Smart Parking Research Articles

TRISDEŠIMTOJI NACIONALINĖ MOKSLINĖ-PRAKTINĖ KONFERENCIJA „GAMTAMOKSLINIS UGDYMAS BENDROJO UGDYMO MOKYKLOJE – 2024“: KONFERENCIJOS APŽVALGA

2024 is a truly special year. This is the year of the Jubilee Scientific and Practical Conference "Natural Science Education in a Comprehensive School - 2024". And this was the thirtieth conference. Throughout this time, science, engineering and technology have developed at a significant and rapid pace. The theory and practice of science education has undoubtedly evolved, including the strategies and practices of science education. The importance of science and technology education is unquestioned, but its orientation towards competences, with an emphasis on learning through science and the linking/integration of science with other subjects, remains. The 30th Conference continued an already established practice. The conference took place over two days. The two-day conference focused on science and technology education, which has recently become a priority in society and is likely to remain so in the future. The 30th Conference started on 25 April. On the eve of the Conference, an informal round table discussion on "Education issues" was organised. The importance of the cultural programme should also be mentioned. On the first day, at the end of the conference, participants had the opportunity to get to know Plunge better. The most important places of the city were visited. The staff of the Plunge Tourism Information Centre organised a sightseeing tour of Plunge. The city centre, the central square and the church were visited. Participants had a unique opportunity to walk around the Plunge Smart Park and to visit the Plunge Manor House, or Mykolas Oginskis Palace, one of the most beautiful monuments of 19th-century Lithuanian architecture. The plenary session of the conference provided a retrospective of all the conferences. Thirty years of really great organisational, managerial, publishing and other work. A total of 30 conference proceedings were published. It is estimated that a total of 677 papers were published, with a total page count of 4787. If all the pages were put together, it would be a single publication 281 mm thick. This is a really big work, a really good repository of scientific and didactic-methodological research and work. The work of this 30th conference has been recorded and is freely available on our YouTube channel (https://www.youtube.com/playlist?list=PLoZNO1c3zi72-RxbETp4AmMQ8Lz2C-QPC). Most conference reports were presented in the form of articles and published in the conference proceedings. One can find full texts in the database at: https://oaji.net/journal-archive-stats.html?number=1984&year=2023&issue=20002 Keywords: national conference, practical work, science education, science teachers, science pedagogy

U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services

U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services

IoT Integrated Smart Parking Zone and Website with Stand-alone System and Wireless Technology

The research project works to digitalize the full parking management system, transform the manual parking methods, solve traffic and road congestion problem and show live updates of the designated parking slots. It is ecofriendly and eliminates human interaction. It also offers personal and corporate business earnings. In the project, anyone can book time slots for parking following their convenient schedule, pay bills via the online payment system, control the entrance while entering and exit gate in the parking area with the vehicle using the website system. Users do not have to use any extra identity instrument, like- RFID Cards, QR Codes etc. The system will verify them by online credentials. Moreover, people can see the live slot updates for better security. The project collaborated with 3 major technology parts which are practical hardware, software and database system. NodeMCU, Ultrasonic Sensors, Servo motors, and other technical components are used in the hardware part. PHP, HTML, JavaScript, CSS etc. codes are implemented for the website development. Firebase Realtime Database and MySQL are integrated for the cloud and local database management with the hardware and software parts for building the IoT technology environment and infrastructure.

Smart Car Parking System based on UWB Tracking

Smart Car Parking System based on UWB Tracking

An intelligent parking allocation framework for digital society 5.0

In recent smart city innovations, parking lot location has garnered a lot of focus. The issue of where to put cars has been the subject of a lot of literature. However, these efforts rely heavily on algorithms built on centralized servers using historical data as their basis. In this study, we propose a smart parking allocation system by fusing k-NN, decision trees, and random forests with the boosting techniques Adaboost and Catboost. Implementing the recommended intelligent parking distribution technique in Smart Society 5.0 offers promise as a practical means of handling parking in contemporary urban settings. Users will be given parking spots in accordance with their preferences and present locations as recorded in a centralized database using the proposed system’s hybrid algorithms. The evaluation of performance considers the effectiveness of both the ML classifier and the boosting technique, and it finds that the combination of Random Forest and Adaboost achieves 98% accuracy. Users and operators alike can benefit from the suggested method’s optimised parking allocation and pricing structure, which in turn provides more convenient and efficient parking options.

Mengintegrasikan Teknologi IoT dan Smart Destinations dalam Pengelolaan Pariwisata Berkelanjutan

The integration of Internet of Things (IoT) technology with the concept of Smart Destinations offers an innovative solution in tourism management with the aim of improving operational efficiency and tourist experience. IoT technology, which includes interconnected sensor devices and communication systems, provides the ability to unify and manage various aspects of a destination in real-time. Meanwhile, Smart Destinations use digital technology to optimize the tourist experience through personalization, resource efficiency, and desire. This article discusses the background and key benefits of IoT integration in Smart Destinations, including improving the visitor experience through data and analytics, better crisis management, and more efficient resource management. Application examples such as smart parking systems, automated energy management, and providing contextual information to tourists through sensors are described to illustrate the practical application of this technology. However, challenges such as data privacy and security, technology compatibility issues, and significant investment requirements are also discussed. In conclusion, while this integration faces several obstacles, its potential benefits in improving the quality and efficiency of tourist destinations make it a promising approach for the future of the tourism industry.

Application of the analytical hierarchy process model in developing a decision support model for the selection of smart parking investors in the form of a public-private partnership in Da Nang city

This study develops a decision support model for the selection of smart parking investors in the form of public-private partnerships in Da Nang City based on an analysis hierarchy process model. The proposed model for selecting smart parking investors consists of three main criteria and 15 secondary criteria. The main criteria include ability-experience, technology, and finance-commerce. The proposed model was used to arrange the priority orders of four investors. The priority orders of four smart parking investors were also determined using Expert Choice software. The results from the Expert Choice software were consistent with those obtained by the proposed model. The proposed model and calculated results of the priority orders of smart parking investors are a scientific and reliable basis for owners to choose the best investor for smart parking in the form of public-private partnerships (PPP).

"Smart Parking Systems: Enhancing Efficiency and Sustainability in Urban Areas"

Abstract: Smart Parking systems are innovative solutions designed to address the challenges faced by drivers when searching for parking spaces, especially in busy urban areas during peak hours. These systems utilize IoT technology deployed on-site to monitor and indicate the availability status of individual parking spaces. Additionally, a mobile application is provided to enable users to conveniently check the availability of parking spaces and easily locate vacant slots. By implementing a Smart Parking system, drivers can save valuable time and avoid the frustrations associated with the daunting task of finding suitable parking spots. Moreover, these systems play a crucial role in alleviating traffic congestion and reducing air pollution in densely populated urban areas.

Smart Car Parking System

In this fast-growing economy, the number of vehicle users is growing tremendously, wasting a significant amount of time and fuel while searching for an available parking space. This work highlights the optimum utilization of parking slots without the physical intervention of humans by making use of an IoT-integrated embedded system. This study aimed to create a mobile application for car parking that uses NodeMCU as the Microcontroller, which is further integrated with Radio-Frequency Identification (RFID) and the Internet of Things (IoT) to detect available parking slots, saving people’s time. When combined with an interactive mobile application, this technology provides an efficient solution by highlighting the availability and occupancy of parking spaces as green and red, respectively. It contributes to enhancing the quality of life of people living in congested metropolitan cities by digitizing the conventional parking system.

A WSN and vision based smart, energy efficient, scalable, and reliable parking surveillance system with optical verification at edge for resource constrained IoT devices

As urbanization accelerates, the demand for efficient parking surveillance solutions has increased. However, existing solutions often face challenges related to energy consumption, scalability, and reliability. This paper introduces a smart hybrid parking surveillance system integrating wireless sensor networks (WSNs) with vision based solution at the edge for resource constrained IoT devices to address these challenges. The solution leverages WSNs for periodic readings of parking space occupancy and introduces a low power sleep mode in the network for energy efficiency, along with optical verification strategies using computer vision models like R-CNN and Faster R-CNN FPN on ResNet50 and MobileNetv2 backbones for distinguishing between true and false positives in the WSN data for a greater accuracy in parking space occupancy. The system utilizes edge for computing on edge servers resulting in increased responsiveness of the system, reduced data transmission and real time processing of data. The proposed solution is formulated in such a way that it automatically switches between WSN and vision based sensing resulting in less energy consumption and longer lifespan of the system without compromising on accuracy. Through experimental results it is observed that models trained on the MobileNetv2 backbone demonstrated at least twice faster for both processing the images and training compared to those models trained on the ResNet backbone. On the other hand, both Faster R-CNN FPN (input resolution: 1440) and R-CNN (input resolution: 128) models trained on the MobileNetv2 backbone have slightly lower accuracies than the same models trained on the ResNet50 backbone.

Towards Future Mobility: Smart Parking System Based on Internet of Things with Wearable Computing Technology on Campus

Technological advances in the digital era opened the door to innovation in various fields, including urban mobility. This article described the concept and implementation of an Internet of Things (IoT)-based smart parking system integrated with wearable computing technology in a campus environment. The main objective of this research was to develop a smart parking system integrated with wearable computing technology. The methodology used involved designing an integrated parking system, mapping parking areas, and developing a mobile application. Facilitating users' capacity to locate available parking spaces and providing them with real-time information regarding such availability were the primary goals of the proposed system, providing notifications to users when their parking time was running out and facilitating parking payments directly via their smartphone or smartwatch. Integration between smart parking applications and smartphones or smartwatches allowed users to access parking services more quickly and efficiently, improving their overall parking experience. This article described the system architecture and algorithms used, as well as testing and evaluating system performance. The unique aspect of this Smart Parking System research was the integration of wearable computing technology and the Internet of Things (IoT) concept to create a smart parking solution that was comfortable and efficient in the campus environment. The limitations of the discussion in this research were limited to Internet of Things (IoT) technology, sensors, and mobile applications. Through the implementation of this system, it was hoped that it could positively contribute to increasing mobility and efficient use of parking spaces in the campus environment, as well as opening up opportunities for developing similar applications on a wider scale in the future.

Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car

Energy management of slow and fast charge electric vehicles (EV) in smart parking lot using NBO-THDCNN algorithm

Energy management of slow and fast charge electric vehicles (EV) in smart parking lot using NBO-THDCNN algorithm

Path Planning Algorithms for Smart Parking: Review and Prospects

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.

Smart Cities towards Artificial Intelligence

The creation of smart cities has come a long way in recent years, especially in domains that use AI, such IoT-based components, security systems, smart living options, and environmental monitoring. These advancements seek to build surroundings that meet human requirements by utilizing cutting edge technologies such as automated parking, smart healthcare, and seismic monitoring systems. Even with these encouraging advancements, the current generation of smart cities is still unable to attain genuine autonomy, particularly when it comes to component-level autonomous situation management and decision-making. The transformational perspective proposed in this work views the constituent components of smart environments as self-governing agents with the ability to make decisions. The purpose of these autonomous agents is to improve security and smart living by being able to self-manage and react automatically to different situations. Such autonomous agents can be integrated to accomplish the goal of artificially intelligent smart cities. In smart cities, autonomous agents may behave pro-actively without human involvement by continually monitoring and analyzing data, anticipating possible problems, and taking proactive actions. In the context of smart healthcare, these agents may, for example, track a patient's vitals in real time, anticipate possible health emergencies, notify medical personnel, or even start emergency procedures. Autonomous agents may dynamically assign parking spots in the context of smart car parking based on real-time data, maximizing space use and minimizing traffic congestion. Agents that can autonomously manage emergency actions, such as directing people to safety and managing infrastructure to minimize damage, might be beneficial for earthquake detecting systems. As autonomous entities endowed with decision-making skills are included, we are moving closer to truly intelligent cities. Because of these agents' capacity for independent operation and quick response to changing circumstances, smart cities are able to offer a better quality of security and service while accommodating the changing demands of its residents. As a result, smart cities that have these intelligent agents installed will be more responsive, efficient, and resilient—ultimately capturing the spirit of artificial intelligence in urban planning.

IoT Based Smart Parking System

IoT Based Smart Parking System

Integration of YOLOv5 Algorithm and OpenCV in Innovative Smart Parking Management Approach

The problem of automatic parking lot identification and vehicle detection in open areas is becoming increasingly important due to the increase in the number of vehicles in Indonesia, particularly in big cities, resulting in difficulties in finding parking spaces during peak hours. In this condition, drivers often have to compete for parking spaces. This research aims to develop a smart parking system that integrates YOLOv5 and OpenCV algorithms. This approach thoroughly combines both algorithms to identify parking spaces and detect vehicles in real time in various parking scenarios. It is carried out in an open area with reference to parking conditions at the BRIN Bandung office. This study collected data from three different parking lot conditions, namely empty, partially occupied, and full. In each condition, the system successfully detected the parking lots and vehicles accurately. The novel contribution of this research is the development of a smart parking system that uses an integrated approach, providing an effective solution to the challenges of parking lot availability and vehicle detection. Using the advantages of both algorithms, we successfully created a system that can identify parking spaces and detect vehicles accurately and efficiently under various parking circumstances. Therefore, this research makes a significant contribution to the development of smart and adaptive parking management technology.

Hybrid energy efficient deep learning and redactable consortium blockchain-based secure framework for smart parking in smart cities

Hybrid energy efficient deep learning and redactable consortium blockchain-based secure framework for smart parking in smart cities

Deep learning and saliency-based parking IoT classification under different weather conditions

The world’s population is growing at an exponential rate, which has created a slew of new issues in our daily lives. Among these challenges is the difficulty of finding available parking spaces. To overcome this obstacle, an effective system for detecting vacant parking spaces is crucial, particularly in densely populated areas. Even though there are numerous parking detecting systems on the market today, they all have their own set of restrictions. There are primarily two types of parking systems available. The first is a hardware-based system that uses hardware to detect whether a space is vacant or occupied, and the second is a smart parking system that uses cameras to classify parking status. Vision-based parking systems are becoming increasingly popular due to benefits such as flexibility and cost efficiency. However, due to challenging weather conditions and various occlusions caused by objects, such systems are challenging to develop. The limitations of a vision-based parking system must be addressed for it to become more accurate. To date, many researchers have attempted to incorporate deep learning-based parking systems with various algorithms but none of them has focused on reducing the complexity of Convolutional Neural Network (CNN) training through unsupervised pre-processing steps. In this study, an attempt is made to reduce the complexity of CNN by integrating salient features as a pre-processing step for normalizing weather conditions. It has been observed that salient features-based pre-processing makes the system more robust in terms of accuracy and time efficiency.

IoT-Enabled Smart Car Parking Management: Revolutionizing Urban Traffic Flow

IoT-Enabled Smart Car Parking Management: Revolutionizing Urban Traffic Flow