Traffic Light
Sensor with
Arduino Uno
Overview of Traffic Light
System
Signal Detection
The sensor identifies the presence of vehicles at the
intersection.
Signal Processing
The Arduino Uno processes sensor data to determine
traffic flow.
Light Control
The system controls LED traffic lights based on
processed data.
Hardware Components
Arduino Uno Microcontroller board for
processing sensor data
Piezo Sensor Sound-based vehicle detection
RGB LED Tri-color lights for stoplight
simulation
Breadboard Connection platform for
components
Wiring the LEDs
Connect LED to Digital Pin Resistors for Current LimitingPower Source
Attach the anode of each RGB LED Insert a 220-ohm resistor between Connect the cathode of each LED
to a separate digital output pin on each LED pin and the to the ground in the circuit.
Arduino Uno. microcontroller to prevent
damage.
Programming the LEDs
1 Define Pin Numbers
2 Set Pin Modes
3 Create Light Patterns
4 Loop for Continuous Operation
Adding Sensor Functionality
Connect Piezo Sensor Power the Sensor Calibrate for Accurate
Detection
Attach the piezo sensor's output pin Connect the positive and ground
to an analog input on Arduino Uno. connections from the piezo sensor to Adjust the threshold value in the code
5V and GND. for optimal vehicle detection.
Responding to Sensor Inputs
1 Detect Sound
Piezo sensor converts sound waves into
electrical signals.
2 Analyze Signal
Arduino Uno processes sensor data to detect
vehicle presence.
3 Adjust Timing
System increases green light duration based
on detected traffic flow.
Optimizing the Traffic Light System
Traffic Flow Analysis Real-Time Adjustments
Record traffic patterns over Implement a dynamic
time to refine system algorithm for real-time
response. traffic management.
Potential Enhancements
and Future Developments
Smart City Integration Multi-sensor
Approach
Link with smart city
infrastructure for optimized Incorporate multiple sensor
traffic management. types for improved accuracy
and reliability.
AI-Powered Decision Making
Utilize machine learning algorithms for predictive traffic
management.
