Project thumbnail image
College of Engineering Unit: 
Electrical Engineering and Computer Science
Project Team Member(s): 
Matthew Jacobsen, Casey Dinsmore, Garett Goodlake, Ryan Ho, David Ortega Molina and Eric Hoang
Physical Location at Expo: 
Community Plaza
Project ID: 
Project Description: 

Our project is a web-based interface that reports traffic intersection data gathered by Traffic Technology Services (TTS). The website provides real-time data to the clients of TTS to analyze intersection traffic efficiency based on statistics such as total delay time, green light arrival rates, split failure arrival rates, approach direction, exit direction, and more. The project utilizes bar charts, pie charts, and histograms to display the processed data in a meaningful and effective manner. The user is able to select parameters for the plots based on the region of the intersection, specific intersections, day of the week, approach direction, and exit direction. Furthermore, the website also has access controls implemented into two user groups: User, and Admin. Admins have control over the backend data to create, delete, and modify all the data as well as the users and their permissions. Each user in the User group has access to view certain coverages or sub-divisions of regions that TTS has determined. This effectively allows users to only access intersections in their jurisdiction, such as an Oregon Department of Transportation worker may only view traffic data in the state of Oregon. 

Our project is written in Python and utilizes open source packages including Flask, Plotly, SQLAlchemy, flask-restx, ItsDangerous, and more. We have implemented a mock database using SQLAlchemy objects for coverages, regions, signals, users, vehicle data, and access controls. The vehicle object has attributes for all data points that are collected for each traffic signal, including the time of arrival, time of exit, approach direction, exit direction, if the vehicle stops, the state of the light upon arrival, day, and others. Each parameter can be grouped and filtered to create a sub dataset for specific graphs.

The future of our project is that it will be continued to be built upon by Traffic Technology Services and their teams so that one day it can be deployed and help engineers and policymakers better understand exactly how our roads and infrastructure function as a whole. The necessity for this project is apparent as it will allow for a future of more safe and efficient traffic flow and infrastructure.

Industry Sponsor(s): 
  • Traffic Technology Services
  • Opportunities: 
    This team is open to networking
    This team is open to collaboration opportunities
    This team is open to employment offers