-
-
4 simulated accidents close up
-
4 simulated accidents zoomed out
Inspiration
The inspiration for OdinAnalytica stemmed from the increasing need for efficient traffic incident management in urban areas like Brampton. Traffic crashes not only lead to congestion but also delay emergency responses, which can worsen outcomes for those involved. By leveraging technology, we aimed to build a system that automates accident detection, provides real-time updates, and facilitates faster on-site assistance.
What it does
OdinAnalytica is a smart traffic incident detection and response system. It uses real-time traffic feeds to identify crashes and immediately marks the incident on a map for easy visualization. The system integrates twilio to notify and dispatch the nearest tow truck driver, ensuring rapid response and minimal downtime for affected roadways.
How we built it
Accident Detection: Leveraged computer vision techniques using YOLO to analyze live traffic feeds and detect crashes.
Mapping: Utilized Mapbox to plot crash locations dynamically on an interactive map. Database and Backend: Managed incident data using SQLite, with a FastAPI backend to handle API requests for the map and alerts.
Alerts: Integrated twilio to enable sms messaging to dispatch of tow trucks.
Frontend: Built a responsive web interface using React for real-time visualization of incidents.
Challenges we ran into
Mapping Integration: Plotting accurate crash locations dynamically on the map while ensuring scalability was a technical hurdle.
Voiceflow Coordination: Setting up seamless integration between accident detection, backend APIs, and Voiceflow for tow truck dispatching required robust API workflows.
Crash Prediction"
Accomplishments that we're proud of
Successfully developed an end-to-end system that integrates accident detection, mapping, and automated response mechanisms.
Built a scalable backend using FastAPI and SQLite that efficiently handles real-time data.
Achieved seamless communication between our system and twilio to dispatch tow trucks effectively.
Created a user-friendly, real-time map interface for visualizing accident locations.
What we learned
The value of designing scalable backend architectures that handle dynamic data inputs.
How to integrate various APIs and services (like twilio and Mapbox) into a cohesive system.
The need for clear workflows when combining machine learning, data visualization, and real-world applications.
What's next for odinAnalytica
Enhanced Detection: Train the detection model further using more diverse datasets to improve accuracy.
Predictive Analytics: Leverage historical data to identify accident-prone zones and recommend preventive measures like speed bumps or signage.
Emergency Services Integration: Expand the system to notify emergency responders alongside tow truck drivers for better incident management.
Mobile App: Develop a mobile version for on-the-go monitoring and management of incidents. Community Engagement: Partner with local municipalities to deploy OdinAnalytica for public benefit, ensuring safer roads and quicker response times.
Log in or sign up for Devpost to join the conversation.