Exploring the Role of Sponsoring Agencies in Shaping the MUTCD Using Supervised and Unsupervised Text Mining-Reference-Cited by-同舟云学术

Exploring the Role of Sponsoring Agencies in Shaping the MUTCD Using Supervised and Unsupervised Text Mining

Published:2024-11 Issue:11 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Kutela Boniphace¹^ORCID,Shita Hellen²^ORCID,Das Subasish³,Kapaya Lucy⁴,Tarimo Elizabeth⁵

Affiliation:

1. Assistant Research Scientist, Texas A&M Transportation Institute, 701 N Post Oak Ln., # 430, Houston, TX 77024. ORCID: .

2. Graduate Research Assistant, Florida International Univ., 10555 W Flagler St., Miami, FL 33174 (corresponding author). ORCID: .

3. Assistant Professor, Texas State Univ., 601 University Dr., San Marcos, TX 78666.

4. Undergraduate Student, Univ. of Dodoma, Kikuyu Ave., Dodoma 41218, Tanzania.

5. Undergraduate Student, Dept. of Statistics, Eastern Africa Statistical Training Centre, Dar es Salaam 35103, Tanzania.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8395

Reference29 articles.

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3. Alduayj S. S. and K. Rajpoot. 2018. “Predicting employee attrition using machine learning.” In Proc. 2018 13th Int. Conf. on Innovations in Information Technology IIT 2018 93–98. New York: IEEE. https://doi.org/10.1109/INNOVATIONS.2018.8605976.

4. Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach

5. ASCE. 2022. “American Society of Civil Engineers.” Accessed April 13 2022. https://www.asce.org/.