Enhanced Cloud Car Parking System using ML and Advance Neural Network

<p>Inefficient parking management systems in urban areas lead to limited parking capacity, congestion, and environmental contamination, resulting in time and fuel waste, increased carbon emissions, and economic inefficiency. Smart parking is a technological system that leverages the capabilities of the Internet of Things (IoT) to address urban congestion and parking management issues. It uses IoT sensors to collect real-time data on parking availability, which is then analyzed to optimize usage, improve traffic flow, and simplify the driver experience. Smart parking can be integrated into urban infrastructure, contributing to more efficient and environmentally-friendly urban mobility. Benefits include reduced time spent searching for parking, reduced traffic congestion, reduced greenhouse gas emissions, and new revenue opportunities for parking lot operators. Overall, IoT-enabled smart parking creates sustainable and user-friendly urban environments. This paper presents a multi-agent smart parking approach, providing a comprehensive architectural framework and workflow for various smart parking scenarios. The proposed methodology and structure encompass several aspects of intelligent parking management, incorporating the use of Internet of Things (IoT) technologies. It involves a multi-agent system for monitoring parking spaces, managing traffic, and providing user services. This system enhances efficiency and reduces the need for manual parking, thereby enhancing the overall efficiency of urban parking.</p>

The shortage of parking spaces in metropolitan cities has become a significant challenge, leading to wasted time, money, traffic congestion, and environmental pollution. While smart parking solutions offer potential relief, existing systems often struggle with integration and coordination issues in the complex smart city ecosystem. In response, this paper introduces SCOPE, a cooperative distributed system architecture and interaction model that facilitates the management of parking spaces in a smart city through coordination and autonomous interactions. The system leverages an overlay network, a hierarchical and spatial structure of coordination nodes, and an integration layer to organize traffic and communication among facilities. By incorporating a sharing economy business model, SCOPE maximizes parking resource usage, merges public and private parking resources, and provides economic opportunities for private parking owners. The evaluation results demonstrate that SCOPE significantly reduces search time, traffic, cost, and air pollution while improving driver satisfaction. This novel approach presents a comprehensive solution to the challenges of smart parking management in metropolitan cities, paving the way for more efficient, sustainable, and economically viable urban environments.

With rapid increase in the number of Electric Vehicles (EVs) hitting the market, there arises a need for an efficient parking and charging management technology for them. The paper presents the design and development of a public smart charging and parking system for EVs by implementing a communication network for EV, Electric Vehicle Supply Equipment(EV-SE) and charging infrastructure (electrical utility). This work involves the development of user space modules, like on-board ECU for battery management (charging, discharging and state of charge estimation) and vehicle to infrastructure (V2I) communication and android application as user interface and infrastructure modules like V2I communication based entry management, navigation management to allotted parking slot, and a centralized server for monitoring, analytics and control. The ECUs are implemented in LPC1768 based mbed microcontroller and communication network implemented using Wi-Fi modem. The proposed system can be implemented in the parking areas of shopping malls, hospitals, schools, colleges, industries or any infrastructure where vehicles will be kept parked for some fixed periods of time.

Quantitative evaluation of source interventions for urban air quality improvement - A case study of Delhi city

Shine: A deep learning-based accessible parking management system

In recent times the concept of smart cities has become very popular. Thanks to the Internet of things the idea of smart city now seems to be completed. Consistent efforts are being made in the field of IoT in order to maximize the productivity and reliability of urban infrastructure. Problems such as, traffic congestion, limited car parking facilities and road safety are being addressed by IoT. In this paper, we present an IoT based cloud integrated smart parking system. The proposed Smart Parking system consists of an on-site deployment of an IoT module that is used to monitor and signalize the state of availability of each single parking space. A mobile application is also provided that allows an end user to check the availability of parking space and book a parking slot accordingly.

Among various types of external space, a park, which is a critical urban public infrastructure, must be planned and redeveloped as a smart park that can reflect new technologies rather than being designed and managed in a traditional way. This study sets Busan Citizens Park, a representative urban park of Busan in Korea, as a case study site and presents guidelines for transforming the existing park into a smart park. To set the direction for presenting the guideline for park smartization, the role and function of desirable urban park in the 4th industrial age are defined. After that, smart technologies concerning park management are comprehensively organized through precedent project studies and literature research to examine the applicability of smart technologies. Based on the information obtained through field surveys, manager interviews, specialist interviews, and literature review, the management and usage status of Busan Citizens Park are identified. In order to improve Busan Citizens Park into a smart urban park, we propose smart technologies that can be applied to five components of the park: furnishings and facilities, water, green space, pavement and roads, and users. This study also suggests ways to distinguish management as urban and local dimensions by categorizing the hierarchy of technologies as urban, park, and facility scale. In conclusion, this presents guidelines for the introduction of next-generation technologies that can be applied to urban parks as a way to actively respond to changes that have already begun.

With rapid increase in population in urban cities, availability of parking space is real issue. This parking issue lead to traffic and encroachment of roads for parking. With implementation of smart cities is real time development, smart parking is integral part of this development. Intelligent parking system describe in this paper solve the parking issue and fits in the smart city development, this system is based on cloud-based parking system where user is able to get location of parking spot with helps sensors network and cloud computing. The user is updated with real time data of available parking spot near their destination, and they can choose the spot according to their convenience. The main components of the system are sensor layer, hardware layer, cloud layer and application layer. The sensor layer is controlled by Arduino board or other system on chip which manages the data collected by sensors, this data is sent to cloud through hardware layer cloud layer manages the data accordingly and data is sent to users’ application on the reception of request through application. This interconnection of all the layers is main aspect of IoT (Internet of Things). This system will help user to get the spot in hassle free and quick way.

Smart parking has become an important construction field of smart cities in China and an application field closely related to urban production and life. Establishing an urban smart parking evaluation index system is the top-level design of smart parking construction. Guided by the characteristics of new quality productivity, namely high technology, high efficiency, high quality, and sustainability, this paper constructs an urban smart parking evaluation framework of “one core, two goals, three levels, and six dimensions” on the basis of elaborating the concept and new connotations of smart parking, and extracts the key indicators for smart parking evaluation from six aspects: technological innovation, efficient governance, data-driven, high-quality service, sustainable industry, and people’s satisfaction. Finally, the calculation model and recommended weight value of the indicators are given, and the concept and calculation method of the “smart parking development index” are proposed. The research can provide evaluation methods and action guidelines for the construction of urban-level smart parking systems.

Vehicle parking monitoring and management has become a big challenge for educational institutions with increasing enrollments, high percentage of vehicle ownership and decreasing parking supply which in result triggering blockage of vehicle, congestion, wastage of time and money. In university campuses particularly in Kingdom of Saudi Arabia, vehicle parking monitoring and management problem is getting worse and more frustrating due to the fact that majority of students, faculty and staff members own cars and drive through them to the university campuses. These common problems include finding out people (evidence) who are responsible for the damages (hitting, scraping, scratching and dents) to other cars and the blockage of car due to wrong car parking which takes much time to locate the owner of the car. Moreover, locating or forgetting their car park location another difficulty that is often faced by the students, faculty and staff members. The existing cameras located at the parking lots are only for video surveillance and cannot help in such situations as there is a lack of proper vehicle parking monitoring and management system. To cope with above mentioned problems and to ensure a better parking experience by accommodating increasing number of vehicles in a proper convenient manner, we propose a smart vehicle parking monitoring and management system called CampusSense. In CampusSense, Automatic Number Plate Recognition (ANPR) cameras and android based mobile application is developed to efficiently monitor, manage and protect the parking facilities at university campuses. Parking problems around the university campus faced by the students, faculty and staff members are analyzed by conducting a survey.

– With the rapid increase in urban populations and the corresponding rise in the number of vehicles, efficient parking management has become a critical challenge. Traditional parking systems are not equipped to handle the demands of modern cities, leading to traffic congestion, inefficient space utilization, and a lack of infrastructure for electric vehicle (EV) charging stations. The Smart Parking Management System using IoT for both EVs and Non-EVs aims to address these challenges by integrating Internet of Things (IoT) technology, data analytics, and real-time monitoring. The proposed system utilizes ultrasonic sensors, ESP8266 microcontrollers to detect and manage parking space occupancy while providing real-time updates to users. Additionally, the system includes provisions for managing EV charging stations efficiently. By optimizing space utilization and minimizing the time spent searching for parking spots, this system enhances user convenience, reduces congestion, and contributes to a more sustainable urban environment. . Keywords: Smart Parking, Internet of Things (IoT), Electric Vehicles (EV), Non-Electric Vehicles (Non-EV), Ultrasonic Sensors, ESP32, Urban Mobility, Real-time Monitoring

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.

Micro‐grid can be used as a supplement to power supply of large power grids. Combined with smart park, users can easily manage the micro‐grid. In the tourist scenic area, the local distributed new energy is mainly used to provide energy for the system. The micro‐grid control system as the core of the system controls the optimal operation of the entire smart park. In order to ensure the efficient operation of the entire system, the energy management system is needed for smart control and automatic scheduling. In the grid‐connected mode, the main‐grid complements the system energy and charges the park energy storage device. In the island mode, the distributed energy is fully used to provide energy for the smart park system. The energy power fluctuates greatly, and the energy management system needs to schedule the micro‐grid energy, energy storage system, electric vehicle load and so on, and power quality needs to be monitored. Use of micro‐grid control system in the smart parking deploying photovoltaic power generation, wind power generation, charging and exchanging devices and other devices, real‐time monitoring of the park of cars, charging cars automatically complete charging, automatic processing of visitor cars into and out of smart park, and improve work efficiency.

In recent years, many city governments around the world have begun to use information and communication technology to increase the management efficiency of on-street parking. Among various experimental smart parking projects, deployment of wireless magnetic sensors and smart parking meters are quite common. However, using wireless magnetic sensors can only detect the occupancy of parking spaces without the knowledge of who are currently using these parking spaces; human labor is still needed to issue the parking bills. In contrast, smart parking meters based on image recognition can detect the occupancy of parking spaces along with the license plate numbers, but the cost of deploying smart parking meters is relatively high. In this research, we investigate the feasibility of building an on-street parking management system mainly based on low-cost Bluetooth beacons. Specifically, beacon transmitters are installed in the vehicles, and beacon receivers are deployed along the roadside parking spaces. By processing the received beacon signals using Kalman filter, our system can detect the occupancy of parking spaces as well as the identification of the vehicles. Although distance estimation using the received signal strength is not accurate, our experiments show that it suffices for correct detection of parking occupancy.

Vehicle load is one of the primary live loads for long-span bridges. However, load distribution and composition are complicated on long-span bridges and vary significantly. In general, bridge codes do not include specifications for bridges with long spans. In this study, the microscale traffic model, cellular automaton (CA) model, was adopted to establish a long-span vehicle load model through a merger with real-monitored traffic data. The differences in the current design vehicle loads used in China, the United States, Great Britain, and Europe are discussed. The effects of the CA load with different vehicle density and vehicle composition were estimated for loaded lengths from 100 to 1,000 m with the influence line. The largest moment of a bridge from a CA load was compared with that of the design vehicle load statistically. The results showed that vehicle density, loaded length, and the heavy truck percentage were the main features of the vehicle loading effects. An increase in vehicle density led to higher average and extreme values for the loading effect. When the loaded length was more than 600 m, the impact of the loaded lengths vanished. When the vehicle density was the same, a heavy percentage of truck traffic had a significant impact on the loading effect. Moreover, when the percentage of heavy trucks was high, the design load for relatively shorter loaded lengths needed to be adjusted because the current design load safety margin for a loaded length of not more than 300 m was small.