A public safety solution for detecting and responding to railroad accidents in real time.
View Project →Improving Public Safety through Smart Railroad Accident Detection and Emergency Response
Every three hours, a vehicle or person is struck by a train in the U.S. Many of these incidents occur at unmanned crossings where emergency response is delayed.
AORF (Automated Obstacle & Response Framework) is an intelligent system designed to detect railroad accidents and automatically contact local emergency services in real time.
It integrates location-based APIs, Twilio communication, and IoT sensors to minimize response time and enhance public safety.
Over 5,800 train-car collisions occur annually in the U.S.
Many are caused by unmanned railroads or delayed emergency notifications.
Average response times remain high due to the lack of real-time accident detection and data sharing.
AORF detects sudden stops or speed changes in trains or vehicles and triggers an Emergency Response Module (ERM) that automatically:
Identifies nearest emergency contacts via the Precisely API (PSAP by location)
Sends SMS and email alerts using Twilio
Shares accident information, including:
Location (Latitude/Longitude)
Vehicle or goods info (oil, livestock, etc.)
Accident photos
Enables emergency planning such as:
Doctor notification
Traffic diversion
Hazardous material handling
AORF detects an anomaly (sudden speed drop or operator trigger).
Latitude & Longitude of the accident are recorded.
ERM (Emergency Response Module) fetches local 911 and PSAP contact data using Precisely API.
Twilio sends SMS or email alerts with detailed info and images.
Authorities receive train info, accident pictures, and response guidance.
| Component | Purpose |
|---|---|
| Precisely API | Fetches nearest Public Safety Answering Point (PSAP) and emergency contacts |
| Twilio | Sends real-time SMS and email alerts |
| IoT Sensors / Hummingbird Controller | Detects sudden speed changes and triggers ERM |
| Raspberry Pi (Next Phase) | Runs model, captures live video, and processes ML-based obstacle detection |
Hardware
Train model with Hummingbird controller
Sensors for speed and obstacle detection
(Planned) Raspberry Pi integration for image and video processing
Software
Python for API integration and communication logic
Precisely API for emergency contact retrieval
Twilio API for automated messaging
(Planned) ML model for obstacle validation
Slide Deck https://www.canva.com/design/DAFTSs_13Bc/C1jSLge_f5LW1sYnImLOXQ/edit?utm_content=DAFTSs_13Bc&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton
Integrate computer vision for accurate obstacle detection
Launch drone surveillance for accident response validation
Implement Raspberry Pi deployment for portability and real-time processing
Python
Precisely API
Twilio API
Hummingbird Controller
Raspberry Pi (planned)
Machine Learning (planned)