Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Swastika Barua

Biographic Data

ID4616838
NAMESwastika Barua
GIVEN NAMESSwastika
FAMILY NAMEBarua
SIGNATUREBARUA S
AFFILIATIONSTexas State University
ORCID0009-0008-4894-9131
VERIFIEDYes
TOTAL WORKS4
TOTAL CITATIONS0
WORKS AS AUTHOR4
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2025
LATEST PUBLICATION YEAR2026
H-INDEX0
  • Explainable AI-driven hybrid modeling for crash severity analysis near bus stops in urban areas

    Open Access•Swastika Barua, Rohit Chakraborty et al.•ARTICLE•International Journal of Urban…•2026

    Crashes near bus stops in urban areas present critical safety risks for pedestrians and vehicle occupants, especially in high-traffic areas with frequent transit activity. This study investigates crash severity patterns near Texas bus stops from 2017 to 2023 using a hybrid explainable AI framework. XGBoost and Random Forest models were applied, as part of feature selection process, to identify key contributing factors, which were subsequently use…

  • Classifying crash severity by SAE automation level using transformer models

    Open Access•Mahmuda Sultana Mimi, Md Majharul Islam et al.•ARTICLE•Transport Policy•2026

  • Toward safer micromobility

    Open Access•Jinli Liu, Swastika Barua et al.•ARTICLE•Cities•2026

  • Built environment and injury risk

    Open Access•Syed Aaqib Javed, Swastika Barua et al.•ARTICLE•Cities•2025

No prominent works on this page.

  • Built environment and injury risk

    Open Access•Syed Aaqib Javed, Swastika Barua et al.•ARTICLE•Cities•2025

  • Explainable AI-driven hybrid modeling for crash severity analysis near bus stops in urban areas

    Open Access•Swastika Barua, Rohit Chakraborty et al.•ARTICLE•International Journal of Urban…•2026

    Crashes near bus stops in urban areas present critical safety risks for pedestrians and vehicle occupants, especially in high-traffic areas with frequent transit activity. This study investigates crash severity patterns near Texas bus stops from 2017 to 2023 using a hybrid explainable AI framework. XGBoost and Random Forest models were applied, as part of feature selection process, to identify key contributing factors, which were subsequently use…

  • Classifying crash severity by SAE automation level using transformer models

    Open Access•Mahmuda Sultana Mimi, Md Majharul Islam et al.•ARTICLE•Transport Policy•2026

  • Toward safer micromobility

    Open Access•Jinli Liu, Swastika Barua et al.•ARTICLE•Cities•2026

Crash (4 works) · Poison control (4 works) · Traffic and Road Safety (4 works) · Autonomous Vehicle Technology and Safety (2 works) · Injury prevention (2 works) · SAFER (2 works) · Urban Transport and Accessibility (2 works) · Assistive Technology in Communication and Mobility (1 work) · Automation (1 work) · Built environment (1 work)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae