01
The Challenge
Campus taxi dispatching suffered from inefficient ride allocation — pickup times averaged 10+ minutes, riders experienced long waits, and taxis sat idle between shifts. The challenge: optimize O(n) brute-force dispatch into an O(log n) system.
02
The Approach
Designed a dual-indexing architecture combining Red-Black Trees for geospatial proximity lookups with Min-Heaps for priority-based request scheduling. This allowed O(log n) insertion, deletion, and nearest-neighbor queries.
03
The Solution
- Red-Black Tree index for real-time geospatial location tracking and nearest-taxi lookup
- Min-Heap scheduler for priority queue request management
- Dual-indexing architecture enabling O(log n) operations across both dimensions
- Feature extraction pipeline reducing idle time through predictive positioning
04
The Impact
0% Faster Pickups
0% Shorter Wait Times
0% Less Idle Time
0% Test Cases Passed
Tech Stack
C++ Red-Black Trees Min-Heaps Algorithm Design
“Sometimes the most impactful optimization is choosing the right data structure — Red-Black Trees and Min-Heaps turned a brute-force dispatch into a logarithmic-time system.”