Attention-Based Data Analytic Models for Traffic Flow Predictions [Brief]

Kumar, Kaushal; Wei, Yupeng

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Attention-Based Data Analytic Models for Traffic Flow Predictions [Brief]

2023-03-01
By Kumar, Kaushal ; Wei, Yupeng

File Language:

English

Details

Creators:

Kumar, Kaushal ; Wei, Yupeng
Corporate Creators:

San Jose State University. College of Business. Mineta Transportation Institute
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

Project 2211
Resource Type:

Brief
Geographical Coverage:

United States
Edition:

Research Brief
Corporate Publisher:

San Jose State University. College of Business. Mineta Transportation Institute
Abstract:

Traffic congestion makes Americans waste millions of hours and dollars each year. In this work, the authors analyze traffic flow data from the Caltrans Performance Measurement System (PeMS) and use deep learning algorithms to predict traffic flow accurately.
Format:

PDF
Funding:

69A3551747127
Download URL:

https://rosap.ntl.bts.gov/view/dot/68471/dot_68471_DS1.pdf
File Type:

[PDF - 1.51 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:6762bcf6011092993c77e46a1f8ba4b93b9e8d88f843716b8db2bf7834285f386ad402731bf9dc798224225e231c3d682596dda9ebfda52c5b3a8ad79df4b17f

File Language:

English

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