AI Engine - Orion Labs
Traffic Behavior Analysis and Classification
Description:
Transportation agencies rely on data to make informed decisions on mitigation strategies to prevent collisions. Typically crash data has been the data that has been relied upon to enact these strategies. Unfortunately, relying on crash data means accidents have already happened. Transportation agencies are now taking a more proactive approach to prevent accidents in areas that are a high priority based on near miss or other traffic behavior data. Building off of Orion Lab’s previous project with Data Mine of The Rockies, Orion Labs’ seeks to enhance its spatio-temporal traffic data capture system.
Two primary systems are proposed for enhancement. The first is building off of the prior project to dive deeper into traffic analysis using the high fidelity data that Orion Labs’ captures. Traffic engineers can benefit greatly from the data that is captured by Orion Labs’ Saiph, but may not always have the skillset to manipulate the dense database. A suite of tools designed to intelligently query, filter, and present this data is desirable. Real traffic data and a software framework will be provided. This project focuses on designing queries, user interaction interfaces, and visualizations for supplied traffic analysis requests, such as “What is the average vehicle turning speed in this intersection?” or “How often do vehicles yield as required based on traffic signal phasing?”
Pre-requisite knowledge/skills:
- Open to sophomores through seniors
Mentors: