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

Kumar, Kaushal; Wei, Yupeng; San Jose State University. College of Business. Mineta Transportation Institute

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

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

[PDF-1.51 MB]

English

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Details:

Creators:

Kumar, Kaushal ; Wei, Yupeng

Kumar, Kaushal ; Wei, Yupeng Less -
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

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

[+]

Data Analysis Forecasting Machine Learning Mathematical Prediction Traffic Congestion Traffic Flow
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
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:070a578ff4a120fbeab9e64efdfc67ceadf18d400464b33224b788749768dbb0
Download URL:

https://rosap.ntl.bts.gov/view/dot/68471/dot_68471_DS1.pdf
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No Additional Files

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

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