Ambulance routing problem with dynamic traffic constraints
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Updated
Dec 12, 2019 - Python
Ambulance routing problem with dynamic traffic constraints
Main aim of the project is to develop a machine learning based system for detecting vehicles that drive up to the gate and to classify them into emergency services and civil cars. This may have a significant impact on the speed of gate opening and reduce the human associated problems and risks.
A discrete-event simulator of the emergency response process of electric and diesel ambulances.
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a real-time ambulance detection using YOLOv8 and Streamlit—detect in images or live webcam.
Redline AI is a AI-powered IVR system designed to assist first responders by transforming emergency voice calls into structured, actionable intelligence in real time. It integrates Twilio telephony with Google Cloud Speech-to-Text and Translation APIs to transcribe and translate calls, applies automated severity analysis
End-to-end emergency response platform with ML-driven triage, ambulance dispatch, and hospital routing. FastAPI + React + 5 trained models (XGBoost / LightGBM / CatBoost ensembles, LSTM hotspot forecaster) plus an optional Groq/Gemini caller-transcript intake layer.
🚨 An AI-powered system that detects emergency vehicles using YOLOv8 and manages traffic signals to give them priority at intersections.
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Ambulance-priority emergency-response platform: a real-road OpenStreetMap traffic simulator with explainable, replayable dispatch and green-corridor routing.
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