No. |
E 9.22:NREL/PR-5400-75682 |
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