Human as a Service: Towards Resilient Parking Search System With Sensorless Sensing

The goal of smart cities and sustainable transport is to ensure the efficient movement of people while minimizing a negative impact on the environment. Many cities around the world conduct a policy aimed at limiting parking spaces; however, the complete elimination of parking spaces in cities currently does not seem possible. Parking vehicles cause disturbances in road traffic by searching for a parking space and performing the parking maneuver. This article analyzes the impact of the parking maneuver on the capacity of the inlets of intersections with traffic lights, and the significance of the time it takes to enter and exit a parking space on road traffic disturbance areas under Polish conditions. The research is carried out in on-street parking, characterized by different positions of the parking space to the road, the different surfaces of parking spaces, and the geometry of the road next to which the parking spaces are located. Differences in the time of entry to and exit from the parking space between the research areas indicate that different characteristics of the parking spaces affect the time of the parking maneuver. Drivers wait for the acceptable distance between vehicles on the road into which the vehicle can exit from the parking space. Drivers exiting from perpendicular parking spaces more often cause traffic disruptions than in the case of parallel parking spaces, due to limited visibility. The occupancy of parking spaces directly next to the analyzed ones also affects the time of entry to and exit from the parking space. However, between the time of entry to or exit from the parking space, and the use of the parking space, there is no relationship. This finding indicates that more factors determine the time of entry to and exit from the parking space. The results presented in the article show the need to conduct further research on the impact of parking maneuvers on the capacity of intersections with traffic lights for road traffic conditions in Poland. The results of the research will allow for the design and construction of an optimal parking infrastructure that will meet the needs of road users, while minimizing the negative impact on road conditions and the natural environment following sustainable development.

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.

Nowadays, the parking problem has seriously affected people’s life. Aiming at the causes of parking problems, this paper proposes an intelligent parking system based on big data and the Internet of Things, and adopts a floating parking fee model to realize the reasonable allocation of parking resources. This paper obtains parking space and vehicle information through the deployment of iot terminal infrastructure equipment, adopts Zigbee technology for wireless short-range communication, NB-LoT technology for wide-area communication, transmits the information to the background database, and then makes statistical analysis of the information through Hadoop big data algorithm. The floating parking fee model is constructed according to the road and parking space information, and the real-time parking space price is determined. Big data algorithm is used to screen and recommend parking lot information for users. Based on the impact of distance and price on users, users can decide the choice of parking lot by themselves. The parking reservation system ensures the realization of user selection and facilitates users to obtain parking resources faster. In this process, the reasonable allocation of parking resources in time and space is realized.

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.

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.

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.

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.

Drivers face parking problems in most area, especially during peak hours. The difficulty roots from the availability of parking space at the given time and place. Despite the technologies and protocols used to solve parking problems and have an intelligent city, many vehicles may pursue a small number of parking spaces, which in turn leads to serious traffic congestion. A driver can spend a lot of time to look for an available parking space closed to his destination, which means the increasing of the traffic problem of the city, a very high consumption of fuel and the spectacular impact on the environment. Wireless sensor networks offer an attractive choice for low cost and easy-to-deploy solutions for intelligent traffic guidance systems and parking lot applications. This paper focuses on comparative study of different smart parking techniques developed with the aim of overcoming said problems using various wireless sensor networks and to propose architecture of a multi-service smart parking management system.

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 this modern era, motorcycles are a primary means of transportation used by many people in Indonesian society, including students who use them to access campus. However, the increase in motorcycles also raises safety issues and the availability of parking spaces on campus. The parking system needs to be improved at the Faculty of Engineering, University of Mataram, because it does not use an automatic checking system, resulting in concerns about vehicle safety. This research aims to build a Web-based Motorcycle Parking Information System to store real-time rider data and detect faces and police numbers through CCTV. The Personal Extreme Programming (PXP) method is used as a development method because of its high level of flexibility and its ability to adapt to changing needs. This system was developed using the Bootstrap framework for the front end and Laravel for the back end, with MySQL Workbench as a server to manage the database. System testing uses the Black Box Testing method to ensure each unit or module functions according to predetermined needs. The test results show that all features function validly and by the expected specifications. This research provides an effective solution for improving parking security and parking space management on campus, thus providing a sense of comfort and security for students and all parking users at the Faculty of Engineering, University of Mataram.

On-street parking is an important component of urban parking system. It is an efficient means for providing more parking spaces and balancing the parking demand and supply. However, it is easy to decrease traffic performance and increase the risk of traffic accident. Aiming at evaluate the influence of on-street parking on characteristics of vehicular flow, a cellular automata model is developed for describing the interaction between the on-street parking maneuvers and vehicular flow in the one-way street with on-street parking spaces. The model takes different driving manures into account according to the dynamic features of different vehicles. Numerical simulation is performed using an extensive Monte Carlo procedure. The results show that the driving maneuvers of on-street parking can antedate traffic state transition from free flow to congested flow at low density. The maximum volume and travel speed are also influenced significantly. The finding in this paper has important ramifications for policy in the area of parking management and planning.

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.

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.

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.