UROP Research Mentor Project Submission Portal: Submission #639
Submission information
Submission Number: 639
Submission ID: 13576
Submission UUID: 97a67c29-aac1-4c14-9840-2d554f7f9e16
Submission URI: /urop-research-mentor-project-submission-portal
Submission Update: /urop-research-mentor-project-submission-portal?token=5lovCAt7jVFK1l-LfzvZwxcZ0pnoPMn-qTYvUUtp5sE
Created: Thu, 04/18/2024 - 09:26 PM
Completed: Thu, 04/18/2024 - 09:59 PM
Changed: Thu, 04/18/2024 - 09:59 PM
Remote IP address: 98.230.37.190
Submitted by: Anonymous
Language: English
Is draft: No
Webform: UROP Project Proposal Portal
Submitted to: UROP Research Mentor Project Submission Portal
Research Mentor Information
Qianwen Guo
she/her
Dr.
Faculty
FAMU-FSU College of Engineering
FAMU-FSU College of Engineering
![Qianwen Guo.png Qianwen Guo.png](https://cre.fsu.edu/system/files/webform/urop_project_proposal_portal/13576/Qianwen%20Guo.png)
Additional Research Mentor(s)
Overall Project Details
Real-time Bus Occupancy Estimation using Wi-Fi Probe Requests
public transportation, bus occupancy, data collection, data analysis, machine learning, deep learning, Wi-Fi probe requests, internet of things
Yes
6
Open to all majors.
On FSU Main Campus
The data collection will be done on FSU campus shuttle buses
Partially Remote
5
Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
This study proposes leveraging IoT-enabled devices, particularly smartphones, using Wi-Fi technology for transit data collection and analysis. It aims to develop a monitoring system that detects smartphones in real-time, capturing data like MAC addresses and GPS coordinates to infer transit patterns. Challenges include Wi-Fi signal range and device detection inconsistencies. Pilot studies in Florida will validate the system's efficacy across diverse transit systems. Implementing this system enables transit agencies to optimize vehicle dispatching and scheduling while enhancing the overall user experience by providing valuable insights into transit travel flow patterns.
The primary objective is to collect Wi-Fi data from FSU campus shuttle buses and manually count the bus occupancy for each station.
Students interested in analyzing Wi-Fi data and developing machine learning or deep learning models can pursue additional opportunities.
Students interested in analyzing Wi-Fi data and developing machine learning or deep learning models can pursue additional opportunities.
Complete the task diligently and ensure accurate counting.
Fostering collaboration and support, enabling everyone to contribute to each other's growth and productivity through mutual assistance, encouragement, and shared learning experiences, cultivating a positive and productive environment.
https://sites.google.com/view/qguo/home
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UROP Program Elements
Yes
Yes
Yes
Yes
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2024
https://cre.fsu.edu/urop-research-mentor-project-submission-portal?element_parents=elements/research_mentor_information/headshot_optional_&ajax_form=1&_wrapper_format=drupal_ajax&token=5lovCAt7jVFK1l-LfzvZwxcZ0pnoPMn-qTYvUUtp5sE