UROP Research Mentor Project Submission Portal: Submission #1402

serial: '1402'
sid: '22375'
uuid: 2e19661a-a7fc-4149-b996-08627967ee75
uri: /urop-research-mentor-project-submission-portal
created: '1782846769'
completed: '1782846769'
changed: '1782846769'
in_draft: '0'
current_page: ''
remote_addr: 146.201.222.254
uid: '0'
langcode: en
webform_id: urop_project_proposal_portal
entity_type: node
entity_id: '1116'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  roundtable_info: {  }
  approximately_how_many_hours_a_week_would_the_research_assistant: '10'
  are_you_and_fsu_employee: 'Yes'
  are_you_currently_looking_for_students_: 'Yes'
  confirmation_1: '1'
  contact_email_fsu_email: ''
  contact_email_fsu_email2: ''
  contact_email_fsu_email_if_affiliated_: gka24a@fsu.edu
  faculty_advisor_confirmation: '1'
  faculty_advisor_name: 'Dr. Arda Vanli'
  faculty_advisor_s_fsu_email: oavanli@eng.famu.fsu.edu
  fsu_college: 'FAMU-FSU College of Engineering'
  fsu_department_if_applicable_: 'Industrial and Manufacturing Engineering'
  headshot_optional_: ''
  if_the_project_location_is_off_campus_does_the_student_need_to_p: ''
  mentoring_philosophy: |-
    Developing a relationship founded on mutual respect
     Giving mentees’ ownership of their work and promoting accountability 
     Sharing your own experience.
    Creating a safe environment in which mentees feel that is acceptable to fail and learn from their mistakes
  mentor_handbook_and_faqs: '1'
  name_of_other_faculty_collaborator_if_applicable_: ''
  number_of_assistants_needed_faculty_postdoc_max_6_graduate_stude: '1'
  other_faculty_collaborator_s_preferred_pronouns: ''
  overall_research_project_description: |
    Background
    State departments and agencies monitor traffic volumes to manage and maintain efficient highway systems. Traffic data collected are used for various applications including risk assessments to identify high crash locations as well as resource allocation, maintenance planning, and policy formulation. 
    Research Problem
    Traffic data collection requires significant time and effort. Current manual methods used for data collection are insufficient and do not cover all road segments. Currently, in Florida, the department of transport uses manual ground-based methods to count traffic on some major roadways.  There is still a substantial gap in data collection in terms of coverage. Artificial intelligence (AI) and statistical computing offer significant potential in providing robust and cost-effective traffic volume data for these unmonitored road segments.  
    Research Objectives

    This research will focus on:
    1.	Reviewing existing literature and identifying best practices for traffic volume prediction.
    2.	Building predictive artificial intelligence models for traffic volume prediction on unmonitored roadways in Florida.

    Methodology
    The project will focus on traffic volume prediction for selected districts in Florida. The proposed methodology will include traffic demand modelling and machine learning. The designed model will be evaluated based on defined metrices to ascertain its robustness.
    Expected Outcomes
    This research is expected to result in defined tested strategies and recommendations for predicting traffic volumes using artificial intelligence. 
  please_add_any_additional_information_here: ''
  please_provide_a_link_to_your_publications_a_video_clip_or_a_web: ''
  please_select_the_choice_that_most_accurately_describes_your_exp: In-person
  please_select_the_location_of_your_project_: 'On FSU Main Campus'
  position_availability_for_student_research: 'Flexible schedule'
  position_title: 'Graduate Student'
  primary_research_mentor_name: 'George Amu'
  project_keywords: 'Artificial Intelligence, Transportation, Machine Learning, Traffic Modelling'
  relevant_student_major_s_: 'Open to all majors'
  research_mentor_preferred_pronoun2: ''
  research_mentor_pronouns: ''
  research_mentor_supervisor_if_different_from_above_: ''
  research_tasks_for_student_research_assistant_s_: |-
    1.	Reviewing existing literature and identifying best practices for traffic volume prediction.
    2.	Building predictive artificial intelligence models for traffic volume prediction on unmonitored roadways in Florida.
    Methodology
  roundtable_times_and_zoom_links: ''
  skills_that_research_assistants_may_need_: |
    data analytics - recommended
  title_of_the_project: 'Explainable Artificial Intelligence for Traffic Volume Prediction'
  update_url: 'https://cre.fsu.edu/urop-research-mentor-project-submission-portal?token=rd299wXj9YCmFC1cwI83_Fbua3QXgCi7xjKXY9zT1q0'
  urop_performance_evaluation: '1'
  urop_poster_presentation: '1'
  when_potential_research_assistants_are_reaching_out_via_email_2: ''
  when_potential_research_assistants_are_reaching_out_via_email_wh: ''
  when_students_are_reaching_out_via_email_what_is_your_preferreda: ''
  will_you_be_employed_at_fsu_for_the_entirety_of_fall_and_spring: 'Yes'
  would_you_like_to_participate_in_the_urop_research_mentor_round2: 'Yes'
  would_you_like_to_participate_in_the_urop_research_mentor_roundt: ''
  year: '2026'