Route Optimisation (Google Maps Clone)
Web DeveloperImpact
- Built dual-implementation system: Flask web app (OSMnx/NetworkX) + 650-line from-scratch C++ Dijkstra engine
- Implemented hand-built data structures: binary min-heap with decrease-key, adjacency-list graph, and QuickSort
- Engineered real-time hospital POI search with dynamic radius expansion (5km to 10km fallback)
- Integrated interactive Leaflet maps via Folium with turn-by-turn route visualization and ETA computation
Skills
Project Abstract & Details
A real-time route optimization algorithm integrated into a web-based application that computes the shortest and most efficient paths using live data. The system dynamically processes factors such as distance, travel time, and routing constraints to deliver optimal navigation results. Paired with a user-friendly interface and interactive map features comparable to Google Maps, it enables seamless route visualization, turn-by-turn guidance, and responsive updates to enhance usability and decision-making.
Key Features
Dijkstra Algorithm
Dual implementation: Python (NetworkX library) + 650-line from-scratch C++ with hand-built binary min-heap and adjacency-list graph
OSM Integration
Real-world road network data via OSMnx — downloads, parses, and converts OpenStreetMap data into navigable graph structures
POI Search Engine
Hospital/landmark search with dynamic radius expansion (5km → 10km fallback) and proximity ranking
Hand-Built Data Structures
Binary min-heap with decrease-key operation, adjacency-list graph representation, and QuickSort for path post-processing
Flask Web App
Python Flask backend with route computation API, interactive Folium map rendering, and Leaflet.js frontend
Turn-by-Turn Navigation
Route visualization with waypoint markers, distance labels, and estimated travel time at each segment
Architecture
Dual-architecture system: (1) Python/Flask web app uses OSMnx to fetch road networks from OpenStreetMap, NetworkX for graph construction and Dijkstra routing, Folium for Leaflet map rendering with turn-by-turn visualization. (2) Standalone C++ engine with hand-built binary min-heap, adjacency-list graph, and Dijkstra implementation. Both compute shortest paths from weighted graphs using edge distance as cost. Hospital POI search uses KD-tree spatial indexing with progressive radius expansion.
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