Improving Campus Security with Web-Based Motorcycle Parking Information System using Personal Extreme Programming Method

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.

There has been a considerable growth in the volume of road traffic in India. As a result, the demand for parking spaces has increased. Due to less availability of parking spaces, commuters spend many hours on average searching for one. Improper management of the same has lead to overcrowding of roads with vehicles, causing air pollution as well. Thus, to ease the process of locating vacant parking spaces and making this information available for commuters, this paper proposes an end-to-end parking system. The system employs a combined approach, referred to as the Hybrid Model, using Mask R-CNN and YOLOv3 to determine the occupancy status of parking slots and update it in real-time for users to view. A new metric, MDR, was devised to evaluate the car detection performance of Mask R-CNN, YOLOv3, and the Hybrid Model.

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 current parking system still uses a manual parking system, vehicle owners still have difficulty finding an empty parking space because they have to go around the parking area so it is less efficient and takes a long time. This study aims to help car drivers save time in finding parking locations and knowing the availability of parking spaces. In this study, there are two types of input used: infrared sensors, which function to measure the capacity of the parking space, and RFID sensors, which are used to detect cards. The data processing process is carried out using Arduino Uno, while the LCD is used to display the number of available parking slots and the location of the slot. In addition, the servo motor functions to open the barrier. This system operates in a way, when a car enters and attaches a registered card, the servo motor will open, and the number of parking slots will decrease. The driver then selects a parking slot, which will be automatically marked as "full" on the LCD screen. When the infrared sensor detects a car that wants to exit, the servo motor will open again, and the number of parking slots on the LCD will increase.

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.

The advancement of information technology has driven digitalization in various sectors, including futsal court booking systems. Manual booking processes are often inefficient and time-consuming. Therefore, a web-based information system is needed to facilitate users in booking courts quickly and conveniently. This study aims to develop a futsal court booking service information system using the Extreme Programming (XP) method. XP was chosen for its ability to accommodate changing requirements flexibly and rapidly through short iteration cycles and a collaborative approach with users. The system includes key features such as user registration, court booking, online payment, and notifications. The development results show that the XP method is effective in ensuring system quality by responding to user feedback in real time and producing a system that is easy to use and maintain. With the implementation of this system, users can efficiently book futsal courts, enhancing their playing experience.

Tambun Bungai College of Law (STIH-TB) has quite a lot of students but the academic process is still done manually. This manual process makes it difficult to present information if it is needed at any time and can be hampered if an error occurs in the required data so that it will result in wasted time. Based on these problems, a website-based Academic Information System (SIAKAD) is needed so that the Tambun Bungai School of Law (STIH-TB) can facilitate the process of academic activities so that they become more effective and efficient. The method used in this research is the Extreme Programming method. There are four stages that must be done in the extreme programming (XP) method, namely planning for problem identification, design for system modeling, coding is the implementation stage or program code creation, and testing to find out what errors arise when the website is running. This research produces an academic information system website that is useful for the Tambun Bungai law school so that it can help the academic process.

The purpose of this research is to develop a website-based system to facilitate the academic administration of student affairs at the Faculty of Information Communication Technology, Universitas Nasional in providing various student information ranging from information on association activities, scholarships, competitions, achievements achieved by FTKI (SI-TI) students. and helping FTKI students and graduates to find updated information about job vacancies related to information technology. This study will develop an administrative information system for student affairs with the Extreme Programming method, an Agile Development Method. will include FTKI students and graduates as users. The results of research from the Student and Alumni Administration Information System (Smart Adma) can provide time efficiency in administrative management, make it easier for students to get information from association activities, proposals, requests for funds to submission forms and internship assessments, as well as alternative media during the Covid 19 Pandemic.

The development of private vehicle ownership is very rapid, but it is not balanced by adequate supporting infrastructure such as roads and parking lots. Especially for parking lots, parking horizontally will be difficult to develop because of the limited land availability. The addition of parking lots can be done vertically by building a multi-storey parking building. So far, the existence of multi-storey parking buildings has not provided an information system on the availability of parking spaces. The form of multi-storey parking lots often makes the driver have to go around few times to find an empty space. This situations wasting of energy and time. This study aims to design an information system as application models in human-computer interaction, that will inform someone there is empty or already filled parking space, so that it can save time and make it easier for drivers to find parking spaces to park their cars. This information system prototype design consists of three main parts, called sensors, controllers, and displays. Car parking control system that works with an automatic portal (parking gate) along with a display screen for information on the location of empty parking spaces and those that have been filled. The portal will open if information states the parking lot is still available, and the portal will not open if information states the parking lot is full.

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.

Ø With the ever-growing urbanization and the increasing number of vehicles on the road, efficient management of parking spaces has become a critical challenge in modern cities. In this project, we propose a smart car parking system based on Arduino micro controllers to address this issue. The system utilizes ultrasonic sensors to detect the presence of vehicles in parking spaces and employs RFID technology for vehicle identification. Ø The smart parking system offers several innovative features to enhance user experience and optimize parking space utilization. Users can conveniently locate available parking spaces through a mobile application or digital displays placed at strategic locations. The system also integrates with existing payment systems, allowing for seamless payment transactions and automated access control. Ø One of the key advantages of the proposed system is its scalability and flexibility. It can be easily deployed in various parking environments, including on-street parking, parking garages, and commercial parking lots. Furthermore, the use of open-source Arduino platforms ensures affordability and accessibility, making it suitable for implementation in both developed and developing regions. Ø In addition to improving parking efficiency and reducing traffic congestion, the smart parking system contributes to sustainability efforts by minimizing carbon emissions associated with vehicle circulation in search of parking spaces. It also provides valuable data insights for urban planners and policymakers to optimize parking policies and infrastructure planning. Ø Through this project, we demonstrate the feasibility and effectiveness of using Arduino-based technology to develop innovative solutions for urban parking management. The smart car parking system presented here serves as a practical demonstration of how IoT and embedded systems can contribute to creating smarter and more sustainable cities.

In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the next 15 minutes based on the previous one-hour parking occupancy. The proposed models were trained on the historical data of Kuala Lumpur Convention Center dataset and evaluated against baseline ARIMA models. The results have shown that our proposed MLP model performed relatively well against baseline model with the root mean square error between (RMSE) 78.25 to 78.41 and mean absolute error (MAE) between 37.02 to 39.17.

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.

The high demand for ubiquitous availability of reliable parking spaces in cities faces challenges on timely information sharing and low-cost infrastructure deployment. In this paper, we propose a mobile crowdsensing system, namely ParkHop, to aggregate on-street and roadside parking space information through sensorless sensing, and disseminate this information to urban drivers in a resilient manner. ParkHop targets special social groups that have stable work routines to serve as crowd workers. We propose a crowdsensing algorithm employing a joint estimator to process crowdsensed data, and evaluate the reliability of crowd workers based on the veracity of their answers to a series of control questions. In addition, the specific worker selection method to speed up the crowdsensing process and incentive scheme to achieve fair reward distribution have been carefully designed in ParkHop. Our system disseminates the availability of parking spaces and their up-to-date price information to drivers with on-demand needs via a publish-subscribe messaging pattern. The efficacy of ParkHop for aggregation and dissemination of parking space information has been evaluated in both real-world tests and simulations. Our results show the system is robust and agile enough to cope with different crowdsensing scenarios.

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.