🚍 Smart Bus System

Authors: Pavan G (@Sin-Kid)

A state-of-the-art, end-to-end IoT-enabled Smart Bus Tracking and Passenger Management System. The platform leverages real-time GPS tracking, RFID-based boarding/alighting detection, distance-based fare calculation, predictive analytics for passenger occupancy, and a modern fleet management dashboard.

🧭 System Architecture & Flow

The system consists of five main components interacting in real time:

                               +-----------------------------+
                               |    ESP8266 IoT Hardware     |
                               | (NEO-6M GPS & Dual RC522)   |
                               +--------------+--------------+
                                              |
                                              | HTTPS REST API (RPC / Express)
                                              v
                               +--------------+--------------+
                               |      Supabase Backend       |
                               |  - PostgreSQL Database      |
                               |  - Real-time Subscriptions  |
                               |  - PL/pgSQL RPC Functions   |
                               +-------+--------------+------+
                                       |              |
                Real-time subscriptions|              | REST API / JSON
                                       v              v
       +-------------------------------+--+        +--+---------------------------+
       |   React Admin Web Dashboard      |        |     React Native Expo App     |
       |  - Live fleet tracking on map    |        |  - Passenger live bus map    |
       |  - Route/schedule management     |        |  - Visual seat occupancy     |
       |  - Bus visual seat occupancy map |        |  - Balance & top-up wallet   |
       |  - Flask ML prediction panel     |        |  - Gemini AI route chatbot   |
       +----------------------------------+        +------------------------------+
                               ^
                               | Predictions API
                               v
               +---------------+---------------+
               |    Flask ML Predictor API     |
               |  - scikit-learn Random Forest |
               |  - Boarding/alighting forecasts|
               +-------------------------------+

Core Communication Flows

Telemetry Upload: The NodeMCU ESP8266 on the bus fetches GPS coordinates from the NEO-6M sensor every 10 seconds and invokes the handle_telemetry RPC endpoint.
Passenger Boarding: Passengers tap their RFID card on the Entry MFRC522 Scanner. The hardware validates the card, verifies a minimum balance of ₹50, opens an active trip, and increments the bus passenger count in Supabase.
Passenger Alighting: Passengers tap on the Exit MFRC522 Scanner when leaving. The system computes the fare, deducts it from the card balance, terminates the active trip, logs the transaction, and decrements the passenger count.
Live Visualization: The React Admin Web and React Native Expo Apps subscribe to Supabase tables using WebSockets. When telemetry or occupancy changes, the UI updates instantly without polling.
Demand Forecasting: The Admin panel queries the Flask Python ML service to predict passenger boarding and alighting at each upcoming stop using Random Forest Regressors.

🛠️ Database Schema

The PostgreSQL database schema is defined in schema.sql and includes the following primary tables, indexes, and relations:

erDiagram
    BUSES ||--o{ BUS_ROUTES : "has"
    BUSES ||--o{ BUS_STOPS : "has"
    BUSES ||--o{ BUS_SCHEDULES : "has"
    BUSES ||--o{ TRIPS : "operates"
    BUSES ||--o{ TELEMETRY : "sends"
    BUSES ||--o{ RFID_LOGS : "logs"
    CARDS ||--o{ TRIPS : "takes"
    CARDS ||--o{ TRANSACTIONS : "funds"
    CARDS ||--o{ RFID_LOGS : "logs"
    BUS_ROUTES ||--o{ BUS_STOPS : "contains"
    BUS_ROUTES ||--o{ BUS_SCHEDULES : "schedules"
    TRIPS ||--o{ TRANSACTIONS : "generates"

    BUSES {
        string id PK
        string name
        jsonb location
        timestamptz last_seen
        numeric speed
        numeric heading
        string status_message
        uuid current_stop_id
        string current_location_name
        integer capacity
        integer sim_occupied
        integer sim_leaving
    }

    BUS_ROUTES {
        uuid id PK
        string bus_id FK
        string name
        jsonb stops
        string source
        string destination
    }

    BUS_STOPS {
        uuid id PK
        string bus_id FK
        uuid route_id FK
        string name
        string code
        numeric lat
        numeric lon
        integer order
        string arrival_time
        numeric price
    }

    BUS_SCHEDULES {
        uuid id PK
        string bus_id FK
        uuid route_id FK
        time departure_time
        time arrival_time
        text_array days_of_week
        numeric fare
        string status
    }

    CARDS {
        string id PK
        string name
        string card_number
        numeric balance
        numeric total_recharges
        numeric last_recharge
        timestamptz last_recharge_date
        timestamptz last_seen
        string card_type
        string status
        uuid active_trip FK
    }

    TRIPS {
        uuid id PK
        string bus_id FK
        string card_id FK
        timestamptz start_time
        jsonb start_location
        string start_stop_name
        timestamptz end_time
        jsonb end_location
        string end_stop_name
        numeric distance_km
        numeric fare
        string status
    }

    TELEMETRY {
        uuid id PK
        string bus_id FK
        jsonb location
        numeric speed
        numeric heading
        timestamptz timestamp
    }

    TRANSACTIONS {
        uuid id PK
        string card_id FK
        uuid trip_id FK
        string bus_id FK
        string type
        numeric amount
        timestamptz timestamp
        string status
        string payment_method
    }

    RFID_LOGS {
        uuid id PK
        string bus_id FK
        string card_id FK
        string event_type
        timestamptz timestamp
    }

Performance Optimization Indexes

To ensure fast queries during high concurrent hardware updates, the following indexes are deployed:

idx_bus_schedules_bus_id & idx_bus_schedules_route_id: Accelerates stop and route schedules search.
idx_cards_card_number: Speeds up physical RFID card authorization matches.
idx_telemetry_bus_id & idx_telemetry_timestamp: Optimizes location history queries for mapping.
idx_rfid_logs_card_id & idx_rfid_logs_timestamp: Quick retrieval of boarding logs for user history screens.

⚡ Algorithms & APIs

The project implements specialized algorithms across the database layer, the Express middle tier, and the Python ML service.

1. Database-Level Control Logic (PL/pgSQL RPCs)

To keep the embedded client logic light and secure, all logical checks run inside Supabase via Remote Procedure Call (RPC) functions:

handle_bus_entry(p_card_uid, p_bus_id, p_lat, p_lon)
- Validation: Searches the cards table by UUID or custom card number. Returns an error if the card is not registered.
- Financial Control: Rejects the card if the current balance is < ₹50 (ensuring sufficient balance for the fare deduction at exit).
- State Control: Checks if active_trip is not null to prevent double entry scans.
- Resolution: Invokes resolve_stop_name to record the station name.
- Telemetry Sync: Creates a new row in trips (ongoing), updates the card's active_trip, adds to rfid_logs, and increments the bus's sim_occupied counter (capped at bus capacity).
handle_bus_exit(p_card_uid, p_bus_id, p_lat, p_lon)
- Validation: Fetches the card and verifies that an active_trip exists.
- Fare Collection: Charges a base flat fare of ₹50 (configurable or matching Express dynamic algorithm).
- State Resolution: Sets the card's active_trip back to NULL, updates trips status to completed, records the end_time, end_location, and end_stop_name.
- Accounting: Inserts a transaction record of type trip and logs to rfid_logs.
- Telemetry Sync: Decrements the bus's sim_occupied counter (bounded at a minimum of 0).
handle_telemetry(p_bus_id, p_lat, p_lon, p_speed)
- Logs incoming coordinate samples into the historical telemetry table.
- Updates the live location, heading, and current speed in the buses record.

2. Express Backend Middleware Algorithms (`functions/index.js`)

An Express API is also available to act as a custom device middleware, containing two critical algorithms:

A. Geolocation Stop Resolution (Haversine Formula)

When GPS telemetry is received, the backend compares coordinates with the route's predefined bus stops to determine the current stop name using the Haversine formula (distance over a sphere):

$$\text{distance} = 2 R \arcsin\left(\sqrt{\sin^2\left(\frac{\Delta \text{lat}}{2}\right) + \cos(\text{lat}_1) \cos(\text{lat}_2) \sin^2\left(\frac{\Delta \text{lon}}{2}\right)}\right)$$

function haversineKm(lat1, lon1, lat2, lon2) {
  const toRad = (v) => (v * Math.PI) / 180;
  const R = 6371; // Earth's radius in kilometers
  const dLat = toRad(lat2 - lat1);
  const dLon = toRad(lon2 - lon1);
  const a = Math.sin(dLat / 2) * Math.sin(dLat / 2) +
    Math.cos(toRad(lat1)) * Math.cos(toRad(lat2)) *
    Math.sin(dLon / 2) * Math.sin(dLon / 2);
  const c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
  return R * c;
}

If the calculated distance is less than 0.5 km (500 meters) from a stop coordinate, the bus is officially recognized as arriving at that stop.

B. Dynamic Route-Stop Price Fare Calculation

During the exit event /device/rfid, the API performs a dynamic progressive calculation. It locates the entry stop and exit stop indices within the route's stop list and sums up the custom price segments between them:

let fare = 10; // Default base fare fallback
if (ctx?.stopsList?.length > 0) {
  const sIdx = ctx.stopsList.findIndex(s => s.name === trips.start_stop_name);
  const eIdx = ctx.stopsList.findIndex(s => s.name === stopName);
  if (sIdx !== -1 && eIdx !== -1) {
    fare = 0;
    // Handle both directions (forward and reverse indexing)
    const [low, high] = sIdx < eIdx ? [sIdx, eIdx] : [eIdx, sIdx];
    for (let i = low; i <= high; i++) {
      fare += (Number(ctx.stopsList[i].price) || 10);
    }
  }
}

3. Machine Learning Forecasting Algorithm (`ml_prediction`)

The ML service operates on a Random Forest Regressor ensemble model implemented in Python using scikit-learn to forecast boarding and alighting.

Features Used in Forecasting:

stop_id: The ID of the station (spatial identifier).
hour: Hour of the day (0-23) (temporal cycle).
day_of_week: Day of week (0-6) (distinguishes weekdays from weekends).
is_peak_hour: Binary flag (1 for morning peak 7-9 AM, evening peak 5-7 PM; 0 otherwise).
current_occupancy: Live passenger count on the bus.
stop_sequence: Normalized stop order (0.0 to 1.0) along the route.
historical_avg: Rolling average passenger activity for the stop.

Training Pipeline:

Generates 180 days of hourly stop activity data with realistic peaks using a Normal distribution.
Trains two separate Random Forest Regressors (200 decision trees, max depth of 15) for:
1. Boarding Predictor: Estimates how many passengers will enter at the stop.
2. Alighting Predictor: Estimates how many passengers will exit.
Evaluates the model using MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), and R² scores.
Exposes predictions via a Flask REST API.

🎛️ Hardware & Control Methods

The smart bus hardware uses a NodeMCU ESP8266 board acting as a main controller. It interfaces with two MFRC522 RFID readers (separating entry and exit gates) and a NEO-6M GPS module.

📌 Pin Mapping Table

Component	Pin Label	Connected to NodeMCU Pin	Function / Note
RFID 1 (ENTRY)	SDA (SS)	D2 (GPIO 4)	Chip Select (Active Low) for Boarding Reader
	RST	D1 (GPIO 5)	Reset pin for Entry Reader
	SCK	D5 (GPIO 14)	SPI Shared Clock Line
	MOSI	D7 (GPIO 13)	SPI Shared Master Out Slave In
	MISO	D6 (GPIO 12)	SPI Shared Master In Slave Out
	GND	GND	Ground Connection
	3.3V	3.3V	Power (Max 3.3V - Do not connect to 5V!)

RFID 2 (EXIT)	SDA (SS)	D8 (GPIO 15)	Chip Select (Active Low) for Alighting Reader
	RST	D0 (GPIO 16)	Reset pin for Exit Reader
	SCK	D5 (GPIO 14)	SPI Shared Clock Line
	MOSI	D7 (GPIO 13)	SPI Shared Master Out
	MISO	D6 (GPIO 12)	SPI Shared Master In
	GND	GND	Ground Connection
	3.3V	3.3V	Shared 3.3V Power Line

GPS (NEO-6M)	TX	D4 (GPIO 2)	NodeMCU Software Serial RX
	RX	D3 (GPIO 0)	NodeMCU Software Serial TX
	VCC	3.3V / 5V	VCC Power Input
	GND	GND	Shared Ground

🔄 Firmware Control Loop & Logic

The firmware folder contains two alternative versions depending on your hardware assembly:

Single RFID Reader with GPS (smart_bus_single_rfid_gps): Best for single reader setups where the system automatically toggles entry/exit based on the active trip status.
Dual RFID Reader without GPS (smart_bus_dual_rfid_no_gps): Best for setups with distinct Entry and Exit gates, utilizing two MFRC522 readers.

Key control loop features include:

SPI Bus Multiplexing: Because both RFID reader modules share the SPI lines (D5, D6, D7), the NodeMCU selects which reader it is communicating with by pulling its corresponding Slave Select pin (D2 or D8) low.
GPS Frame Parsing: The firmware continuously reads incoming serial bytes from the GPS module in loop() and pipes them to the TinyGPSPlus parser. Valid location fixes update internal latitude/longitude coordinates.
5-Second Software Tap Debouncing: To prevent passengers from triggering double charges if their card lingers near the RF field, the system implements a software timer:
```
if (uid == lastUidEntry && (millis() - lastTimeEntry < 5000)) {
    Serial.println("Ignored Duplicate Entry Tap: " + uid);
    return;
}
```
Asynchronous Telemetry Upload: Telemetry data is sent to the server every 10 seconds via a non-blocking millis() delta check.
HTTPS Client Security: The board uses WiFiClientSecure with .setInsecure() mode to skip SSL fingerprint validation, making it light enough to run on a cheap ESP8266 chip without certificate management.

📂 Project Structure Tree

smart-bus-supabase/
├── admin-web/                      # React Fleet Management Dashboard (Vite)
│   ├── dist/                       # Production build directory
│   ├── public/                     # Static assets (images, icons, custom diagrams)
│   ├── src/
│   │   ├── components/             # Reusable UI dashboard elements
│   │   │   ├── ThemeToggle.jsx     # Dark/Light mode theme button
│   │   │   ├── ConnectionStatus.jsx# Database synchronization status indicator
│   │   │   ├── LogsViewer.jsx      # Streaming logs table
│   │   │   ├── Sidebar.jsx         # Sidebar navigation component
│   │   │   ├── MLPredictionPanel.jsx# Connects to Flask to render stop prediction charts
│   │   │   ├── TimePicker.jsx      # Time picker element for schedules
│   │   │   ├── BusSeatMap.jsx      # Interactive seat grid mapping capacity occupancy
│   │   │   ├── RouteTimeline.jsx   # Live visual representation of bus stops
│   │   │   ├── PassengerFrequencyChart.jsx # Hourly passenger frequency chart
│   │   │   └── RoutePassengerChart.jsx     # Route-by-route boarding/alighting stats
│   │   ├── context/
│   │   │   └── ThemeContext.jsx    # React Context Provider for app theme state
│   │   ├── pages/                  # Main Dashboard Views
│   │   │   ├── Dashboard.jsx       # Global fleet status overview page
│   │   │   ├── BusesPage.jsx       # Add, modify, or update bus capacities
│   │   │   ├── RoutesPage.jsx      # Map out stops and create routes
│   │   │   ├── SchedulesPage.jsx   # Dispatch schedules for buses
│   │   │   └── StatusPage.jsx      # Raw telemetry tracking details page
│   │   ├── utils/
│   │   │   ├── supabaseTest.js     # DB connectivity utility
│   │   │   └── simulatedData.js    # Local backup generators for testing
│   │   ├── App.jsx                 # Client entrypoint and route definitions
│   │   ├── main.jsx                # DOM mounting
│   │   └── styles.css              # Custom styling tokens (vanilla CSS & layouts)
│   ├── package.json
│   └── vite.config.js
│
├── expo-user-app/                  # React Native Mobile App for Passengers
│   └── user-app/
│       ├── assets/                 # App assets (icons, splash screens, seat maps)
│       ├── components/             # Custom mobile view components
│       ├── screens/                # Mobile application views
│       │   ├── WelcomeScreen.js    # Welcome splash page
│       │   ├── LoginScreen.js      # User registration / account sign-in
│       │   ├── HomeScreen.js       # Main routing hub and quick links
│       │   ├── FindBusScreen.js    # Search routes and upcoming buses
│       │   ├── BusListScreen.js    # Displays list of active buses
│       │   ├── BusInfoScreen.js    # Specific details on a single bus
│       │   ├── BusRouteScreen.js   # Route stop list and timeline
│       │   ├── BusLiveScreen.js    # Map view with live markers using React Native Maps
│       │   ├── BusOccupancyScreen.js# Renders physical seat layout occupancy map
│       │   ├── CardInfoScreen.js   # Linked RFID card overview and stats
│       │   ├── CardHistoryScreen.js# Log of transactions and recent bus trips
│       │   ├── TopUpScreen.js      # Top up virtual wallet page
│       │   ├── PaymentDummyScreen.js# Simulated payment gateway
│       │   ├── QRCodeScreen.js     # Virtual RFID barcode generator
│       │   ├── ChatbotScreen.js    # AI voice/text chatbot support for passengers
│       │   └── OtherScreens.js     # Settings and Profile
│       ├── utils/                  # Map rendering helpers
│       ├── supabaseConfig.js       # Supabase Mobile Client configuration
│       ├── App.js                  # Navigation Container setup (Stack/Tab)
│       └── package.json
│
├── functions/                      # Serverless APIs & ML Services
│   ├── index.js                    # Express.js REST middleware (device-telemetry & dynamic fare)
│   ├── check_status.js             # Utility checking Edge server status
│   ├── ml_prediction/              # Python Random Forest forecasting service
│   │   ├── api.py                  # Flask API server exposing prediction endpoints
│   │   ├── generate_data.py        # Generates historical dataset (training_data.csv)
│   │   ├── train_model.py          # Builds & trains Random Forest Regressor models
│   │   ├── simple_bus_viz.py       # Matplotlib script plotting occupancy reports
│   │   ├── import_matlab_data.py   # Imports generated MATLAB dataset files
│   │   ├── requirements.txt        # Python dependency file
│   │   ├── setup.sh                # Bootstraps dependencies, generates data, trains model
│   │   └── retrain.sh              # Backs up old weights and triggers model retrain
│   ├── package.json
│   └── node_modules/
│
├── arduino/                        # NodeMCU Microcontroller Firmware
│   ├── smart_bus_single_rfid_gps/  # Firmware for Single RFID + GPS configuration
│   │   └── smart_bus_single_rfid_gps.ino
│   ├── smart_bus_dual_rfid_no_gps/ # Firmware for Dual RFID configuration (no GPS)
│   │   └── smart_bus_dual_rfid_no_gps.ino
│   └── hardware_block_diagram.png  # Visual breadboard diagram for pin wiring
│
├── schema.sql                      # Complete Database Setup Script
├── seed_data.sql                   # Sample Data for routes, stops, schedules, & cards
├── SETUP.md                        # Exhaustive deployment steps
└── HARDWARE_SETUP.md               # Hardware-specific wiring schematics

🚀 Execution & Startup Guide

Follow these commands to deploy, run, and develop the different parts of the system.

1. Database Setup (Supabase)

Log in to Supabase Console.
Create a new project.
Open the SQL Editor in the dashboard.
Copy the entire contents of schema.sql into the SQL Editor and click Run (this creates the tables, indexes, and RPC database functions).
Copy the contents of seed_data.sql into the editor and click Run (this populates test buses, schedules, routes, stops, and cards).

2. Run the Machine Learning API Server

Deploy the Python environment to enable passenger demand predictions.

# Navigate to prediction directory
cd functions/ml_prediction

# Run setup script (installs requirements, generates training data, and trains model)
chmod +x setup.sh
./setup.sh

# Start the Flask prediction server
python3 api.py

The forecasting server will start running at http://localhost:5000.

3. Run the Express Backend Service

Run the Express middleware for stop resolution and dynamic pricing.

# Navigate to functions folder
cd functions

# Install dependencies
npm install

# Configure environment keys in .env (Use .env.example as template)
# Add your SUPABASE_URL, SUPABASE_SERVICE_ROLE_KEY, and a custom DEVICE_TOKEN

# Start the service
npm start

The Express server will launch on port 3000.

4. Run the Fleet Management Admin Web Dashboard

Start the administrative React front-end application.

# Navigate to dashboard folder
cd admin-web

# Install package dependencies
npm install

# Set up environment configuration (.env)
# Create a .env file containing:
# VITE_SUPABASE_URL=https://your-project.supabase.co
# VITE_SUPABASE_ANON_KEY=your-anon-key

# Launch Vite local hot-reload server
npm run dev

The dashboard will be active at http://localhost:5173.

5. Run the Passenger Mobile App (Expo)

Run the React Native Expo app on an emulator or a physical device via Expo Go.

# Navigate to mobile project folder
cd expo-user-app/user-app

# Install package dependencies
npm install

# Set up environment configuration (.env)
# Create a .env file containing:
# EXPO_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
# EXPO_PUBLIC_SUPABASE_ANON_KEY=your-anon-key

# Launch Expo packager
npx expo start

Press a to run on an Android emulator, i to run on iOS Simulator, or scan the terminal QR code using the Expo Go app on your iOS/Android smartphone.

6. Flash the Microcontroller (Arduino)

Open the Arduino IDE.
Install the ESP8266 Board support via Preferences URL: http://arduino.esp8266.com/stable/package_esp8266com_index.json
Install required libraries from the Library Manager:
- MFRC522 by GithubCommunity
- TinyGPSPlus by Mikal Hart
- ArduinoJson by Benoit Blanchon
Open either arduino/smart_bus_single_rfid_gps/smart_bus_single_rfid_gps.ino or arduino/smart_bus_dual_rfid_no_gps/smart_bus_dual_rfid_no_gps.ino depending on your hardware assembly.
Edit the configuration settings in the .ino file with your parameters:
- WIFI_SSID
- WIFI_PASS
- SUPABASE_URL (Without https:// prefix, e.g., xyz.supabase.co)
- SUPABASE_KEY (Supabase Anon public key)
Select your board (e.g., NodeMCU 1.0 (ESP-12E Module)) and the correct COM Port.
Click Upload to flash the firmware.

✅ Integration Verification Checklist

To confirm that all systems are successfully set up and communicating:

Verify Database population: Run this SQL query in the Supabase editor:

SELECT 'Buses' as table_name, COUNT(*) as count FROM buses UNION ALL
SELECT 'Routes', COUNT(*) FROM bus_routes UNION ALL
SELECT 'Stops', COUNT(*) FROM bus_stops UNION ALL
SELECT 'Cards', COUNT(*) FROM cards UNION ALL
SELECT 'Schedules', COUNT(*) FROM bus_schedules;

Expected counts: Buses (3), Routes (3), Stops (10), Cards (4), Schedules (3).

Verify ML Endpoint: Send a GET request to http://localhost:5000/health. It should return {"status": "healthy", "model_loaded": true}.
Verify Web Dashboard connection: Access http://localhost:5173. The connection status dot in the corner should show a green "Connected" indicator showing real-time socket synchronizations are live.
Verify Mobile App execution: Log in on the Expo User App. Scan coordinates, and check that live buses show up on the map.
Verify Hardware communication: Open the Arduino IDE Serial Monitor at 115200 baud. Tap an RFID card to verify that it successfully prints PROCESSING ENTRY followed by Server Response (200).

🎓 Academic Publication

This project is documented in an IEEE-format research paper:

Title: IoT-Based Smart Bus Management and Real-Time Tracking System using Cloud Computing
ML Performance: 76.7% prediction accuracy, 48% improvement over baseline
Dataset: 6 months operational data, 10 stops per route

📄 License

This project is developed for academic and research purposes.

Authors: Pavan G (@Sin-Kid)

🤝 Contributing

For new contributors:

Read GIT_SETUP.md for environment setup
Never commit .env files or credentials
Follow existing code style and structure
Test changes locally before pushing

📞 Support

For issues or questions:

Check documentation in respective folders
Review USAGE.md for feature guides
Ensure all environment variables are correctly configured

Navigation Menu

Repository files navigation

🚍 Smart Bus System

🧭 System Architecture & Flow

Core Communication Flows

🛠️ Database Schema

Performance Optimization Indexes

⚡ Algorithms & APIs

1. Database-Level Control Logic (PL/pgSQL RPCs)

2. Express Backend Middleware Algorithms (`functions/index.js`)

A. Geolocation Stop Resolution (Haversine Formula)

B. Dynamic Route-Stop Price Fare Calculation

3. Machine Learning Forecasting Algorithm (`ml_prediction`)

Features Used in Forecasting:

Training Pipeline:

🎛️ Hardware & Control Methods

📌 Pin Mapping Table

🔄 Firmware Control Loop & Logic

📂 Project Structure Tree

🚀 Execution & Startup Guide

1. Database Setup (Supabase)

2. Run the Machine Learning API Server

3. Run the Express Backend Service

4. Run the Fleet Management Admin Web Dashboard

5. Run the Passenger Mobile App (Expo)

6. Flash the Microcontroller (Arduino)

✅ Integration Verification Checklist

🎓 Academic Publication

📄 License

🤝 Contributing

📞 Support

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Name		Name	Last commit message	Last commit date
admin-web		admin-web
arduino		arduino
expo-user-app/user-app		expo-user-app/user-app
functions		functions
.gitignore		.gitignore
HARDWARE_SETUP.md		HARDWARE_SETUP.md
README.md		README.md
SETUP.md		SETUP.md
schema.sql		schema.sql
seed_data.sql		seed_data.sql

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Folders and files

Repository files navigation

🚍 Smart Bus System

🧭 System Architecture & Flow

Core Communication Flows

🛠️ Database Schema

Performance Optimization Indexes

⚡ Algorithms & APIs

1. Database-Level Control Logic (PL/pgSQL RPCs)

2. Express Backend Middleware Algorithms (functions/index.js)

A. Geolocation Stop Resolution (Haversine Formula)

B. Dynamic Route-Stop Price Fare Calculation

3. Machine Learning Forecasting Algorithm (ml_prediction)

Features Used in Forecasting:

Training Pipeline:

🎛️ Hardware & Control Methods

📌 Pin Mapping Table

🔄 Firmware Control Loop & Logic

📂 Project Structure Tree

🚀 Execution & Startup Guide

1. Database Setup (Supabase)

2. Run the Machine Learning API Server

3. Run the Express Backend Service

4. Run the Fleet Management Admin Web Dashboard

5. Run the Passenger Mobile App (Expo)

6. Flash the Microcontroller (Arduino)

✅ Integration Verification Checklist

🎓 Academic Publication

📄 License

🤝 Contributing

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2. Express Backend Middleware Algorithms (`functions/index.js`)

3. Machine Learning Forecasting Algorithm (`ml_prediction`)