Parking System Research Articles

Matching Methods of Shared Parking Slots Considering Overdue Parking Behavior

With the continuous increase in the number of vehicles worldwide, parking challenges have become more severe, making it a shared goal for governments to alleviate parking difficulties in urban centers. Shared parking has emerged as an effective solution to address parking problems and has been widely studied in recent years. However, existing research primarily focuses on static or single-period parking matching, often neglecting the conflicts between overdue parking users and subsequent users. Therefore, addressing the impact of overdue parking on shared parking systems is highly important. This study proposes a multi-period dynamic matching decision model (MDMD), which divides the operation period of the shared parking platform into multiple decision points. At each decision point, parking demands are classified into four categories: newly arriving demands, allocated demands with a start time not within the current decision point, overdue demands during the current decision point, and demands affected by overdue parking. Three decision variables are established to determine matching schemes for the first, second, and fourth types of parking demands, facilitating a dynamic decision-making process that effectively mitigates the impact of overdue parking. A corresponding algorithm is designed to solve the model. Since the single-period model is a linear programming model, the CPLEX solver obtains allocation schemes for each decision point. These schemes, along with new parking demands, are used as input for the next decision point, achieving a dynamic matching process. Simulation experiments are conducted to compare the MDMD model with the traditional First-Book-First-Served (FBFS) model based on platform revenue, parking space utilization, and parking demand acceptance rate. The experimental results show that, compared to FBFS, MDMD improves long-term earnings by 83%, actual profits in recent profits by 6.6%, and parking space utilization by 8% while maintaining a similar parking demand acceptance rate. To validate the robustness of the model, additional simulations are performed under various overdue probability scenarios, demonstrating that MDMD maintains stable system performance across different probabilities. These improvements highlight the advantages of the dynamic matching strategy, distinguishing this study from existing methods lacking adaptability. These findings provide valuable insights for the optimization of shared parking systems, contributing to sustainable transportation solutions and efficient urban mobility management.

Effective smart rotary parking management in urban areas

With urbanization on the rise, coupled with the burgeoning intensity of traffic in cities, parking management is becoming an increasingly insurmountable problem. This paper concentrates on the design and development of an intelligent rotary parking, with an appropriable lending function for eight vehicles within a mini urban encroachment. The system combines sensors, servomotors, and an Arduino controller to an automated parking system that saves space by parking vehicles vertically one above another. The proposed solution consists of sensors with an automatic barrier system to allow and control access to vacant parking spaces. To provide more ground parking space with better reliability and lower impact on the environment, the evaluation shows that for urban areas, the system might improve the traffic flow, reduce emissions, and offer a sustainable way of solving problems about parking.

IOT Based Car Parking Using Edge Computing

The promise of autonomous driving is that edge computing can deliver real time services. Using a variety of sensors and communication modules, autonomous cars may function both indoors including in parking lots and outdoors. But because the interior station's ability to communicate with the outer world is limited globe, and the present underpowered autos don't really work; they could have to handle all the data. Autonomously, hence a control mechanism must be put in place. Completely self-sufficient car in the surroundings. In this research, we suggest an edge computing based smart parking system for self driving cars. Smart parking has resolved Boundaries and traffic, and parking spaces are managed using a grid. To assess the effectiveness of the smart parking system, In the current parking system, we contrasted the journey time with the parking time. Furthermore, the resource to make sure that the service quality margin number is offered and maintained; expenses and transaction data are examined.

Research on Intelligent Industrial Park Management System

In recent years, the scale of industrial parks in China has grown rapidly, but the use of intelligent management methods for industrial park management in China has not been carried out for a long time and lacks relevant experience. To explore the management mode and application characteristics of smart industrial parks, this article derives the operation mode of the smart industrial park management system based on the digital twin infrastructure, and elaborates and analyzes the main functions and characteristics of the smart industrial park management system, To provide reference and reference for better promoting the management system of smart industrial parks.

Static and Dynamic Analysis of Automated Car Parking Tower Systems

Background: Our country has dramatically evolved over the decades, and now we have a large number of well-connected roads, public buildings and a rising number of automobiles. With the passage of time, the manual car parking system in commercial spaces has become a stumbling block and wastes time. As a result, it requires a solution that can address these issues. Automated car parking systems are the answer to these issues. Methods: In this study, the static and dynamic response spectrum approach was used to analyze G+13 storey automated car parking by using ETABS-2018 systems Results: The storey displacement, storey stiffness, storey shear and overturning moment of steel and Reinforced cement concrete (RCC) automated car parking tower subjected to static and dynamic load for seismic zone III and zone IV in India have been determined. Conclusion: The RCC automated car parking tower was found to be not only stiffer but also lowered the displacement of construction.

The automated system of the trolleybus park as part of the sustainable city infrastructure

The automated system of the trolleybus park as part of the sustainable city infrastructure

Fusion algorithms on identifying vacant parking spots using vision-based approach

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.</p></div></div></div>

IoT Based Vehicle Accident Detection, Prevention and Parking System

IoT Based Vehicle Accident Detection, Prevention and Parking System

IOT Based Parking Lot for Smart City

Abstract: This project presents an intelligent parking lot system using infrared sensors to detect available parking spaces in realtime. The system improves user convenience and reduces traffic congestion by providing accurate parking availability data. Infrared sensors, chosen for their reliability and low power consumption, are installed in each parking slot. The sensor data is processed centrally and shared via thingspeak website and digital displays. The system will be piloted in a section of the parking lot, with calibration ensuring accuracy. Results show reduced time searching for parking and decreased congestion. The system's scalability, energy efficiency, and real-time capabilities enhance user experience and optimize parking operations. Future developments include predictive analytics and expansion to other hightraffic areas.

Comparative Analysis of Learning-by-Doing in the Development of an Internet of Things Intelligent Parking System Between Those Carried out Individually and Collectively

The Internet of Things (IoT) has become necessary in higher education, so unsurprisingly, IoT learning research has received significant attention from academic researchers. In the meantime, classroom education is essential for facilitating learning by doing things such as engineering lessons. The question remains: what about the learning outcomes of IoT development and the impact on student learning by doing? This research reveals it; therefore, this study aims to compare the effects of students' learning by doing in building an IoT-based automatic intelligent parking system carried out individually and collectively. The research of this study is a combination of experiment and survey methods. The research results show that students who learn collectively by building IoT intelligent parking systems are more successful than students who learn individually. Meanwhile, regarding the level of satisfaction with learning by doing, no differences were found between students who studied individually and collectively.

Spatiotemporal Correlation Analysis of Landscape Pattern and Habitat Quality in and Around China’s Tropical Rainforest National Park

With the establishment of the national park system in China, balancing ecological conservation within national parks and development in surrounding areas has become a critical issue. Despite its importance, studies on the scale effects of landscape fragmentation and its relationship with habitat quality (HQ) in these regions remain scarce. Therefore, this study focuses on the Hainan Tropical Rainforest National Park (HTRNP) in China and the surrounding nine cities and counties. The optimal scale for analyzing the spatiotemporal characteristics of landscape patterns, we assessed the best grain size and extent of landscape pattern indices using the regional information loss assessment model, area loss index, and semivariance function analysis. Based on this, the Fragstats and InVEST models analyzed the spatiotemporal evolution of landscape patterns and HQ from 1980 to 2020. Additionally, bivariate spatial autocorrelation theory was applied to examine the spatial correlation between the two. The results show that (1) the landscape indices in the study area vary with grain size and extent. The optimal grain size is 300 m, and the optimal extent is 2 km × 2 km. (2) Over the past 40 years, the landscape pattern of HTRNP has remained stable. In contrast, the surrounding areas exhibit notable spatial differentiation, with landscapes in coastal town centers becoming increasingly fragmented. (3) Over the past 40 years, the overall HQ in the study area has been relatively high, showing a spatial distribution pattern that decreases gradually from the center of HTRNP outward to rural areas, towns, and coastal regions. HTRNP has maintained a high and stable HQ, while the surrounding areas exhibited a decreasing trend in HQ, although the decline was relatively modest. (4) The spatial distribution of HQ aligns closely with landscape patterns, indicating a significant spatial correlation. The landscape connectivity of HTRNP remains high, with consistently superior HQ. In contrast, coastal town centers are characterized by highly fragmented landscapes and poor HQ. This study reveals the spatiotemporal dynamics of landscape patterns and HQ, along with their spatial associations. These findings provide a scientific basis for ecological conservation in national parks and the planning optimization of surrounding areas.

Parking Slot Scanning for Maximum Efficiency Using Python

The growing number of vehicles in major cities has posed significant challenges in parking lot management. Motorists often have difficulty finding empty parking slots quickly, which not only wastes time but also aggravates traffic congestion and increases air pollution. This research develops a Python-based smart parking system by utilizing the OpenCV library to detect the status of parking slots in real-time. The system uses a camera as the main sensor and processes the image using techniques such as grayscale, Gaussian blur, and adaptive threshold to identify the parking slot status, whether empty or occupied, with good accuracy. The parking slot coordinate data is stored in CSV format to ensure efficient data management. Experimental results with video recordings show that the system is able to operate well in various parking conditions. The system proved to be cost-effective and easy to implement, making it an ideal solution for parking managers who want to improve management efficiency without being burdened with high costs. This research offers a practical solution to help motorists and parking managers optimize parking space usage, reduce search time, and minimize negative impacts such as congestion and carbon emissions.

The impact of national park construction on the livelihood capital of original residents: A case study of the Qilian Mountain National Park system pilot

While achieving ecological protection, the construction of national park inevitably has an impact on livelihood capital of original residents. Determining livelihood impact of national parks is essential for harmonizing ecological protection and livelihood development. In this study, we have conducted a field survey of 247 households in bordering villages of Qilian Mountain National Park (QMNP). We used difference-in-difference model (with fixed effect and with score propensity matching) to evaluate the net impact of the pilot construction of QMNP on livelihood capital of original residents. The results showed that the pilot construction of QMNP has no significant impact on the total livelihood capital of original residents. Specifically, the impact of the pilot construction of QMNP on natural capital and physical capital was negative. Households inside the pilot area experienced more significant reductions in grassland area, arable area and the scale of large livestock production. At the same time, we also observed a significant increase in social capital. This should be the result of increasing the diversity of livelihoods or changing livelihood strategies. The ecological compensation system close to the needs of residents and the rational development of economic activities in the general control area will help to achieve the balance between ecological protection and livelihood development in QMNP. Our study results can provide a reference for evaluating the effects of national park construction and improving policy design.

Parking Assistant with the Simulation of Application

Smart Parking can be used for any city. It aims to improve the efficiency and sustainability of urban transportation systems by using technology to manage parking systems. This article provides an overview of the different components of smart parking systems. The increasing vehicular population in urban areas has led to significant challenges related to parking management, exacerbating traffic congestion, environmental impact, and user dissatisfaction. The app facilitates real- time information on available parking spaces, reservation capabilities, and payment integration, thereby enhancing user experience and operational efficiency. We summarize the methodologies applied in developing the app, which include sensor integration for dynamic parking space monitoring and mobile application development for user interaction. The article concludes with a consideration of the future of smart parking and its role in the overall development of smart cities. In conclusion, the findings serve as a basis for future advancements in intelligent parking systems, ensuring improved service delivery for urban commuters.

Smart Park: Innovating the Future of Vehicle Parking

Effective parking solutions become increasingly important as urban areas increase because of wasted space, time-consuming searches, and traffic congestion. By utilizing cutting edge technologies to improve parking management and user experience, the Smart Vehicle Parking System (SVPS) tackles these issues. To deliver dynamic solutions, SVPS combines sophisticated algorithms, sensor networks, and real-time data. Parking space sensors track occupancy and transmit data to a central system, where machine learning maximizes space distribution and directs vehicles to open spaces. This smart parking system is software and hardware based. Different algorithms are used to find vacant slots using machine learning and deep learning. IR sensors, RFID, Servomotors and ESP 32 microcontrollers and Internet of things are useful to find vacant slots in real time signal processing. In traditional method it is difficult to search free space, and this method is time consuming, Smart parking system overcome this problem with new technology and improve parking management system. The SVPS not only improves urban mobility but also contributes to reducing vehicle emissions and enhanced overall efficiency. Keywords— smart parking, integration, secure payment,advanced technologies, convenient.

Design of Automatic Parking System of Lhokseumawe State Polytechnic Using RFID

The design of the barrier or portal system now in use predominantly relies on a manual mechanism; however, the automatic barrier door of the Lhokseumawe State Polytechnic aims to create a device that facilitates the opening and closing of the parking portal for users. This Portal system employs RFID (Radio Frequency identifying) cards as identifying tags for vehicles entering and exiting. This autonomous portal comprises several components, specifically an RFID (Radio Frequency Identification) Tag, an RFID Reader, a Servo Motor, an Arduino Uno, and a Buzzer. The servo motor is designed to elevate and lower the barrier door, while the buzzer is intended to detect vehicles entering and exiting via unregistered RFID (Radio Frequency Identification) tags.

Inovasi Parkir Cerdas: Prediksi Posisi Kendaraan dengan Teknologi RFID dan Arduino Mega 2560 R3

Urban parking management is an urgent challenge in an ever-increasing vehicle mobility era. This research develops an intelligent parking system that integrates RFID (Radio-Frequency Identification) technology and Arduino Mega 2560 R3 to predict the vehicle's position in real time. The initial literature study identified various approaches to parking management, but none comprehensively combined RFID and Arduino Mega 2560 R3. Therefore, this study fills this gap. The research method involves literature study, system design, hardware and software development, and field testing. In the proposed system, vehicles are equipped with RFID cards, and the parking infrastructure is equipped with RFID readers. When the vehicle enters the parking area, the system reads the RFID card and predicts available parking positions. The results of field tests show an increase in the efficiency of parking space use of around 37% to 46%, and the accuracy of parking position prediction is above 90%. This research provides effective solutions for urban parking management, reducing traffic congestion and air pollution. This system also increases the driver's comfort in finding a suitable parking space. Thus, this research has the potential to support more sustainable urban development by optimizing the use of parking lots and reducing the negative impact of motorized vehicles in urban areas.

Juridical Implementation of Regional Regulation Number 11 of 2023 Regarding Local Taxes and Levies on the Elimination of Subscription Parking in Tulungagung

The implementation of Regional Regulation Number 11 of 2023 concerning Local Taxes and Levies in Tulungagung District has significantly altered the management of public parking services. This regulation abolishes the subscription parking system and replaces it with a conventional ticket sales system. The accompanying procedural and technical changes aim to streamline operations and enhance revenue collection for the local government. This study investigates the juridical-sociological aspects of the implementation process, focusing on its impacts on local revenue, quality of public services, and citizen satisfaction. Through qualitative analysis involving primary data from interviews and secondary data from documentation and field observations, the research evaluates how the elimination of subscription parking has affected revenue generation and service provision. Key findings highlight a significant decline in revenue from subscription parking but indicate potential benefits in improving service efficiency and citizen satisfaction. Recommendations include continuous policy evaluation and adaptation to optimize regional financial management and public service provision in Tulungagung District.

Integrated automatic parking path planning and trajectory tracking optimization method

Current automatic parking systems often separate path planning and trajectory tracking, increasing complexity and failing to meet the parking needs of autonomous cars. Consequently, this study presents an approach based on Multi-stage Nonlinear Model Predictive Control (MSNMPC) for integrated autonomous parking path planning and trajectory tracking optimization. The method represents the evolution of uncertainty parameters over time through a scenario tree, and the MSNMPC controller with a predictive horizon P defines P + 1 phases, each with specific cost and constraint functions that depend only on the vehicle state and control inputs of that phase to satisfy the constraints of all phases of the parking process. In addition, the method integrates path planning and tracking control into one optimization problem, which is solved online to achieve integrated parking control. Simulation confirms that, in comparison to MPC with RRT* hierarchical control, the integrated parking scheme of MSNMPC has less lateral error and offers superior flexibility and tracking performance. To verify the generality of the scheme, it was validated in the diagonal parking scenario and the vertical parking scenario, respectively. The results show that, compared to the control experiments, the parking elapsed time has been reduced by 10.76% and 9.02%, respectively, enhancing parking efficiency. In addition, the parking error has decreased by 30.77% and 38.46%, respectively, thus improving parking accuracy. Moreover, the minimum safe distance in the control scheme for addressing uncertainty factors is 0.7 m greater than that of the control experiments, meeting the driving requirements for driverless vehicles in parking scenarios.

The projected exposure and response of a natural barrier island system to climate-driven coastal hazards

Accelerating sea level rise (SLR) and changing storm patterns will increasingly expose barrier islands to coastal hazards, including flooding, erosion, and rising groundwater tables. We assess the exposure of Cape Lookout National Seashore, a barrier island system in North Carolina (USA), to projected SLR and storm hazards over the twenty-first century. We estimate that with 0.5 m of SLR, 47% of current subaerial barrier island area would be flooded daily, and the 1-year return period storm would flood 74%. For 20-year return period storms, over 85% is projected to be flooded for any SLR. The modelled groundwater table is already shallow (< 2 m deep), and while projected to shoal to the land surface with SLR, marine flooding is projected to overtake areas with emergent groundwater. Projected shoreline retreat reaches an average of 178 m with 1 m of SLR and no interventions, which is over 60% of the current island width at narrower locations. Compounding these hazards is subsidence, with one-third of the study area currently lowering at > 2 mm/yr. Our results demonstrate the difficulty of managing natural barrier systems such as those managed by federal park systems tasked with maintaining natural ecosystems and protecting cultural resources.