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Abstract for presentation (Poster or Podium)

AI in Transportation

Enhancing Traffic Safety for Roadside Responders with AI

Monday, June 17, 2024
1:30 PM - 2:45 PM ET
Room: Galleria

    Primary Author(s)

  • Abolfazl Karimpour, n/a

    Assistant Professor
    SUNY Polytechnic Institute
    Clinton, New York, United States

    • Presenter(s)

  • Abolfazl Karimpour, n/a

    Assistant Professor
    SUNY Polytechnic Institute
    Clinton, New York, United States

    • Co-Author(s)

  • TW

    Tianhao Wang, n/a

    Researcher
    Yangzhou University
    Yangzhou, Jiangsu, China (People's Republic)

  • JO

    Jishun Ou, n/a

    Assistant Professor
    Yangzhou University
    Yangzhou, Jiangsu, China (People's Republic)

  • Matthew Rycraft

    Undergraduate Student
    SUNY Polytechnic Institute
    Utica, NY, United States

    • Submitter(s)

  • Abolfazl Karimpour, n/a

    Assistant Professor
    SUNY Polytechnic Institute
    Clinton, New York, United States

Abstract:

Roadside first responders, including towing operations, law enforcement, firefighters, and emergency medical services, play a critical role in maintaining a functional transportation system. Unfortunately, these responders face an increased risk of being struck by passing motorists while performing their duties. To address this issue, researchers and stakeholders are working towards finding solutions to reduce the risk of severe and fatal injuries to roadside responders. However, the lack of a comprehensive data collection and reporting system focused on struck-by and near-miss incidents has limited our understanding of the extent of this risk. In this study, we propose the development of an AI-based platform to collect and analyze recent years' news articles and reports related to struck-by crashes involving roadside first responders. This platform will utilize natural language processing and machine learning techniques to mine and extract valuable insights from these news sources. Additionally, we will develop a pattern recognition method that aims to understand the patterns and conditions leading to struck-by crashes involving first responders. By implementing these methodologies, our research seeks to provide a deeper understanding of the risks faced by roadside first responders and to identify potential strategies for mitigating these risks. The findings from this study will contribute to the development of evidence-based safety measures and policies to protect these essential personnel while they perform their crucial duties.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Understand the importance of collecting and analyzing recent news and data related to struck-by crashes involving roadside first responders for improving traffic safety.
  • Gain proficiency in developing an AI-based platform for data mining and natural language processing to extract valuable insights from news articles and reports.
  • Acquire knowledge and skills in implementing a pattern recognition method to identify and analyze the patterns and conditions leading to struck-by crashes, contributing to evidence-based safety measures for roadside responders.