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Teaching freight mode choice models new tricks using interpretable machine learning methods

Overview of attention for article published in Frontiers in Future Transportation, March 2024
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Title
Teaching freight mode choice models new tricks using interpretable machine learning methods
Published in
Frontiers in Future Transportation, March 2024
DOI 10.3389/ffutr.2024.1339273
Authors

Xiaodan Xu, Hung-Chia Yang, Kyungsoo Jeong, William Bui, Srinath Ravulaparthy, Haitam Laarabi, Zachary A. Needell, C. Anna Spurlock

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 100%
Readers by discipline Count As %
Unspecified 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 March 2024.
All research outputs
#21,160,107
of 25,992,468 outputs
Outputs from Frontiers in Future Transportation
#1
of 1 outputs
Outputs of similar age
#233,298
of 340,561 outputs
Outputs of similar age from Frontiers in Future Transportation
#1
of 1 outputs
Altmetric has tracked 25,992,468 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 0.5. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,561 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them