The rapid growth of motor vehicles without adequate advances in road safety has resulted in a huge loss of human life. With the growing population, the use of cars has also increased, leading to more traffic, road hazards and more road accidents. One of the causes of death of a person in a car accident is also the non-availability of first aid due to the inability of the paramedics to gather the details of the accident in time and subsequently dispatch the proper rescue forces to the accident site. An accident may involve multiple victims and hence it is important to provide adequate number of rescue personnel to rescue all the victims. The ideal survival situation after an accident is that rescue forces approach the accident scene as quickly as possible. In this thesis, analyze the current status of rescue centers, the technologies used and needed to effectively assess the severity of the accident without the need of human intervention. Also analyze the available data or datasets from previous traffic accidents. Design a system that will be based on information collected in real time while the vehicle is moving and then evaluate this data. Implement an intelligent web-based system for rescue centers, and try to design an algorithm that will evaluate the severity of the traffic accident and automatically assign the appropriate number of rescue units to be sent to the accident scene. Validate the proposed solution on real-time data collected while the vehicle is moving and evaluate the result.
This app si a MERN fullstack implementation in MongoDB, Expressjs, React/Redux, Nodejs.
MERN stack is the idea of using Javascript/Node for fullstack web development.
git clone https://github.com/DanielCok17/Diploma-thesis.git./start-local.shdocker-compose build
docker-compose -f ./backend/docker-compose.yml -f ./backend/docker-compose.dev.yml up -d --build
docker-compose -f ./frontend/docker-compose.yml -f ./frontend/docker-compose.dev.yml up -d --buildCopyright © 2023, Daniel Cok