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🚗 RideIQ

RideIQ is a mobile application that compares ride-hailing services in real time and detects pricing trends to help users avoid surge pricing and reduce wait times.

Built as a full-stack project with a Node.js backend and React Native (Expo) mobile client.


✨ Features

  • 📊 Real-time ride comparison across providers (simulated for development)
  • 📈 Price trend detection (rising / falling / stable)
  • ⏱ Pickup time estimation
  • 🔄 Live updates via repeated requests
  • 📱 Mobile interface built with React Native + Expo Router
  • 🌐 Backend API with Express
  • 🧠 Foundation for surge prediction using contextual signals

🏗 Architecture

📱 Mobile App (React Native)
        ↓ HTTP
🌐 Backend API (Express)
        ↓ Logic
📊 Trend Engine (in-memory history)
        ↓ Response
📱 UI updates in real time

🧪 Example Output

Uber  ---  $18  ---  Pickup 4 min  ---  📈 Rising
Lyft  ---  $16  ---  Pickup 6 min  ---  🟢 Stable

🛠 Tech Stack

Frontend (Mobile)

  • React Native
  • Expo Router
  • TypeScript
  • Axios
  • Safe Area Context

Backend

  • Node.js
  • Express
  • TypeScript
  • In-memory data store (V1)
  • REST API

🚀 Getting Started

1️⃣ Clone the repo

git clone https://github.com/YOUR_USERNAME/rideiq.git
cd rideiq

2️⃣ Run Backend

cd rideiq-api
npm install
npx ts-node-esm src/server.ts

Server runs on: http://localhost:3001

3️⃣ Run Mobile App

cd ../rideiq-mobile
npm install
npx expo start

Open with Expo Go on your device.


📌 Project Status

  • ✅ V1 Foundation Complete
  • 🔄 Real API integration planned
  • 🧠 AI prediction engine planned
  • 🗺 Map integration planned

💡 Motivation

Ride-hailing prices fluctuate unpredictably due to demand, traffic, and driver availability. RideIQ aims to provide transparency and decision support so users can choose the cheapest or fastest option before booking.


🔮 Future Improvements

  • Real Uber/Lyft API integration
  • Event-based surge forecasting
  • Weather-aware predictions
  • Multi-modal transport comparison
  • Push notifications for price drops
  • Machine learning models for forecasting

👨‍💻 Author

Mario Ezeh — Computer Science Student / Aspiring Software Engineer / Entrepreneur

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