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Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

Overview of attention for article published in IEEE Transactions on Intelligent Transportation Systems, January 2023
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2 X users

Citations

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4 Dimensions

Readers on

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7 Mendeley
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Title
Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach
Published in
IEEE Transactions on Intelligent Transportation Systems, January 2023
DOI 10.1109/tits.2023.3237134
Authors

Tianli Tang, Ronghui Liu, Charisma Choudhury, Achille Fonzone, Yuanyuan Wang

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 29%
Lecturer 2 29%
Student > Bachelor 1 14%
Unknown 2 29%
Readers by discipline Count As %
Engineering 2 29%
Computer Science 1 14%
Philosophy 1 14%
Unknown 3 43%
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 02 April 2023.
All research outputs
#17,436,941
of 25,582,611 outputs
Outputs from IEEE Transactions on Intelligent Transportation Systems
#1,337
of 1,637 outputs
Outputs of similar age
#271,419
of 474,779 outputs
Outputs of similar age from IEEE Transactions on Intelligent Transportation Systems
#11
of 14 outputs
Altmetric has tracked 25,582,611 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,637 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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 474,779 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.