DYNAMIC PARKING SPACE BOOKING SYSTEM USING AI-DRIVEN SLOT MANAGEMENT

Abstract

This research presents a dynamic parking space booking system that integrates a deep learning model to enhance parking space utilization and reduce traffic congestion in busy cities caused by improperly parked vehicles. The system utilizes advanced computer vision techniques for the real-time detection of vehicles parked in the space and employs deep learning .algorithms to analyze vehicle types. The system automatically determines the most suitable parking space for each vehicle. Security cameras installed in the parking, area continuously monitor and classify the occupancy status of each spot, providing accurate, time-to-time information. Real-time footage from cameras ensures the system works effectively under varying lighting . conditions, across different vehicle types, and in diverse parking layouts. Users can .get benefitted via a mobile application, allowing them to easily locate, reserve, and pay for parking. Additionally, the deep learning model adapts to user, behavior and parking trends, further optimizing efficiency and usage over time.

Specification

FIELD OF THE INVENTION
The field of invention involves the application of Artificial Intelligence (Al), Deep Learning, and Computer Vision technologies within the domain of urban mobility and parking management. This invention focuses on developing a real-time parking system that uses camera vision-based vehicle detection and Automatic Number Plate Recognition (ANPR) to streamline parking operations. By utilizing Deep Learning models, the system can accurately detect vehicles and available spaces without relying on traditional sensors, enhancing the efficiency of parking space utilization. This solution reduces traffic congestion and minimizes carbon omissions, while providing a seamless parking experience for users. The emphasis is on creating a sustainable parking system that optimizes parking availability and contributes to smarter, more efficient urban infrastructure.
BACKGROUND AND PRIOR ART OF THE INVENTION
In urban areas, the rapid increase in vehicle numbers has led to significant challenges in parking management, including traffic congestion, improper street parking, and elevated carbon emissions from vehicles searching for available parking spots. One of the key reasons for urban traffic congestion is that vehicles searching for parking, contribute significantly to the problem, which exacerbates fuel consumption and greenhouse gas emissions, further contributing to climate change. Traditional parking systems, relying on manual or sensor-based methods, are inefficient in managing parking demand and supply, often leading to overcrowding and mismanagement of parking spaces.
Several existing methods have attempted to address these issues. Traditional sensor-based parking systems, which rely on ground or overhead sensors to detect vehicle presence, have limitations in scalability and maintenance due to their high cost and susceptibility to malfunction in harsh environments. While smart parking systems with sensors are becoming more common, they still present operational challenges, particularly in terms of cost, maintenance, and data accuracy.
In addition, parking systems often fail to integrate automated entry/exit mechanisms, requiring human intervention and resulting in inefficiencies. The time wasted in searching for parking spots can be compounded by the fact that even when drivers find a location, the spots may be unavailable, leading to further waiting in line to park their vehicles.
The existing methods lack the integration of.advanced technologies, which can greatly improve accuracy and real-time data processing. Recognizing these limitations, the proposed invention introduces an Al-driven parking system that employs vision-based detection and Automatic Number Plate Recognition (ANPR) to efficiently manage parking spaces without relying on sensors. This system not only reduces traffic congestion and parking time but also minimizes carbon emissions by providing real-time availability data to drivers via a mobile app. The integration of Deep Learning model ensures high accuracy, scalability, and seamless user experience, overcoming the limitations of traditional parking management systems.
SUMMARY OF THE INVENTION
This dynamic parking space booking system addresses urban parking challenges by leveraging deep learning, computer vision, and edge computing. The system detects available parking spaces in real-time through CCTV footage, allowing users to check free slot availability online. The captured data is stored in a cloud database for managing bookings, while a mobile application provides a seamless interface for reservations and payments. The Parking Lot Control System manages entry and exit scanners for smooth transitions. By optimizing parking space utilization, the system enhances user convenience, supports smarter city infrastructure, reduces traffic congestion, and conliibutes to environmental sustainability through efficient vehicle movement. Additionally, the application offers directions from the user's location to the parking space, representing a significant advancement in intelligent transportation solutions. The system adapts over time, utilizing a diverse dataset to ensure robust performance under various conditions and continuously learns from user behavior and parking patterns to improve overall efficiency.
OBJECTIVE OF THE INVENTION.
The objectives of this project are multi-dimensional, addressing environmental, technological, and societal challenges.
The primary objective is to develop an innovative, Al-driven system to significantly reduce the time users spend searching for parking spaces.
The second objective is to enhance fuel efficiency by minimizing unnecessary travel in search of parking, thereby reducing carbon emissions and contributing to lower air pollution levels.
The third objective is to minimize urban traffic congestion caused by improper parking and prolonged searches for parking spots, helping to smooth traffic flow in high-demand areas.
The fourth objective is the integration of security cameras and automated entry/exit systems, powered by Al, strengthens vehicle safety. The system's ability to detect unusual behavior adds an extra layer of security, reducing the risk of theft or damage.
The fifth objective is by Using Al-based vehicle detection and Automatic Number Plate Recognition (ANPR) technology, the system will enhance security and automate entry/exit management,, reducing human, errors and manual intervention while ensuring the safety of vehicles.
The sixth objective is the Al-driven, computer vision-based management system ensures real-time monitoring and dynamic optimization of parking spaces, preventing underutilization and maximizing occupancy.
The seventh objective is designed to enhance the overall user-experience by providing a mobile app that allows users to easily find, reserve, and pay for parking, receiving real-time notifications and updates for a stress-free parking experience.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig.1 : illustrates the block diagram of Al-Driven Parking Management System
Fig.2 : illustrates the booking process of the whole system.
Fig.3 : illustrates the architecture of the Al-Driven Parking Management System.
Fig.4 : illustrates the real-time detection of vehicles in a parking lot using vehicle occupancy detection model.
Fig.5: illustrates the working of Automatic Number Plate Recognition (ANPR).
Fig.6: Raspberry Pi 5, acts an edge device for processing.
Fig.7: Security camera, one of the components in the system.
Fig.8: illustrates the user interface of the mobile application through which the user books their parking spots
DETAILED DESCRIPTION OF THE INVENTION:
The Dynamic Parking Space Booking System using Al-driven slot management addresses the critical issue of parking shortages in India's rapidly urbanizing regions, where vehicle ownership has outpaced the availability of parking spaces. This has led to traffic congestion, improper street parking, and inefficient management of parking*demand. The system leverages real-time data and advanced Al technology to optimize parking space utilization, reduce congestion, and minimize the environmental impact caused by prolonged vehicle searches, offering a smart solution to streamline urban mobility and support better business and urban planning outcomes.
Fig. 1: Block Diagram of Al-Driven Parking Management System
Security Lot Footage and Entry/Exit Security Footage: These represent the camera feeds from both the parking lot and entry/exit points. These video streams are the primary inputs to the system, capturing real-time footage of parking spaces and vehicle movements.
Occupancy Al Model: This component processes the security footage to detect the presence of vehicles in the parking spots. Using Al algorithms, particularly computer vision techniques, it determines whether a parking space is occupied or available.
ANPR (Automatic Number Plate Recognition) Validation: This system analyzes the entry/exit footage, specifically focusing on vehicle license plates. It validates the license plate against pre-existing bookings to ensure that only vehicles with valid reservations are granted access.
Raspberry Pi 5 (Al Processing Unit): This acts as the central processing unit, handling the Al computations for both the occupancy detection model and ANPR validation. The Raspberry Pi receives input from the Al models and communicates with other system components.
Database Server: The database server stores.parking-related data, including security footage, reservation information, and real-time occupancy statuses. It facilitates communication between the Al processing unit and the web application (Park Sync web App), ensuring that users receive up-to-date information on parking availability.
Park Sync Web Application: This web application interfaces with the users, allowing them to book parking spots, check real-time availability, and receive notifications. The app is continuously updated by the database server based on the Al models' outputs and security footage analysis.
Power Supply to RPI (Raspberry Pi 5): This block represents the power source for the Raspberry Pi, ensuring that the processing unit remains operational to manage the system's Al-driven functionalities.
Fig.2: Booking Process of the Parking Management System
The system allows users to conveniently book parking spots through the mobile application, which suggests the nearest available spot based on the user's current location. To reserve a spot, users provide details such as vehicle number and desired time period, followed by the payment process. The application is continuously updated to reflect real-time parking ' availability, ensuring users can access accurate information at all times.
Upon arrival, the automated entry system checks the vehicle's number plate. If it matches an active booking for the specified time, the tollgate grants access to the parking lot, directing the vehicle to its designated spot. As the booked time approaches expiration, the system sends notifications to the user. If the vehicle exceeds the reserved time, additional charges apply. Once the extra fees are paid, the automated exit system allows the vehicle to leave the parking lot seamlessly.
Fig.3: Architecture of the Parking Management System
The architecture, integrates security cameras, computer vision technology, and an automated entry/exit system to provide a seamless parking experience. High-resolution security cameras continuously monitor parking spaces, capturing real-time footage that is processed, using computer vision techniques, such as the Point Polygon Test Method, to detect the presence, of vehicles. If a vehicle is detected in a designated spot, the space is marked as occupied; otherwise, it is marked as available, with real-time updates provided through the web application.
At the entry and exit points, the system employs Automatic Number Plate Recognition (ANPR) to verify vehicles1 license plates against valid bookings. When a match is confirmed, the tollgate grants access. Users are notified when their booking period is about to expire, and if the vehicle remains beyond the allotted time, extra charges are automatically applied. Payments are processed through the system, allowing for a smooth exit once all dues are cleared.
We claim,
1. Efficient Parking Management
The system provides real-time monitoring of parking spaces, updating availability status instantly using security footage processed with computer vision techniques. It detects whether a parking spot is occupied or vacant and communicates this information to a central database, ensuring the mobile app always provides accurate data. This optimizes space utilization, reducing unnoticed empty spots and facilitating a smooth flow of vehicles in and out of parking areas, leading to overall efficient parking management:
2. Reduced Traffic Congestion
One of the primary causes of urban traffic congestion is drivers circling to find parking. By providing real-time parking availability through the mobile app, drivers can navigate directly to available spots, reducing the number of vehicles searching for parking and thereby alleviating congestion, particularly in high-traffic urban areas like shopping centers and entertainment venues.
3. Lower Carbon Emissions
Reducing the time spent searching for parking leads to fewer vehicles on the road and decreased fuel consumption. This, in turn, lowers carbon emissions, contributing to cleaner air in cities and aligning with environmental sustainability goals. Efficient parking management also aids cities in meeting regulatory standards for reducing vehicle emissions.
4. Seamless User Experience
Users , can enjoy a hassle-free parking experience by reserving spots in advance through the app. They enter details like vehicle number, parking duration, and payment to secure a spot before arriving. Real-time updates ensure accurate space availability, and notifications alert users when their booking is about to expire, offering options to extend the reservation and pay any additional charges if necessary. .
5. Advanced Security Measures
The system integrates security cameras and Al-driven technologies for enhanced parking lot security. Cameras monitor parking spaces continuously, and Al-based models detect vehicle presence using computer vision techniques, such as the Point Polygon Test Method. The automated entry/exit system, equipped with Automatic • Number Plate Recognition (ANPR), verifies vehicle license plates, allowing only authorized vehicles to enter, preventing unauthorized access.
6. Scalable Infrastructure
The system is designed to grow and adapt to changing parking demands. It can expand parking capacity by utilizing underutilized land, creating pop-up parking lots during high-demand periods. This approach provides flexibility to scale parking operations for cities experiencing growth or seasonal fluctuations. Additionally, the platform can integrate with various parking facilities, such as malls, office buildings, and public spaces, providing a unified solution across multiple locations.
7. Real-Time Alerts and Notifications
The system provides real-time alerts and notifications to users throughout the parking process, including reservation status updates, reminders for upcoming expiration times, and notifications of additional fees if a vehicle remains parked beyond the reserved time. These real-time communications enhance user convenience by providing timely information and preventing unexpected charges.
8. Data-Driven Decision Making
The system collects valuable data on parking usage, including peak times; stay durations, popular locations, and revenue generated from each spot. By analyzing this data, parking managers can identify trends and optimize space allocation. Dynamic pricing strategies can be implemented, offering higher prices during peak hours and discounts during off-peak times, enhancing revenue and parking efficiency.
9. Enhanced Accessibility for Events
• Event organizers often face parking challenges due to large crowds. By integrating the Al-driven parking system with event planning, users can prebook parking spots, reducing last-minute issues and providing a more organized experience. The system dynamically manages parking availability by directing vehicles to different lots based on real-time conditions, ensuring smooth traffic flow before and after events.

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