Enabling smart parking for smart cities using Internet of Things (IoT) and machine learning.

Area of Science:

• Computer Science
• Urban Planning
• Artificial Intelligence

Background:

• Urban parking scarcity causes significant fuel waste and time inefficiencies.
• Employees frequently experience delays due to difficulties finding parking at work.
• Manual verification of vehicles by security personnel is time-consuming.

Purpose of the Study:

• To develop an intelligent parking system for smart cities.
• To automate vehicle entry and optimize parking space utilization.
• To reduce the time and effort required for drivers to find parking.

Main Methods:

• Implementing Automatic Number Plate Recognition (ANPR) with Optical Character Recognition (OCR) for vehicle identification.
• Utilizing cameras within parking areas to detect vacant and occupied spaces.
• Developing a centralized database for real-time parking availability data.

Main Results:

• Streamlined vehicle entry through automated license plate recognition and database cross-referencing.
• Real-time display of available parking spots on digital screens at entrance gates.
• Significant reduction in time and effort for drivers to locate parking spaces.

Conclusions:

• The proposed system effectively addresses urban parking challenges.
• The integration of ANPR, OCR, and machine learning enhances parking management.
• This solution contributes to improved urban mobility and the development of smart cities.