Parking Space Management Research Articles

Research Paper on Smart Car Parking System

Ø With the ever-growing urbanization and the increasing number of vehicles on the road, efficient management of parking spaces has become a critical challenge in modern cities. In this project, we propose a smart car parking system based on Arduino micro controllers to address this issue. The system utilizes ultrasonic sensors to detect the presence of vehicles in parking spaces and employs RFID technology for vehicle identification. Ø The smart parking system offers several innovative features to enhance user experience and optimize parking space utilization. Users can conveniently locate available parking spaces through a mobile application or digital displays placed at strategic locations. The system also integrates with existing payment systems, allowing for seamless payment transactions and automated access control. Ø One of the key advantages of the proposed system is its scalability and flexibility. It can be easily deployed in various parking environments, including on-street parking, parking garages, and commercial parking lots. Furthermore, the use of open-source Arduino platforms ensures affordability and accessibility, making it suitable for implementation in both developed and developing regions. Ø In addition to improving parking efficiency and reducing traffic congestion, the smart parking system contributes to sustainability efforts by minimizing carbon emissions associated with vehicle circulation in search of parking spaces. It also provides valuable data insights for urban planners and policymakers to optimize parking policies and infrastructure planning. Ø Through this project, we demonstrate the feasibility and effectiveness of using Arduino-based technology to develop innovative solutions for urban parking management. The smart car parking system presented here serves as a practical demonstration of how IoT and embedded systems can contribute to creating smarter and more sustainable cities.

Enhanced Parking Space Management System With Voice Control Navigation

Enhanced Parking Space Management System With Voice Control Navigation

Smart Parking Slot Management System Using IOT

The paper proposes an innovative approach to tackle the pressing issue of efficient parking space management amidst the backdrop of rapid urbanization and escalating vehicle ownership rates. Through the amalgamation of Internet of Things (IoT) technology and ultrasonic sensors, the Smart Parking Slot Management System (SPSMS) emerges as a promising solution. Within this system, IoT-enabled devices, particularly ultrasonic sensors, are strategically deployed within parking slots to constantly monitor their occupancy status in real-time. These sensors function by detecting the presence or absence of vehicles through precise measurements of the distance between the sensor and the ground surface. Subsequently, the collected data is seamlessly transmitted wirelessly to a centralized server or cloud-based platform via internet connectivity. Upon reception of sensor data, the central server undertakes robust processing and analysis to ascertain the availability of parking slots within the designated area. End-users can conveniently access this pertinent information through a dedicated mobile application or a web interface, facilitating effortless identification and reservation of vacant parking spaces. The system further enhances user experience by incorporating automated notifications, alerting users to nearby available parking slots, alongside real-time updates on parking availability and robust reservation management functionalities. Keywords: Internet of Things, Thing Speak, Arduino Uno, ultrasonic sensor, Node MCU.

Intelligent parking space management: a binary classification approach for detecting vacant spots

Intelligent parking space management: a binary classification approach for detecting vacant spots

A Decentralized Approach to Parking Space Management with Fine-Grained Permission Level Using Blockchain Technology

A Decentralized Approach to Parking Space Management with Fine-Grained Permission Level Using Blockchain Technology

Identifying Parking Lot Occupancy with YOLOv5

With the increasing need for efficient parking space management and growing population, the application of artificial intelligence (AI) in occupancy detection has become a topic of significant interest. This paper explores the effectiveness and reliability of the YOLO (You Only Look Once) object detection algorithm in differentiating between occupied and empty parking spots. Moreover, it analyzes the impact of the number of training epochs on the overall accuracy of the AI model. The study utilizes the YOLO algorithm due to its speed and accuracy which makes the training process highly efficient. A custom dataset of 135 images was created and annotated for training purposes. The primary objective of this experiment is to demonstrate the way how AI models can successfully distinguish between occupied and empty parking spaces. By addressing the capabilities of YOLO in occupancy detection, this research aims to contribute to the growing interest in AI applications for efficient parking space management and its implications in tackling real-world challenges.

Improving Service Delay in Smart Parking System in Smart Cities with 5G

Rapid urbanization and population growth leads to substantial increase in vehicular traffic while reducing empty spaces. This necessitates an enhanced parking systems to efficiently address the parking space management. The smart city and Internet of Things (IoT) holds a very high potential to solve this problem. However, the data traffic generation at the edge is increasing rapidly. Tremendous growth in IoT is putting a high congestion on cloud services. To ease up the congestion issue, we have proposed an efficient fog-based communication model for the smart city parking management. In this work, we have proposed a parking system management model based on 5G with low latency and green communication. Our model reduces congestion at the cloud and enable faster processing by addressing the parking system locally. Furthermore, our model finds out the nearby parking without putting additional load to the cloud. Simulation result shows the performance efficiency of the proposed model in terms of response time, average cost and service delay.

Heart Disease Detection by Machine Learning System

Heart disease is a prevalent global health issue that impacts a substantial number of individuals worldwide. It is characterized by symptoms such as shortness of breath, muscle weakness, and swollen feet. However, the current diagnostic methods for heart disease have limitations in terms of accuracy and efficiency, making early detection challenging. Consequently, researchers are striving to develop an effective approach for early detection of heart disease. The lack of advanced medical equipment and qualified healthcare professionals further complicates the diagnosis and management of cardiac conditions., there have been approximately 26 million reported cases of heart disease, with an additional 3.6 million new cases identified annually. In the United States, a significant proportion of the population is affected by heart disease. Typically, doctors diagnose heart disease by considering the patient's medical history, conducting a physical examination, and assessing any concerning symptoms. However, this diagnostic method does not consistently provide accurate identification of individuals with heart disease. The importance of employing. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. “Patient 1,patient 2,patient 3 and patient 4.” Dyspnea can be described as a sensation of breathlessness and inadequate breathing, where one feels unable to take in enough air or breathe deeply. It involves the interplay of mechanoreceptors in the upper airways, lungs, and chest wall, along with peripheral receptors, chemoreceptors, and other sensory receptors. Edema refers to the accumulation of excessive fluid in the body tissues, leading to swelling. While edema can occur in any part of the body, it is more commonly observed in the lower extremities Ascites - The pathological buildup of fluid in the abdominal cavity is known as ascites. It is the most frequent cirrhosis consequence and happens in 50% of patients with decompensated cirrhosis within 10 years. Ascites formation marks the change from stressed to decompensated cirrhosis. Patent 1 is in rank 1 and patient 5 is ranked 5. In weighted table every value is equally split by 1,so that each value is equal. In the study, the researchers compared the sensitivity levels of two classifiers: the Relief FS method with a linear SVM classifier and the NB classifier with specific features from the LASSO FS algorithm. The findings revealed that the NB classifier, utilizing LASSO FS features, exhibited the highest performance in terms of sensitivity. Additionally, the Logistic Regression MCC classifier, employing the FCMIM FS method, achieved a classification accuracy of 91%.

Analysis of Smart Parking System Using IOT Environment

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability.

VEHICLE LICENSE PLATE DETECTION USING YOLO ALGORITHM

<p><em>Urban population growth has created challenges in efficient parking space management. Manual data collection is time-consuming and error-prone, especially at night. Modern technology-based solutions are urgently needed. This research focuses on an innovative parking management system using YOLO for real-time object detection, including license plates. The objective is to assess the YOLO algorithm's accuracy in license plate detection. The methodology follows software development best practices, utilizing Python and Tkinter GUI for an intuitive interface. YOLO and EasyOCR enable object detection and character recognition. Results show high accuracy: 88.8% for HD and 86.3% for sub-HD resolutions. YOLO proves reliable for license plate data collection, reducing manual intervention and enhancing parking management.</em></p>

Curbside Parking Monitoring With Roadside LiDAR

Cities worldwide are striving to find more efficient approaches to address the prevalent parking challenges in urban areas. A key aspect of achieving an optimal parking environment is the collection of curbside parking data, which enables informed decision-making and effective management of on-street parking spaces. This study proposes a solution for curbside parking monitoring and data collection using roadside LiDAR systems. By leveraging laser beam variation detection, this solution can extract essential information about parking usage. Unlike existing solutions, such as imagery or embedded sensor-based monitoring, our solution offers portability and ease of deployment for short-term or long-term curbside parking data collection. Additionally, the LiDAR sensor captures only three-dimensional data and is independent of illumination conditions, ensuring stable operation throughout the day while safeguarding privacy by not capturing imagery. These features align with the requirements of city agencies for parking data collection. The workflow follows a simple trend without the need for complex training, as typically seen in machine learning-based methods, and instead relies on parameter tuning based on real-world environmental factors. To validate the effectiveness of our method, we collected curbside parking data for five days at a midtown traffic junction with eight parking spaces. Manual validation confirmed a 95% match between identified parking events and observed data across different time periods. The study further presents parking statistics based on the identified events, revealing crucial insights about parking usage in the study area.

Development of an IoT-Based Parking Space Management System Design

Development of an IoT-Based Parking Space Management System Design

Smart Parking System using MERN Stack

Abstract: The paper presents a novel approach to the problem of parking space management in urban areas. The smart parking registration system developed using the MERN stack offers an intuitive and visually appealing interface for vehicle parking registration, similar to the seat registration system used by BookMyShow. This system aims to simplify the process of parking space booking for vehicle owners and reduce the time and effort required for parking management. The paper provides a comprehensive literature survey on state-of-the-art research papers in the field of smart parking booking, which highlights the significance of the system and its potential impact on parking space management. The methodology used in the development of the system, which includes the implementation of a seat map using React, integration with a backend API built using Node.js and Express, and data storage in MongoDB, is explained in detail. This approach enables the system to handle a large number of parking bookings and provides a scalable solution that can be easily extended to cater to the needs of different cities and parking spaces. Overall, the smart parking registration system developed in this paper offers a user-friendly and efficient solution to the problem of parking space management in urban areas. The system's intuitive interface and efficient booking process can potentially help reduce the time and effort required for parking management, enhance user experience, and ultimately contribute to reducing traffic congestion in urban areas.

VOXEL-BASED POINT CLOUD LOCALIZATION FOR SMART SPACES MANAGEMENT

Abstract. This paper proposes a voxel-based approach for creating a digital twin of an urban environment that is capable of efficiently managing smart spaces. The paper explains the registration and localization procedure of the point cloud dataset, which uses the KISS ICP for scan point cloud combination and the RANSAC method for the initial alignment of the combined point cloud. The mobile mapping point cloud using Riegl VMX-250 serves as the reference map, and Velodyne scans are used for localization purposes. The point-to-plane iterative closest-point method is then employed to refine the alignment. The paper evaluates the efficacy of the proposed method by calculating the errors between the estimated and ground truth positions. The results indicate that the voxel-based approach is capable of accurately estimating the position of the sensor platform, which are applicable for various use cases. A specific use case in the context is smart parking space management, which is described and initial visualization results are shown.

Site Selection of Car Parking with the GIS-Based Fuzzy Multi-Criteria Decision Making

Rapid motorization and uncertainty in urban growth patterns make parking space management a serious task, especially in middle-income developing countries, and this has severe social, economic, and environmental repercussions including increased congestion, crash frequency, fuel and time consumption, and air pollution. Due to the complexity of the urban transportation issue and the wide variety of variables involved, a multicriteria assessment is essential. This study used fuzzy logic and geographical information systems (GIS) to develop a multi-criteria decision making (MCDM) model for managing parking in Shiraz’s central business district (CBD). The literature was mined for information on the variables that affect parking site placement, and a poll of experts ([Formula: see text]) was used to determine their relative importance. The distance to travel attraction centers, distance to roads, land price, population density, and available land for multi-storey parking were among the factors considered. Meanwhile, the parking space shortage for each TAZ is calculated by subtracting the estimated parking space supply from the estimated parking space demand. An overlay of these two layers distinguishes locations that are in parking shortage zones and also meet multiple criteria. The results may aid policymakers in controlling parking demand by pinpointing the most promising places for investment.

Analysis of the Potential Use of Unmanned Aerial Vehicles and Image Processing Methods to Support Road and Parking Space Management in Urban Transport

Progressive urban density affects city centers especially and results in growing congestion, lack of parking spaces, and increasing environmental costs of transportation, causing increased air pollutant emissions and noise. These phenomena reduce the attractiveness of the city and result in a degradation of the quality of life for its residents. In light of these phenomena, there is a clear need for intelligent management of urban space using new technologies that would be complementary to existing intelligent transportation systems. Expanding information resources obtained from mobile cameras will have a positive impact on increasing the efficiency of transportation management and use of limited space in city centers. It will also have an impact on reducing external transport costs and increasing the quality of logistics services provided in the city. The main aim of the paper is to develop a concept of a transport management system in cities using mobile vision systems mounted on unmanned aerial vehicles. The model will concern the cases of lane occupation by freight vehicles and the analysis of parking spaces in the city in order to improve their management. The results of the developed model will contribute to the automation of the parking space management process and increase the efficiency of the use of city parking space resources.

New Parking Lot Selection Approach Based on the Multi-Criteria Decision Making (MCDM) Methods: Health Criteria

To find a parking space, valet parking drivers have to travel a lot, which leads to carbon dioxide (CO2) emissions. In order to reduce these emissions, it is essential to understand a user’s needs and criteria when searching for a parking space. Several selection criteria are considered when allocating a parking space. Recent research on parking space management mentions several parameters that have an impact on the choice of a parking space: namely, the traffic situation, the availability of each parking lot in question, and the cost of parking, etc. In this article, we discuss a new criterion: the physical condition of the driver in the management of parking spaces; the identification of the driver’s bodily fragility. We also propose MCDM as a parking space allocation model that best meets the cost–benefit convention. This reflection leads us to evaluate MCDM methods in the field of intelligent parking management. Therefore, we conducted a comparison between the most recent multi-criteria decision making methods used by researchers, namely, CODA, EDAS, TOPSIS, and WASPAS. The CRITIC method was used in this paper to objectively determine the weight of each criterion. A new approach is proposed to evaluate and select the best MCDM method. Indeed, we propose a method that computes the “average inter-item correlation SW”, a combination of the “average inter-item correlation” and the SW coefficient. This approach allows us to efficiently compute the correlation between a method and the set of methods while favoring the cells with the best ranking. A case study is presented to illustrate the MCDM approach to parking space allocation and evaluation. The proposed system provides drivers with services such as intelligent parking decisions, taking into account the human aspect while reducing energy consumption, driving time, and traffic congestion caused by searching for available parking spaces.

The use of Digital Twin in the sustainable development of the city on the example of managing parking resources

Sustainable city development is an approach to city planning and management that takes into account the balance between economic, social and environmental aspects in order to ensure sustainable social development, environmental protection and ensuring a high quality of life for residents.The aim of the article is to draw attention to the problems in the field of parking vehicles in a smart city, which should be solved as soon as possible. Problems related to parking in the city are a common challenge that can affect the comfort of living of residents, traffic, natural environment and transport efficiency. Many cities lack parking spaces in relation to the number of vehicles, leading to difficulties in finding a parking space. This can lead to traffic jams that increase the time it takes to find a parking space, increase traffic-related emissions and reduce the quality of life for residents.The research method to solve the problem related to the management of parking spaces is focused on the analysis of the city's problems and guidelines resulting from the sustainable development of the European Union. In the application part, open data resources were used to carry out the simulation in the proprietary application solution. The result of the article, which is a scientific research, is the presentation of the author's application in the Digital Twin concept for the simulation of parking space management.The simulation is the result of research conducted using open data published on the Internet.The conclusion of the research is that in the process of effective management of parking spaces, simulation in the Digital Twin concept can be used. There is great potential in this approach.

Opencv in Smart Parking Space Allocation and Management

In today’s world, the quantity of cars on the road remains steadily rising at a quickening pace. Due to the growing sum of automobiles on the road also the imperfect number of parking spaces, it can be a pain and a waste of time to look for a parking spot. This is because there are fewer parking spots than there are cars. The upshot of this is traffic congestion on the roads. According to studies that were carried out in this region, motorists typically spend 15 minutes driving about in search of a parking spot and cover a distance of 0.5 miles while travelling at a speed of 10 mph. The use of intelligent parking solutions has the potential to significantly cut down on the severity of these difficulties. Because it requires a large number of sensors to be installed at each parking lot, the traditional method is not only expensive but also time-consuming and labour-intensive. The results of this study present a smart parking classification that is based on image dispensation and may be utilised in a number of scenarios, including open parking lots, parking garages with many levels, and other similar places. In order to ascertain whether or not a parking spot in the gathered video is occupied, the proposed design for the system utilises a combination of edge detection and coordinate bound pixel sections. In addition to that, it functions as an illustration of the process of transforming text into images. Tesseract is utilised whenever an image is analysed in order to extract the text contained within it. It is possible to modify the strength of the image processing so that each photograph experiences precisely the amount of processing that is necessary to provide the highest quality text results.

Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning

<p>Aiming at the parking space heterogeneity problem in shared parking space management, a multi-objective optimization model for parking space allocation is constructed with the optimization objectives of reducing the average walking distance of users and improving the utilization rate of parking spaces, a real-time allocation method for shared parking spaces based on deep reinforcement learning is proposed, which includes a state space for heterogeneous regions, an action space based on policy selection and a reward function with variable coefficients. To accurately evaluate the model performance, dynamic programming is used to derive the theoretical optimal values. Simulation results show that the improved algorithm not only improves the training success rate, but also increases the Agent performance by at least 12.63% and maintains the advantage for different sizes of parking demand, reducing the user walking distance by 53.58% and improving the parking utilization by 6.67% on average, and keeping the response time less than 0.2 seconds.</p> <p> </p>