Smart Parking Management System Research Articles

Eco-Friendly Smart Car Parking Management System with Enhanced Sustainability

Efficient car parking management systems that minimize environmental impacts while maximizing user comfort are highly demanding for a future sustainable society. Using electric or gasoline vehicle-type information, emerging computation and communication technologies open the opportunity to provide practical solutions to achieve such goals. This paper proposes an eco-friendly smart parking management system that optimally allocates the incoming vehicles to reduce overall emissions in closed parking facilities while providing comfort incentives to the users of electric vehicles (EVs). Specifically, upon arrival of a car, the most suitable parking spot is determined by minimizing an adaptive objective function that indirectly reflects anticipatory operation for the overall performance maximization of the parking facility using electric or gasoline vehicle-type information. The adaptive objective function includes a trade-off factor that tunes driving and walking distances, relating emissions and comfort to treat incoming vehicles appropriately. The proposed system is simulated for managing a model car parking facility in a shopping complex in Japan, and the aspects related to fuel consumption, CO2 emissions, and user comfort are evaluated and benchmarked with other standard parking management systems. The proposed system reduces CO2 emissions and fuel consumption and improves parking efficiency compared to the current parking management systems, while also prioritizing user comfort.

Editorial: Smart Parking Management System Using Artificial Intelligence

The escalating challenges of urban parking due to increasing urbanization and rising vehicle numbers have spurred the integration of Artificial Intelligence (AI) into parking management. This article explores the potential of a Smart Parking Management System (SPMS) driven by AI to revolutionize urban parking infrastructure. The SPMS leverages AI technologies, including advanced algorithms, machine learning models, and real-time data analytics, to intelligently monitor, allocate, and optimize parking spaces. Beyond addressing immediate concerns such as congestion and parking availability, the system aligns with broader urban development goals of sustainability and improved quality of life. The SPMS offers benefits beyond convenience, contributing to a more sustainable and eco-friendly urban environment. By optimizing traffic flow and reducing time spent searching for parking, the system aims to decrease fuel consumption, emissions, and overall environmental impact. The emergence of Internet of Things (IoT) technologies plays a crucial role, with sensors in parking spaces providing real-time occupancy information, and enabling dynamic system responses. Mobile applications and smart devices further empower users with real-time information, fostering smart and sustainable transportation habits. While the promise of AI-driven SPMS is considerable, challenges such as data privacy, security, and seamless integration into existing urban infrastructure must be addressed.

BUDGET-BASED SMART CITY IMPLEMENTATION FOR TIER III CITIES

In the present day's world the Internet of Things (IoT) is gaining popularity as a field of research in various engineering applications. IoT includes various elements like communication technologies, communication protocols, identification technologies, sensing, computation, and actuation. Today, almost every home is equipped with smart IoT home appliances. As cities grow and expand, smart and innovative solutions are crucial for improving productivity, increasing operational efficiencies, reducing management costs, and enhancing the lifestyle of human beings. A smart city consists of a number of applications. In this paper, budget-based implementation of smart cities with specific applications like smart parking, smart dustbins, smart streetlights, and smart traffic monitoring and management systems are done using various IoT boards, various sensors, actuators, and various IoT cloud platforms. In the smart city application, several heterogeneous devices are connected through the internet for communication, and huge amounts of data are collected from various applications and stored in databases. Data are classified, analyzed, and visualized on both web and mobile dashboards.

A Smart Real-Time Parking Control and Monitoring System.

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle's license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review

Major cities in India have a significant number of vehicles, and the rate of ownership is increasing every day. However, the lack of proper parking infrastructure in these cities causes problems such as difficulty in finding parking spaces. According to the Urban Mobility Survey 2023 by Times Network, nearly 74% of vehicle owners in metropolitan cities struggle to find a parking slot. Various measures have been implemented to address this issue. One of the most promising measures is a smart parking management system. This system can use technologies like Radio Frequency Identification (RFID) and Automatic License Plate Recognition (ALPR) to make check-in and check-out easier. It can also include Wireless Sensor Networks (WSN), wired sensors, or visual occupancy detection to provide real-time occupancy status. The smart parking management system can offer useful services through mobile or web applications such as parking occupancy monitoring, reservation, payment gateway, occupancy prediction, automated check-in and check-out, and parking record management. The purpose of this review paper is to summarize the works undertaken in the field of smart IoT parking systems and educate the technological community about the technologies, features, and procedures for implementing the smart parking management system. In the paper, we aim to summarise the works on occupancy monitoring in smart IoT parking systems addressing the advantages and issues of the present occupancy monitoring methods, also suggesting future inclusions for smart IoT parking systems.

Internet of things enabled parking management system using long range wide area network for smart city

As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT.

An Intelligent Smart Parking System Using Convolutional Neural Network

Saudi Arabia has started building smart cities and communities as part of the Saudi 2030 vision, which aims to digitalize all services. Smart cities use different types of technologies and data to improve the quality of life for citizens, manage resources, and make operations more efficient. In big cities such as Riyadh and Jeddah, the number of vehicles on the road has dramatically increased. Hence, parking has become a problem since there are limited spaces available. In this article, a novel, intelligent, and automated method for vehicle parking and management is proposed. This approach employs a convolutional neural network (CNN) tool to train the algorithm deeply. Image segmentation and preprocessing techniques are employed as well. All operations are automated and cost-effective since the proposed smart parking management system utilizes only a single camera to provide real-time views of the status of a parking lot. Furthermore, there is no need for human interference, and it is easy to maintain. Several simulation scenarios were conducted on MATLAB to validate this approach and prove its efficiency. A comparative evaluation between the proposed system and some works of literature is provided, and it indicates that the developed system outperforms the works from the preexisting literature.

Automatic number plate detection using TensorFlow in Indian scenario: An optical character recognition approach

India, a country of over 130 Crores population, witnessed an exponential rise in the number of vehicles running on the road. As per the survey made in the year 2019, in India there is one death in every 4 min occurring due to over speeding. Moreover, there are around 2 lakhs vehicles being stolen in India per Year. This number can be reduced to a greater extent if proper steps are taken. Automatic number plate detection is a very important tool when it comes to detecting the license plate of a vehicle from the image of a vehicle so taken. Moreover, same tool can be implemented in the case of over speed monitoring, smart parking management system, law enforcement and electronic toll collection. In this paper, work has been carried out to automatically detect the number plate of a vehicle using computer vision technique. TensorFlow, a model developed by Google is used for the purpose of training and testing of the algorithm. A data set of about 500 images obtained at the different condition of the illumination, camera angle and varied distances has been used while training and testing the model. This resulted in an improved accuracy of about 85% with which the license plate has been detected. Further optical character recognition is implemented in order to get the license number from the image being detected.

Parking Detection Using Combined Magnetic Sensor and Pulsed Coherent Radar

With its potential to solve the problem of car parking difficulties, smart parking management systems have elicited a significant amount of interest from both academia and industry. Robust and reliable parking detection is a key technology for smart parking management systems. Presently, magnetic sensors are being widely used in parking detection. However, they are prone to produce high false and missed detection rates in environments that have geomagnetic interference. This article proposes a new sensing device based on the combination of a magnetic sensor and a pulsed coherent radar. The sensing device is used for parking detection in the context of Internet of Things. A Dempster–Shafer evidence theory-based collaborative fusion approach is developed to improve the accuracy of vehicle detection. Pulsed coherent radar is triggered by the magnetic sensor to reduce sampling power consumption. The prototype system is tested under various interference conditions. The results show that the combination of magnetic and radar sensors improves vehicle detection accuracy, and the lifetime of battery-powered sensor node is longer than five years.

An Evaluation of Temporal- and Spatial-Based Dynamic Parking Pricing for Commercial Establishments

All smart parking management systems (SPMS) have incorporated the dynamic pricing into its features and capabilities. The collection of parking fees on a corresponding spot has been dependent on either its time- or space-value, with most SPMS utilizing temporal-based parking fee collection. However, little to no study has been conducted to assess or evaluate these pricing schemes according to its friendliness and economics towards both parkers and business operators. In this work, we evaluate two current temporal- and three proposed spatiotemporal-based dynamic parking pricing methods by computing their social optimum range and economic effects to both users and parking management entities. Our extensive analysis utilizing both empirical mobility traces and driver parking duration behavior provide important insights in the current pricing setups and present necessary adjustments in improving parking fee collection that contribute to societal benefits.

A systematic framework for urban smart transportation towards traffic management and parking

<abstract> <p>Considering the wide applications of big data in transportation, machine learning and mobile internet technology, artificial intelligence (AI) has largely empowered transportation systems. Many traditional transportation planning and management methods have been improved or replaced with smart transportation systems. Hence, considering the challenges posed by the rising demand for parking spaces, traffic flow and real-time operational management in urban areas, adopting artificial intelligence technologies is crucial. This study aimed to establish a systematic framework for representative transportation scenarios and design practical application schemes. This study begins by reviewing the development history of smart parking systems, roads and transportation management systems. Then, examples of their typical application scenarios are presented. Second, we identified several traffic problems and proposed solutions in terms of a single parking station, routes and traffic networks for an entire area based on a case study of a smart transportation systematic framework in the Xizhang District of Wuxi City. Then, we proposed a smart transportation system based on smart parking, roads and transportation management in urban areas. Finally, by analyzing these application scenarios, we analyzed and predicted the development directions of smart transportation in the fields of smart parking, roads and transportation management systems.</p> </abstract>

Distributed parking management architecture based on multi-agent systems

<span>With the increase of the number of vehicles on the road, several traffic congestion problems arise in the big city, and this has a negative impact on the economy, environment and citizens. The time spent looking for a parking space and the traffic generated contributes to mobility and traffic management problems. Hence the need for smart parking management to help drivers to find vacant spaces in a car park in a shorter time. Today, researchers are considering scenarios in which a large amount of services can be offered and used by drivers and authorities to improve the management of the city's car parks and standards of quality of life. Based on literature on smart parking management system (SPMS), we have established the most important services needed such as reservation, orientation, synchronization, and security. The dynamic distributed and open aspect of the problem led us to adopt a multi-agent modeling to ensure continuous evolution and flexibility of the management system. In this conceptual paper, we propose to structure those services on a multi-agent system (MAS) that covers the whole functions of a distributed SPMS. Each service is provided as an autonomous agent, able to communicate and collaborate with the others to propose optimized parking space to customers.</span>

Smart parking management system with dynamic pricing

Smart parking is becoming more and more an integral part of smart city initiatives. Utilizing and managing parking areas is a challenging task as space is often limited, finding empty spaces are hard and citizens want to park their vehicles close to their preferred places. This becomes worse in important/posh areas of major metropolitan cities during rush hour. Due to unavailability of proper parking management system, citizens have to roam around a lot in order to find a suitable parking area. This leads to the wastage of valuable time, unnecessary fuel consumption and environmental pollution. This paper proposes a smart parking management system (SPMS) based on multiple criteria based parking space reservation algorithm (MCPR) that allows the driver/owner of vehicles to find and reserve most appropriate parking space from anywhere at any time. The system also considers the concept of dynamic pricing strategy for calculating parking charge in order to gain more revenue by the government agencies as well as private investors. The system employs sensors to calculate concentration index, average inter-arrival time of vehicles of a parking area for better parking management and planning. The simulation results show that proposed system reduces the average extra driving required by the users to find a parking area and hence it will reduce traffic congestion, which in turn reduces air pollution caused by unnecessary driving to find a proper parking area.

Design and Implementation of Smart Car Parking System using LabView

Abstract: Due to rapid proliferation in the number of vehicles on the road, finding a vacant car parking space is becoming challenging and ubiquitous in every major city, resulting traffic problems are bound to exist. Increase in traffic causes the number of accidents thatcause serious bodily harm to the road users, the pollution caused by the large amount of CO2 released by the vehicles, and the continuous stress of drivers who must drive in often narrow and very busy roads and who must look for a long time to find a space to park. Thus, tosolve the parking problem, lot of research is being done on smart parking management system mechanisms. The existing system gives us the information about the empty slots availability but does not give information about the exact location of parking slot available in sucha big area. This paper proposes an efficient, cost effective smart car Parking System on wireless Sensor Networks (WSN) technology using LabView that can easily locate and secure a vacant parking space at any car park deemed convenient to them. The parking slots are continuously monitored, and data is continuously updated in the display board. Index Terms: parking system, smart parking, IR sensor

Development of smart parking management system

As the usage of vehicles is increasing day by day, it became difficult to identify a proper parking space for every vehicle. Smart Parking System deals with development of mobile application, that is useful for user to access detailed information about parking space and managing it efficiently in parking lot. Smart Parking System uses Image Processing technique to identify registration plate of a vehicle and also provides autonomous door opening and closing operation whenever it detects a vehicle at entrance of a parking lot. The mobile application provides information about available parking spaces, security and safety features of parking lot like fire and gas leakages alert. Raspberry Pi is the control unit which controls and processes the whole operation of the system. Liquid crystal display (LCD) display is located at entry point of the parking lot to display current parking space availability. Infrared (IR) proximity sensors are employed to identify the existence of vehicle at the entry gate of parking lot. From the captured image, characters from vehicle registration plate can be identified, and then Raspberry Pi sends a signal to servo motor to open the gate for a specified interval of time. The user will then park the vehicle in the available parking slot. When the user wants to leave the space and moved the vehicle, the date and time information will be captured which will be further used for processing the bill.

The Smart Parking Management System

With growing, Car parking increases with the number of car users. With the increased use of smartphones and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the server and are recovered by the mobile application which offers many options attractively and with no cost to users and lets the user check reservation details. With IoT technology, the smart parking system can be connected wirelessly to easily track available locations.

Smart parking management system using SSGA MQTT and real-time database

Smart parking system as a part of smart city development has been widely proposed with several research. In this research, proposed a system of parking management application that functions to monitor and control the location of parking slot that can be used by the parking management and parking users. The web application connected to ultrasonic sensor and GPS using MQTT protocol and real-time database. The research used modify algorithm of the SSGA, to optimize the allocation of empty parking slot and MQTT protocol to obtain the faster response time of the system when many users are accessing the website application. The results obtain a variation of sending delays from the client publish to firebase at 4 seconds. Meanwhile, for the sending delay from the broker to firebase the variation was at 2 seconds for each time of data sending.

An IoT-Driven Vehicle Detection Method Based on Multisource Data Fusion Technology for Smart Parking Management System

Recently, with the increasing difficulty of parking in cities, the wireless vehicle detectors (WVDs) based on the Internet-of-Things (IoT) technology and magnetic sensor have been widely used to obtain parking space status data, which lay the foundation for smart parking management system (SPMS). However, there is a weak magnetic region under every vehicle where the magnetic variation is even smaller than that distorted by adjacent vehicles, which leads to the accuracy decrease of the magnetometer-based vehicle detection algorithm (MB-VDA). To improve the performance of the WVDs, an IoT-Driven vehicle detection method that combines the heterogeneous data feature of the UWB channel with that of the magnetic signal is proposed in this article. In the proposed method, the length of the propagation path and signature of the channel impulse response (CIR) with respect to the vehicles, which can be obtained from UWB modules, are introduced to achieve vehicle detection. Moreover, considering that the energy consumption of UWB modules is much higher than that of magnetic sensors, the UWB-based vehicle detection algorithm is only activated when the signature of magnetic signals cannot enable MB-VDA to output an accurate decision. Finally, the proposed method was evaluated in a commercial parking lot under different conditions. The experiments show that it has a detection accuracy of 98.81% when the sampling rate of the magnetic sensor is 1 Hz.

The Smart Parking Management System

With growing, Car parking increases with the number of car users. With the increased use of smartphones and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the server and are recovered by the mobile application which offers many options attractively and with no cost to users and lets the user check reservation details. With IoT technology, the smart parking system can be connected wirelessly to easily track available locations.

An IoT-based smart parking management system

In this paper, the author implemented smart parking information management system based on IoT using ultrasonic parking sensor and Bluetooth beacon. Ultrasonic IoT sensors used for parking sensing are controlled by the Arduino board to collect parking sensing data, and the collected parking information is transmitted to the server in real time using the MQTT protocol. The server stores the parking information in the database and provides the vehicle driver with real-time parking status using the MQTT protocol. The driver starts the smartphone app in the parking lot and enters the parking lot. The smartphone automatically recognises which parking space using the Bluetooth beacon signal at the entrance of the parking lot and can confirm the parking information of the parking space to be parked at a glance by communicating with the server. The parking management system implemented in this study not only helps the driver to park the car but also uses the real-time parking information stored in the database to obtain marketing information such as hourly, daily or monthly number of visiting customers and average shopping time.