Traditional taxi pricing models fail to adapt to real-time demand patterns, weather conditions, and traffic situations. This leads to lost revenue during high-demand periods and poor service availability during low-demand times. The challenge was to build a system that could process thousands of ride requests per minute and adjust pricing dynamically.
Solution Architecture
Implemented a real-time streaming architecture that processes ride requests, weather data, and traffic information to calculate optimal pricing. The system uses machine learning models to predict demand and adjusts fares within regulatory limits to maximize both revenue and service availability.
Stream Ingestion
Kafka captures ride requests, GPS data, weather feeds, and traffic information in real-time
Real-time Processing
Spark Streaming processes events and applies ML models for demand prediction
Price Optimization
Dynamic pricing algorithm calculates optimal fares based on supply/demand patterns
Response System
Redis caches pricing decisions and serves them to mobile apps within 100ms
Impact & Outcomes
Revenue Increase
Dynamic pricing improved overall revenue by 18% during peak hours
Response Time
Achieved sub-100ms pricing decisions for 99.9% of ride requests
Service Availability
Reduced wait times by 22% through better supply/demand balancing
System Throughput
Processed 50,000+ pricing decisions per minute during peak periods
Tools and technologies chosen to simulate real production pipelines and deliver reliable, scalable solutions.
Explore More Projects
See how I solve different data engineering challenges across various industries and use cases.
View All Projects