Namma Yatri Peak Hour Solution

Overview

This project presents an AI-driven solution for optimizing supply-demand balance during peak hours for Namma Yatri. The solution addresses one of the biggest challenges in urban mobility: the imbalance between supply and demand during peak hours, where riders struggle to find autos when they need them most, while drivers may reject trips due to traffic, distance, or fare concerns.

Key Metrics

• 23% Peak Hour Ride Denials
• 18 min Average Wait Time (Peak)
• ₹950 Average Daily Driver Earnings
• 72% Average Acceptance Rate

Solution Components

Core AI Systems

1. Hotspot Prediction

   • Uses LSTM Neural Networks
   • Predicts high-demand areas by time of day
   • Trained on 6 months of historical ride data
   • Forecasts demand for the next 24 hours in 1-hour intervals

2. Dynamic Pricing

   • Q-Learning Reinforcement Learning model
   • Optimizes pricing to balance supply and demand
   • State space includes demand level, supply level, time of day, day type, weather
   • 11 discrete price multipliers ranging from 1.0x to 2.0x

3. Ride Matching

   • Intelligent route optimization algorithm
   • Allows multiple passengers to share a single vehicle
   • Reduces costs for riders and increases earnings for drivers

4. Queue Management

   • Priority-based allocation system
   • Fair ride distribution during peak hours
   • Reduces wait times and ride denials

User Experience Features

1. Namma Yatri Plus

   • Subscription-based model for premium features
   • Priority queue access for subscribers
   • Reduced surge pricing with cap on maximum multipliers
   • Scheduled rides up to 7 days in advance
   • Multiple subscription tiers (Basic, Silver, Gold)

2. Loyalty Queue System

   • Priority based on ride history
   • Ride history rewards for frequent users
   • Referral benefits for user acquisition
   • Peak hour passes earned through off-peak usage

3. WhatsApp Integration

   • Direct queue access through WhatsApp
   • Real-time ride status notifications
   • Voice message support for booking
   • Wider accessibility for users with limited data

Driver Engagement

1. Experience-Based Incentives

   • Rewards for app tenure and usage
   • Performance tiers with elite status
   • Training rewards for skill development
   • Mentorship program for experienced drivers

2. Real-time Recommendations

   • Location guidance by time of day
   • Personalized suggestions based on driver preferences
   • Reduces idle time and deadheading

Expected Outcomes

For Drivers

• 15-20% increase in daily earnings
• Reduced idle time and deadheading
• More predictable earning opportunities
• Transparent and fair pricing

For Riders

• 40% reduction in wait times during peak hours
• 30% fewer ride denials
• More reliable service
• Transparent surge pricing with explanations

For Namma Yatri

• 25% increase in platform usage
• Higher driver retention
• Improved brand trust
• Data-driven operational insights

Implementation Timeline

Feature	Development	Testing	Rollout	Total (Months)
WhatsApp Integration	2	1	1	4
Namma Yatri Plus	1	1	2	4
Loyalty Queue System	1	1	2	4
Driver Experience Incentives	1	1	2	4

Technical Implementation

LSTM Neural Networks for Hotspot Prediction

• Input Features: Historical ride data, time of day, day of week, weather conditions, special events
• Architecture: Multi-layered LSTM with dropout for regularization
• Benefits: Captures complex temporal patterns, adapts to changing patterns, provides accurate predictions

Reinforcement Learning for Dynamic Pricing

• Reward Function: Balances driver earnings with ride acceptance probability
• Learning Process: Updates Q-values based on observed rewards and transitions
• Benefits: Optimizes for both driver earnings and platform growth, adapts pricing in real-time

Clustering & Recommendation Systems

• Spatial Clustering: K-means algorithm to identify distinct hotspot areas
• Temporal Analysis: Time-series decomposition to identify recurring patterns
• Personalization: Collaborative filtering to match driver preferences with opportunities

Data Processing & Integration

• Data Preprocessing: Normalization, outlier detection, feature engineering
• Feature Extraction: Time-based features, geospatial features, weather data integration
• Deployment: Containerized microservices architecture for scalability

Dashboard Components

The solution includes a comprehensive dashboard with the following sections:

1. Overview - Summary of the solution, key metrics, and expected outcomes
2. Hotspot Analysis - Visualization of high-demand areas and LSTM prediction results
3. Dynamic Pricing - Interactive price multiplier heatmap based on various conditions
4. Ride Sharing - Simulation of ride sharing benefits and cost savings
5. Driver Recommendations - Personalized guidance for drivers to maximize earnings
6. Interactive Tools - Tools for exploring different scenarios and parameters
7. Waiting Queue - Management interface for the priority-based queue system

Future Scope

Beyond the current implementation, we've identified several high-potential enhancements:

1. Enhanced WhatsApp Integration

• Voice commands in multiple languages
• Integration with payment systems
• Chatbot assistance for common queries

2. Advanced Subscription Tiers

• Family plans for multiple users
• Business accounts for corporate clients
• Integration with other mobility services

3. Expanded Loyalty Program

• Gamification elements to increase engagement
• Partnerships with local businesses for rewards
• Seasonal promotions and challenges

4. Driver Development Platform

• Comprehensive training modules
• Financial management tools
• Community forums and support networks