I Built UberSim v2.0: A Production-Grade Urban Mobility Intelligence Platform 🚗🧠

Every time you open Uber and see a 2.1× surge multiplier, a complex system has already predicted demand, optimized prices, matched drivers, and logged events for future learning — all within milliseconds.

I wanted to understand how those systems work.

So I built UberSim v2.0.

A Python-based urban mobility intelligence platform that simulates the core engineering challenges behind modern ride-sharing marketplaces.

Instead of building another dashboard project, I wanted to recreate the intelligence layer behind a ride-sharing platform from scratch.

🚀 What's Inside?

🧠 Demand Forecasting

• Spatio-temporal demand prediction (R² = 0.89)
• Weather effects, seasonality, lag features, and neighboring zone influence
• Predicts ride demand across multiple city zones

🕸️ Graph Neural Networks

• Models the city as a graph
• Nodes = city zones
• Edges = historical trip flows
• Captures spatial mobility patterns that traditional models miss

🤖 Reinforcement Learning Pricing

Built a PPO-based surge pricing engine that learns pricing policies instead of relying on hand-crafted rules.

Optimizes multiple objectives simultaneously:

• 📈 Platform revenue
• 🚕 Driver earnings
• 😊 Rider welfare
• ⏱️ Wait times
• ⚖️ Fairness constraints

One interesting finding:

The RL agent learned to gradually increase surge prices instead of aggressively reacting to demand spikes. This behavior wasn't explicitly programmed.

⚡ Kafka-Style Real-Time Streaming

Implemented an event-driven architecture with:

• Ride request streams
• Driver status updates
• Pricing events
• Match results

Supports historical replay and live marketplace metrics.

🧠 Driver State LSTM

Predicts four operational driver states:

• online_idle
• online_busy
• relocating
• offline

Built entirely in NumPy with Backpropagation Through Time and Adam optimization.

🧪 Counterfactual A/B Testing

Implemented production-style experimentation techniques:

• IPS (Inverse Propensity Scoring)
• Doubly Robust Estimation
• CUPED variance reduction
• Bootstrap confidence intervals

This allows evaluating policies without deploying every experiment in production.

🗺️ Multi-Modal Transit Planning

Journey planning across six transportation modes:

• 🚗 Rideshare
• 🚌 Bus
• 🚇 Subway
• 🚲 Bike
• 🛴 Scooter
• 🚶 Walking

Uses A*/Dijkstra optimization to balance:

• Travel time
• Cost
• CO₂ emissions
• Number of transfers

💡 What I Learned

The hardest problem isn't maximizing revenue.

It's maximizing revenue while remaining fair.

Without constraints, optimization naturally prioritizes high-demand areas and disadvantages low-supply neighborhoods.

Adding fairness fundamentally changes the optimization landscape.

Some other takeaways:

• RL discovers strategies humans don't explicitly program.
• GNNs capture spatial relationships that tabular models miss.
• Causal inference is essential for policy evaluation.
• Pure NumPy is more powerful than people think.

🛠️ Tech Stack

Python · Streamlit · Plotly · Stable-Baselines3 · NetworkX · NumPy · Scikit-Learn · Gymnasium

🔮 What's Next?

• [ ] Graph Attention Networks (GAT)
• [ ] Multi-Agent Reinforcement Learning
• [ ] Real Kafka Broker Integration
• [ ] WebGL City Visualization
• [ ] Real-World Dataset Integration (NYC TLC, Chicago Divvy)

🔗 GitHub

https://github.com/kh-bikash/ubersim

Feedback, ideas, and contributions are welcome 🚀