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.
