Xiang ‘Jacob’ Yan, Assistant Professor

Mentor
Xiang ‘Jacob’ Yan, Assistant Professor
Project Title
Promoting Equitable AI in Transportation
College
Herbert Wertheim College of Engineering
Time Commitment
Variable
Method
Depends on lab funding Paid Research Credit Unpaid with future paid opportunities
Location of Research
On-Campus ,Remote (can be conducted by an online student)
Possible Co-Authorship
Yes

Project Description

AI systems have been increasingly deployed in transportation to make transportation cleaner, safer, more efficient, and more reliable. In a July 2020 report, the USDOT Intelligent Transportation System (ITS) Joint Program Office identified 60 AI-enabled applications in ITS across 11 categories, covering various domains of transportation that affect lives of almost all travelers. AI technologies can address ITS operational changes and transportation needs in a range of real-life scenarios. While AI systems hold great potential to improve transportation for communities and travelers, potential bias in AI development and deployment risk exacerbating existing transportation inequalities.

 

The goal of this project is to generate new knowledge and to develop practical guidelines that can promote more equitable applications of AI technologies in transportation. We will focus on AI applications in three transportation domains: transit operations, transportation system management and operations, traveler decision-support tools. Specifically, the project has three objectives: 1) investigate sources of data biases that can make AI applications augment transport inequality; 2) identify potential AI applications that are motivated by community needs; 3) develop a practical guide for transportation professionals to promote equitable AI applications.

Additional Requirements
Interest in AI and transportation; current knowledge in AI and transportation is strongly preferred

Contact Information

Email Address
xiangyan@ufl.edu
Phone Number
  • xiangyan@ufl.edu
  • AI systems have been increasingly deployed in transportation to make transportation cleaner, safer, more efficient, and more reliable. In a July 2020 report, the USDOT Intelligent Transportation System (ITS) Joint Program Office identified 60 AI-enabled applications in ITS across 11 categories, covering various domains of transportation that affect lives of almost all travelers. AI technologies can address ITS operational changes and transportation needs in a range of real-life scenarios. While AI systems hold great potential to improve transportation for communities and travelers, potential bias in AI development and deployment risk exacerbating existing transportation inequalities.

     

    The goal of this project is to generate new knowledge and to develop practical guidelines that can promote more equitable applications of AI technologies in transportation. We will focus on AI applications in three transportation domains: transit operations, transportation system management and operations, traveler decision-support tools. Specifically, the project has three objectives: 1) investigate sources of data biases that can make AI applications augment transport inequality; 2) identify potential AI applications that are motivated by community needs; 3) develop a practical guide for transportation professionals to promote equitable AI applications.

  • Interest in AI and transportation; current knowledge in AI and transportation is strongly preferred