The problem Map-EV solves

Enhanced Route Visualization and Planning
Use Case: Ideal for navigation and travel planning applications.
Benefits: Users can visually plot routes from a starting point to a destination, making trip planning more intuitive and interactive. The inclusion of alternative routes offers flexibility and choice, catering to various preferences or needs, like avoiding tolls or taking scenic paths

Proximity Analysis for Points of Interest
Use Case: Useful for logistics, delivery services, tourism, and EV charging network apps.
Benefits: The component can display markers within a specified radius of the route, helping users find services or points of interest along their journey. This functionality is especially valuable for identifying essential stops like gas stations, EV charging points, restaurants, or tourist attractions.

Customized and Informative Mapping Experience
Use Case: Applicable in urban planning, real estate, and outdoor recreational activities.
Benefits: By customizing marker colors and displaying route distances, the component enhances the user's ability to interpret map data quickly. It can aid in decision-making processes for activities like property evaluations based on proximity to key routes or planning hiking and biking trails with distance measurements.

Challenges we ran into

1. Integrating External APIs and Libraries
   Challenge: Using Mapbox and Turf.js libraries effectively, especially for complex tasks like route planning and proximity analysis, can be challenging. This includes handling API requests, processing geospatial data, and rendering it on the map.
   Solution: Overcoming this involves thoroughly understanding the documentation of these libraries, experimenting with different functions, and possibly seeking help from community forums or Q&A sites like Stack Overflow. Breaking down the problem into smaller, manageable tasks can also help in gradually building up the desired functionality.

2. Performance Optimization
   Challenge: Rendering a large number of markers or complex routes can lead to performance issues, especially on less powerful devices or slow internet connections.
   Solution: To address this, optimizing the number of markers rendered and simplifying route data can be helpful. Techniques like clustering markers or dynamically loading markers based on the viewport can significantly improve performance. Additionally, ensuring efficient data fetching and handling (like minimizing API calls and processing data optimally) is crucial.