AI Smart Public Transport Crowd Monitoring System

This is an excellent AI + IoT + Agentic Automation final-year engineering project that combines ESP32, AI, Computer Vision, n8n, Telegram, Google Sheets, and ThingSpeak into a smart public transport monitoring platform. Because your request includes an entire engineering project (documentation, firmware, workflow JSON, cloud integration, circuit design, AI logic, deployment, etc.), it is far too large to fit into a single ChatGPT response. A complete version would be several hundred pages long with thousands of lines of code. A practical way to build it is as a structured project manual. Below is Part 1, covering the complete concept, architecture, hardware, workflow, and implementation plan. The remaining sections (firmware, source code, n8n JSON, web application, documentation, etc.) can be developed chapter by chapter. AI Smart Public Transport Crowd Monitoring System Complete Project Title AI Smart Public Transport Crowd Monitoring System using ESP32, AI Vision, Agentic IoT, n8n Automation, Telegram Voice Alerts, Google Sheets and ThingSpeak Cloud Dashboard Chapter 1 Introduction Urban public transportation systems frequently experience overcrowding, especially during peak hours. Excessive passenger density leads to: Passenger discomfort Safety hazards Delayed boarding Increased waiting time Poor transport planning Lack of real-time occupancy information The proposed system uses AI-powered crowd estimation with ESP32, camera-based counting, cloud analytics, and automation workflows to monitor bus or train occupancy in real time. Passengers, transport authorities, and administrators receive live occupancy updates through web dashboards and Telegram notifications, while historical data is stored in Google Sheets and ThingSpeak for analysis. Objectives The project aims to: Monitor passenger count in real time Detect overcrowding automatically Display occupancy percentage Predict crowd levels using AI Generate Telegram alerts Store historical records Visualize trends in ThingSpeak Provide web dashboard access Automate workflows using n8n Improve transport management Applications Smart buses Metro trains Railway coaches College buses Airport shuttle services Public transport authorities Smart city infrastructure School transportation Industrial employee buses Tourism transportation Advantages Real-time monitoring AI-based crowd prediction Low-cost implementation Cloud connectivity Remote monitoring Voice alerts Automatic reporting Expandable architecture Easy deployment Overall Architecture Passengers │ ESP32 + IR Sensors + Load Sensor + ESP32-CAM │ WiFi │ Cloud API │ PHP Server │ MySQL Database │ n8n Automation ├──────────────┐ │ │ │ │ Telegram Google Sheets │ ThingSpeak Dashboard AI Prediction Engine Administrator Dashboard Chapter 2 System Modules Module 1 Passenger Detection Uses: IR Beam Sensors ESP32-CAM AI Vision Model Purpose: Count passengers entering and exiting. Module 2 Crowd Calculation Current Occupancy = Passengers Entered − Passengers Exited Occupancy % (Current Occupancy ÷ Maximum Capacity) ×100 Module 3 AI Prediction Predict: Peak hours Future occupancy Overcrowding Traffic congestion Module 4 Cloud Storage Stores: Timestamp Vehicle ID Passenger Count Occupancy Prediction Alert Status Temperature GPS Module 5 Telegram Alert Example 🚌 Smart Bus Alert Bus Number : TS09AB1234 Current Occupancy : 94% Passengers : 47 Status : ⚠ Crowd Level HIGH Location: Bus Stop 12 Time: 08:45 AM Module 6 Voice Notification Example Attention Bus Number TS09AB1234 has reached 95 percent occupancy. Please dispatch an additional vehicle. Chapter 3 Hardware Components Component Quantity ESP32 DevKit V1 1 ESP32-CAM (optional) 1 IR Sensors 2 Ultrasonic Sensor HC-SR04 1 Load Cell + HX711 1 OLED Display 1 GPS Module NEO-6M 1 Buzzer 1 LEDs 3 Push Button 2 Relay Module 1 Breadboard 1 Jumper Wires Many 5V Adapter 1 Sensor Functions IR Sensor Counts passengers Ultrasonic Sensor Measures doorway occupancy Load Cell Estimates crowd weight GPS Bus location ESP32-CAM AI object detection OLED Shows Passengers Occupancy WiFi Status Alert Level Chapter 4 Pin Configuration ESP32 Pin Device GPIO4 IR Entry GPIO5 IR Exit GPIO18 HX711 DT GPIO19 HX711 SCK GPIO21 OLED SDA GPIO22 OLED SCL GPIO16 GPS RX GPIO17 GPS TX GPIO25 Buzzer GPIO26 Relay GPIO27 Status LED Circuit Description IR Entry ↓ ESP32 GPIO4 IR Exit ↓ GPIO5 HX711 ↓ GPIO18 GPIO19 OLED ↓ I2C GPS ↓ UART ESP32 ↓ WiFi ↓ Cloud Server Chapter 5 Working Principle Step 1 ESP32 boots. ↓ Step 2 Connects WiFi. ↓ Step 3 Reads sensors. ↓ Step 4 Counts passengers. ↓ Step 5 Calculates occupancy. ↓ Step 6 Uploads data. ↓ Step 7 ThingSpeak updates. ↓ Step 8 n8n detects threshold. ↓ Step 9 Telegram alert. ↓ Step 10 Google Sheet updated. ↓ Step 11 AI predicts next occupancy. Flowchart Start ↓ Initialize ESP32 ↓ Connect WiFi ↓ Read Sensors ↓ Count Entry ↓ Count Exit ↓ Calculate Occupancy ↓ Overcrowded? ↓ No ---------> Upload Cloud ↓ Yes ↓ Telegram Alert ↓ Voice Alert ↓ Google Sheet ↓ ThingSpeak ↓ AI Prediction ↓ Dashboard Update ↓ Repeat AI Crowd Prediction Logic The AI module forecasts crowd levels for the next 15–60 minutes using historical and live transport data. Inputs: Current passenger count Vehicle capacity Time of day Day of week GPS location Historical occupancy Route number Special event indicator (optional) Features: Time-series forecasting (e.g., LSTM or lightweight regression) Crowd level classification (Low/Medium/High/Critical) Peak-hour prediction Recommended dispatch actions Example Logic: IF Occupancy < 40% Crowd = LOW ELSE IF Occupancy < 70% Crowd = MEDIUM ELSE IF Occupancy < 90% Crowd = HIGH ELSE Crowd = CRITICAL The prediction can run in Python on a server or via an AI API, with results sent back to the ESP32 dashboard and automation workflow. n8n Automation Workflow Overview Receive HTTP webhook from ESP32 or PHP server. Parse JSON payload. Check occupancy threshold. Write data to Google Sheets. Send Telegram text notification. Generate Telegram voice alert using a Text-to-Speech service. Update ThingSpeak if required. Log events to a database. Trigger AI prediction service. Send follow-up alerts if occupancy remains critical. Cloud Dashboard The web dashboard should display: Live passenger count Occupancy percentage Bus/train location Current crowd level Historical occupancy charts AI prediction graph Telegram alert history Device online/offline status ThingSpeak charts Google Sheets export Future Enhancements Multi-camera AI counting Face anonymization for privacy Automatic bus dispatch recommendation Integration with traffic signal systems Mobile passenger app with live occupancy QR-code ticket analytics Edge AI using ESP32-S3 with TensorFlow Lite Solar-powered operation 5G/NB-IoT connectivity Digital twin visualization Predictive maintenance for transport fleets Integration with smart city command centers Complete Project Deliverables (Recommended Structure) To build this as a professional final-year project, the documentation can be organized into: Project Abstract Introduction Literature Survey Problem Statement Objectives System Architecture Hardware Components Circuit Schematic Flowchart ESP32 Firmware (Arduino IDE) PHP + MySQL Web Application HTML/CSS/JavaScript Dashboard MySQL Database Schema n8n Workflow (JSON) Telegram Bot Integration Google Sheets Integration ThingSpeak Configuration AI Crowd Prediction Module Voice Notification Automation Testing and Results Deployment Guide Cost Estimation Advantages and Limitations Future Scope IEEE-Style Research Paper Viva Questions and Answers User Manual Maintenance Guide References Appendices This structure is suitable for expanding into a 200–250 page project report with complete source code, diagrams, workflows, and implementation details.