The problem?
According to World Health Organization’s studies, the number one cause of death globally are Cardiovascular diseases, this includes heart attacks, cardiac arrests and strokes. It is of extreme importance that the victims of cardiovascular diseases are quickly attended by EMS staff in order to increase their odds of survival.
Moreover, according to WHO road accidents are the 8th most common death cause, road accident victims require speedy attendance by EMS while at the same time it is harder to have access at the scene because of the blocked roads that occur due to the accident itself.
It is apparent that in times of emergency even mere minutes can make a huge difference and it would be unacceptable for traffic to be the determining factor between life and death, between saving or destroying property. This is where our app comes in.
Solution:
We created an app that is to be used by emergency services, such as EMS, police and fire departments, aiming to reduce traffic on the roads when an emergency vehicle needs to pass by.
Think about that, a big fire has occurred on a building and the lives of many people are at risk, the need of both firefighters’ and doctors’ help is crucial. That is the reason why E.V.A.T.O. tries to reduce the response time of the emergency services by sending proper messages on the vehicles blocking their way.
How does that work, though?
Well, with the help of NASA’s satellites (gROADS)and Google Maps it can observe the traffic in the path those vehicles need to follow.
Firstly, it finds the best and fastest route from the station, or the vehicle that is closest, to the accident (and back when it comes to ambulances) based on the data NASA and Google provides. This information ,alongside with the importance of the incident and the incident’s details are being shared with the appropriate vehicle. It gives the people working in the call center the ability to select one of three options depending on the level of emergency of the accident. When the incident is not particularly urgent the person that handles the app may select to send the message only at the areas with heavy or moderate traffic (red or orange color on the map). Where as in the case of extreme emergency the option of sending the message to every vehicle on the entire path is advised.
When the choice has been made, an appropriate message gets sent via Cell Broadcast, a method of sending messages to multiple mobile telephone users in a defined area at the same time.
The story so far
According to researches, Greece is among the countries in the EU with the highest rate of road accidents that need immediate first aid care, which due to traffic cannot always arrive at the optimal time. Adding criminal activity, an old problem and fires that occur ever so often, especially in the warm days of summer, the need for emergency services is significant.
That’s what inspired us to create an App that helps people on earth with exactly that, their crucial everyday life problems. And what’s better way to do it than taking up on the challenge “Human, with People”?
Firstly, we decided to develop our idea to make sure that is feasible with realistic situations, numbers and even coding methods. After that, we had to think about who will actually use our App, to make it user-friendly and not too complicated, as it concerns life or death situations.
Furthermore, we had to find a way to send every single vehicle on a specific road the message of an upcoming emergency vehicle passing by, and we came up with the idea of Cell Broadcasting.
Finally, our project was ready to code and test with real life data.
Unfortunately, due to the limitation of time we were not able to write code that works for every case so we decided to not include any code in our project. We developed a program, though, that roughly estimates the improvement in response times that occurs because of our App, to highlight the important impact it has to real life circumstances.
It is written in Python, based on statistical analysis and real-time data we collected from Google Maps, and NASA gROADS. The first one is used for its traffic data, whereas the other one for its topological. We, also worked with the App InVision to demonstrate how our app will ideally look to the people who would use it. All of which is included on our power point presentation.
Certainly, we faced problems along the way, most of them regarding our confidence on our idea. We questioned if at the end of the day our app would make any difference or even if we were able to code and develop it in the first place. Luckily, the short amount of time we had made us push ourselves to develop the idea that the whole group thought to be astounding.
Related works
While researching for other similar applications we found that most of them focus on optimizing the time it takes to contact the emergency services and do nothing to improve the actual travel time of the emergency vehicle by reducing the traffic, which is the main innovation of our project. Apart from that we encountered many projects that were hardware-based and had to be built from scratch (such as traffic light switches or emergency dedicated roads), where as our project is software-based and the few hardware parts that are needed (e.g. Signal Antennas) are already built.
9-Slides Presentation at: https://drive.google.com/file/d/1eckCxylcUJnBWkSallo6PVTsDiVkqZZ2/view?usp=sharing
Data & Resourcers
https://www.esa.int/esapub/bulletin/bullet115/chapter7_bul115.pdf
https://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1
https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries#:~:text=Key%20facts,road%20traffic%20crashes%20by%202020.
https://ec.europa.eu/transport/media/news/2020-06-11-road-safety-statistics-2019_en
https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
https://www.bmj.com/content/322/7299/1385.abstract
https://en.wikipedia.org/wiki/Cell_Broadcast
https://www.engineergirl.org/108047/The-Solution-to-Ambulance-Delays
https://play.google.com/store
https://www.google.com/maps
https://www.python.org/
https://matplotlib.org/
https://www.invisionapp.com/
https://numpy.org/
https://pandas.pydata.org/