Toward Distributed Smart 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.

Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT‐enabled environments focusing on proposing an ML‐based model for predicting available parking space. The study evaluates the performance of various models including K‐nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1‐score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1‐score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT‐enabled environment. On the other hand, models such as K‐nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT‐enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real‐time scenarios, and better user satisfaction in the IoT‐enabled environment.

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.

Based on the analysis of the vehicles total number growth rates, which exceed the rates of expansion and optimization of the transport infrastructure, the need for the introduction of real-time traffic forecasting and control systems is shown. The factors that make it possible to detect the probability of potentially dangerous situations on the road, such as traffic jams, accidents and lack of parking spaces, respectively, in certain urban areas, based on the data of sensor networks and surveillance cameras combined within the global system of the Internet of Things, have been determined. It is proposed to build a sensor network based on magnetic sensors, which allows for high-precision geolocation with refinement of the received data by using ultrasonic sensors and optical monitoring tools, while identification is carried out by reading RFID tags. It is shown that the task of optimal organization of the relay system includes the determination of the features of the city infrastructure and statistical indicators of the city's traffic flows, and for a multi-level communication system, protocols are determined depending on the distance between nodes, requirements for the level of data protection, data transmission speed, minimum radio signal amplitude, as well as restrictions on the power supply of a separate node. The presented topology of the relay network includes the organization of sensor nodes into clusters, transmission from the main node of the cluster to the gateway node, and from the gateway nodes to the base station. On the basis of the specified model, a scheme for building cluster self-organization algorithms can be presented by forming clusters in real time according to the topology of the cluster tree, which allows simplifying the data transfer subsystem and reducing the time of processing input data. The developed scheme for analyzing the traffic flow at the intersection and the availability of parking spaces can be used in the development of methodological recommendations for the implementation of the "Smart City" concept and the creation of software applications that provide drivers with information about the state of traffic and predicted changes within a certain time interval. Keywords: automaticregulation of auto-traffic, controlled intersection, sensor network, Internet of Things, geolocation, identification, self-organization of clusters.

Parking space is becoming a serious problem due to the day-by-day increase in number of vehicles on the road. Particularly, in cities with large population, or in places where sports or artistic events are scheduled, looking for parking space is a major problem and finding a parking spot can be a frustrating experience. In order to combat this problem, some parking lots have introduced sensors to detect when a car enters or leaves a parking lot in order to track capacity and alert drivers if they are full. This is a partial solution that allows drivers to determine if a parking lot has open parking spots, but not the exact location of those spots. In this paper, we present a prototype of smart parking system using wireless sensor technology and networks. Using a Wireless Sensor Network (WSN), parking spot statuses (occupied or idle) are detected and transmitted to a database. This information then be accessed by users through website or mobile app (application) to receive real-time updates. This system should provide users with near instantaneous updates of available parking spots while the WSN allows for flexibility of sensor placement. With the successful implementation of smart parking, the economical and time costs associated with traffic jams, cost associated with wasted gas fuel, and time looking for an empty parking space that are caused by inefficient parking will be significant reduced.

Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

In densely populated areas, obtaining parking spaces is a major problem because the number of automobiles on the road is increasing daily. RFID is the most often used technique for removing or overcoming the cause. Checking the card's balance rather than hunting for parking spaces at far-off locations is the present RFID concept's procedure. The biggest problem with the existing approach is that it is hard to measure how much money is taken out because it varies over time and between various slots. Therefore, this research study provides a solution, namely the suggested method, which guarantees an efficient monitoring system and permits the monitoring of parking spaces in remote regions. These efforts The goal of this project is to tie the RFID concept to the Internet of Things (IoT). Users can communicate remotely regarding parking space availability thanks to IoT's client-server connection. The creation of a website that informs users when parking spaces are available would enhance the mobile-friendly environment. From a distance, users will be able to reserve a parking spot, and that spot will be held for 30 minutes while it waits for the user to arrive. After the time restriction has gone and the slot is still available, the user must rebook it. By doing this, parking lot traffic congestion is reduced to a minimum. This can be employed in retail areas where traffic congestion is regularly caused by parking concerns.

Nowadays the concept of smart cities has gained grate popularity. and thanks to the IoT of things technology for the concept and the idea of smart city in this paper, we present an IoT based smart parking system. A Smart parking system that helps drivers to find vacant spot. Driver Problems such as limited car parking, traffic problems and road safety are being addressed by IoT. In this paper, I present an IoT based smart parking system. The Smart car Parking system will help user to solve the problem for finding a parking space and to reduce the time that they have spent in searching for the nearest available car parking. A mobile application is also provided that allows an end user to check the availability of parking space and book a parking slot according to their time convenience.

The concept of Wireless Sensor Networks (WSNs) has garnered significant global attention due to their wide range of applications. According to the IEEE 802.11 standard, WSNs are wireless networks consisting of sensor nodes (SNs) that interconnect via wireless communication links. These SNs are capable of sensing, processing, and wirelessly transmitting data, even in challenging environments. WSNs are primarily utilized for communication in various domains, including smart cities, healthcare, residential areas, and military applications. However, the deployment of WSNs in environments such as smart cities come with some challenges, particularly traffic congestion. Traffic congestion in smart cities, particularly during peak hours, is often caused by a high volume of vehicles traveling at the same time, resulting in delays, accidents, and inefficiencies under various weather conditions, including sunny and rainy days. Therefore, this paper proposes a novel algorithm called the Fusion-based Intelligent Congestion Management (FICM) algorithm, developed through the integration of the Navigation Reference Spatial Data (NRSD) algorithm and Fusion-based Multimodal Abnormal Detection (FMAD) algorithm. The objective of FICM is to mitigate on-road traffic congestion within smart cities effectively. The algorithm’s performance was evaluated using Network Simulator 3 (NS-3) by comparing its effectiveness with the NRSD and FMAD algorithms. Under sunny weather conditions, the NS-3 simulation results revealed that the FICM algorithm achieved an average False Alarm Rate (FAR) of 0.83%, a Mean Time to Detection (MTTD) of 76.0%, and a Detection Rate (DR) of 84.3%, outperforming both the NRSD and FMAD algorithms. Similarly, under rainy weather conditions, the FICM algorithm demonstrated an average FAR of 14.09%, an MTTD of 57.03%, and a DR of 78.04%, surpassing the performance of the NRSD and FMAD algorithms within the smart city environment.

Smart parking management aims to leverage advanced sensing and monitoring technologies to optimize the usage of parking spots within large car parks and ease parking for drivers by directing them to available parking spots, thereby reducing unnecessary stress and delay that further worsen the traffic conditions. The idea of smart parking development began to make parking easier, faster, with less of a drain on resources which are slowly disappearing. The aim of the Smart Parking Management System (SPMS) proposed in this paper is to assist drivers in alleviating several issues linked to parking. Our SPMS provides a sensing platform supported by a mobile app to enable real-time interaction of drivers with the car park infrastructure in a way that minimizes drivers' search time for an empty parking spot within large multi-storey car parks and facilitates the localization of their parked cars. To validate the efficiency of our SPMS, a small scale test-bed was developed as a proof of concept and tested under five different scenarios. The obtained results highlight its effectiveness and call for its potential integration in real world car parks.

With the rapid growth of the economy, the number of vehicle users has surged, leading to an increasing demand for parking spaces. The widespread use of smartphones has encouraged users to turn to mobile application-based solutions. The development of IoT technology has facilitated the integration of wireless connectivity, mobile devices, and mobile applications.. In order to provide a complete parking solution for both users and parking space owners, this paper suggests an smartphone application and a smart parking system powered by the Internet of Things (IoT).The application enables users to book parking spots, verify reservations, find the closest spot based on vehicle size, find the parking slot, and retrieve daily, weekly, and monthly account data. The availability and location of open parking spaces are communicated using Wi-Fi, microcontrollers, and infrared (IR) sensors.And technology for wireless communication to a server. A smartphone app is then used to retrieve this data. RFID tags affixed are employed to verify users who make hourly, daily, weekly, or monthly parking reservation reservations. Based on the size of the vehicle, a scheduling algorithm is used to determine the closest available space. The seamless user experience is the goal of the mobile app's design. The study also emphasizes the difficulties and inefficiencies brought about by conventional parking arrangements. The authors suggest an Internet of Things (IoT)-based smart parking system that makes it simple for users to find open spots in a certain location.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

Dynamic parking space allocation at urban scale: Problem formulation and resolution

In the realm of smart cities, issues related to vehicle parking have increasingly contributed to traffic congestion, primarily due to drivers searching for vacant spots and the inefficient management by parking operators. Therefore, smart parking systems must continuously be enhanced with real-time models that can reflect the availability of parking spaces and improve the utilization of parking capacity, thereby addressing part of these challenges. This paper proposes a two-stage hybrid approach aimed at optimizing parking space reservations in smart parking systems. The model enables drivers to locate the most time-efficient parking spot within these systems. In the first stage, the parking space is evaluated within an environment defined by fixed capacity. In the second stage, the most suitable parking spot is made available through a hierarchical parking management system using a search-based method. The approach is grounded in Markov Chain modeling, which is employed to accurately estimate the number of vehicles requiring reservations. Underestimation of this figure results in insufficient demand adjustments, whereas overestimation leads to additional operational costs. Therefore, the proposed system uses Markov Chains for capacity forecasting and mathematical models to simulate the temporal movement of vehicles within the parking facility to determine the proportion of reserved versus regular parking spaces. This temporal analysis improves reservation predictions and yields an average occupancy rate of approximately 0.032, with 15 successful reservations recorded over a 24-hour period.

Nowadays, traffic congestion is increasingly aggravating in almost every urban area and existing traffic light controllers cannot satisfy the rising demands efficiently to handle the traffic pressure. An intelligent transportation system (ITS) aims to provide major innovations for improving the performance of a public transport system including the traffic light control systems. In this paper, we propose a fog-based distributed architecture for a dynamic traffic light control system for ITS. In the proposed architecture, the local gateway collects real-time local traffic data using a wireless sensor network at each intersection. It also gets the neighboring traffic data from the distributed fog nodes. Using this local and global traffic data, the proposed Efficient Dynamic Traffic Light Control algorithm for Multiple (EDTLCM) intersections calculates the optimal green light sequence and duration (that maximizes the benefits), considering three objectives i) reducing the average waiting time ii) minimizing the fuel consumption and iii) maximizing the throughput. The simulation result shows that the proposed EDTLCM minimizes the waiting time, reduces fuel consumption and improves system throughput compared to the other dynamic strategies.