Design and Implementation of an IoT-Based Intelligent Parking System

Abstract

This research outlines the development and execution of an intelligent parking system using IoT technology. The system employs sensors and data analysis methods to deliver up-to-the-minute information on available parking spaces. The plan was simulated using Python code, which generated random sensor data for cars and bikes and analysed that data to determine the parking availability for each vehicle type. The final parking counts were then used to create a bar chart that visualised the parking availability for the entire day. The simulation results demonstrated the potential benefits of an intelligent parking system, including reduced search time for parking spots and traffic congestion. The plan effectively provided real-time information on parking availability, with an accuracy rate of over 90%. The system can examine and comprehend parking trends and utilisation using sophisticated data analysis methods, including machine learning and artificial intelligence. However, the simulation also identified potential weaknesses, including technical issues, privacy and security concerns, and stakeholder engagement. Technical problems may arise in implementing a real-world parking sensor system, such as maintenance and repair of the sensors. Privacy and security concerns may also occur as the system collects data on the location and movements of vehicles. In addition, stakeholder engagement may be necessary to ensure the system is designed and implemented to meet all users’ needs.