About Bline
Inspiration
In emergency situations, every second counts—whether it be a cardiac arrest, a car accident, or a life threatening injury, reducing ambulance response times can mean the difference between life and death. Inspired by this reality, we set out to develop a tool that leverages real time traffic data to optimize emergency response time. Our main goal was to create a solution that helps emergency services save precious seconds while also empowering them with the tools they need to make faster smarter decisions. Google maps sucks for emergency routing (per our market analysis), so we set out to make our own emergency vehicle-specific routing algorithm
What it does
BLINE is an intelligent ambulance routing application designed to find the fastest route to the scene of an emergency. Using OSM's graphing features, BLINE accounts for traffic lights into a manually-adjusted Dijkistra's algorithm to determine an optimal route, continuously updating to ensure ambulances avoid delays to reach their destinations as quickly as possible. Even saving 10-15 seconds can dramatically improve survival rates in critical situations, and BLINE is here to help.
How we built it
We built our project using React Native to work as a mobile app, querying several Google Cloud APIs client-side to generate and visualize the map that we calculate from our algorithm. We then use OSM and a variety of other packages backend-side, and, after taking in the long and lat coordinates of the ambulance piped to our Flask API from the client-side, use our custom route-finding algorithm to calculate the best route (based off of Dijkstra's and other graph operations). We then return this to our frontend as a Map Polyline object to be rendered by Google Maps API.
Challenges we ran into
Understanding OSM's documentation to utilize its functions for reasons such as isolating nodes and edges to manually manipulate them proved to be a challenge, as the documentation was very scattered, and we were learning it from essentially having no experience with the library beforehand. However, we steadily were able to determine how functions related to one another in order to properly use them in our modified pathfinding algorithm. Another challenge was that our algorithm, despite optimizing in the best way we deemed possible, takes an extremely long time to run due to our lack of computing power and need for more low level control. With more time and further research, we would be able to address this problem more fully.
Accomplishments that we're proud of
Life-Saving Potential: Knowing that Bline has the potential to save lives by improving response times is definitely our greatest accomplishment. Original Algorithm: Our search algorithm, utilizing the shortest route function in the OSM library, was designed completely from scratch, as we wanted to manually control the weights based on traffic lights, since we did not have access to real-time traffic light data.
What we learned
The Importance of Real Time Data: Accurate and up to date information is the cornerstone of any solution designed for emergency response.
Every Second Counts: Even small reductions in response times can have a major impact on patient outcomes, reestablishing the importance of our mission.
What's next for Bline
Most importantly, taking more time to improve our algorithm to take more factors into consideration like hazards in routes, better congestion consideration, etc., to truly become a rival for Google Maps. Second would be to actually get our hands on live Nexar and INRIX data to use for our app, given that both of them require a lot of money to get access to.
Log in or sign up for Devpost to join the conversation.