Vacant Parking Spaces Research Articles

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.

Deep learning and saliency-based parking IoT classification under different weather conditions

The world’s population is growing at an exponential rate, which has created a slew of new issues in our daily lives. Among these challenges is the difficulty of finding available parking spaces. To overcome this obstacle, an effective system for detecting vacant parking spaces is crucial, particularly in densely populated areas. Even though there are numerous parking detecting systems on the market today, they all have their own set of restrictions. There are primarily two types of parking systems available. The first is a hardware-based system that uses hardware to detect whether a space is vacant or occupied, and the second is a smart parking system that uses cameras to classify parking status. Vision-based parking systems are becoming increasingly popular due to benefits such as flexibility and cost efficiency. However, due to challenging weather conditions and various occlusions caused by objects, such systems are challenging to develop. The limitations of a vision-based parking system must be addressed for it to become more accurate. To date, many researchers have attempted to incorporate deep learning-based parking systems with various algorithms but none of them has focused on reducing the complexity of Convolutional Neural Network (CNN) training through unsupervised pre-processing steps. In this study, an attempt is made to reduce the complexity of CNN by integrating salient features as a pre-processing step for normalizing weather conditions. It has been observed that salient features-based pre-processing makes the system more robust in terms of accuracy and time efficiency.

Smart Parking Slot Management System Using IOT

The paper proposes an innovative approach to tackle the pressing issue of efficient parking space management amidst the backdrop of rapid urbanization and escalating vehicle ownership rates. Through the amalgamation of Internet of Things (IoT) technology and ultrasonic sensors, the Smart Parking Slot Management System (SPSMS) emerges as a promising solution. Within this system, IoT-enabled devices, particularly ultrasonic sensors, are strategically deployed within parking slots to constantly monitor their occupancy status in real-time. These sensors function by detecting the presence or absence of vehicles through precise measurements of the distance between the sensor and the ground surface. Subsequently, the collected data is seamlessly transmitted wirelessly to a centralized server or cloud-based platform via internet connectivity. Upon reception of sensor data, the central server undertakes robust processing and analysis to ascertain the availability of parking slots within the designated area. End-users can conveniently access this pertinent information through a dedicated mobile application or a web interface, facilitating effortless identification and reservation of vacant parking spaces. The system further enhances user experience by incorporating automated notifications, alerting users to nearby available parking slots, alongside real-time updates on parking availability and robust reservation management functionalities. Keywords: Internet of Things, Thing Speak, Arduino Uno, ultrasonic sensor, Node MCU.

Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning

Short-term prediction of on-street parking occupancy is essential to the ITS system, which can guide drivers in finding vacant parking spaces. And the spatial dependencies and exogenous dependencies need to be considered simultaneously, which makes short-term prediction of on-street parking occupancy challenging. Therefore, this paper proposes a deep learning model for predicting block-level parking occupancy. First, the importance of multiple points of interest (POI) in different buffers is sorted by Boruta, used for feature selection. The results show that different types of POI data should consider different buffer radii. Then based on the real on-street parking data, long short-term memory (LSTM) that can address the time dependencies is applied to predict the parking occupancy. The results demonstrate that LSTM considering POI data after Boruta selection (LSTM (+BORUTA)) outperforms other baseline methods, including LSTM, with an average testing MAPE of 11.78%. The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance, which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM. When there are more restaurants on both sides of the street, the prediction performance of LSTM (+BORUTA) is significantly better than that of LSTM.

Integrating YOLOv8 with Fuzzy Logic System: A Novel Hybrid Approach for Efficient Parking Slot Availability Management

In urban areas, the challenge of finding parking spaces has become a significant concern, leading to congestion, wasted time, and increased pollution. Traditional parking management systems often lack the efficiency needed to address this issue adequately. In this paper, a novel approach was proposed to smart parking management that integrates the YOLOv8 model and fuzzy logic system. YOLOv8, a state-of-the-art object detection algorithm, enables real-time detection and tracking of vehicles within parking lots. By utilizing YOLOv8, the system can accurately identify vacant parking spaces in a timely manner. Furthermore, fuzzy logic was employed to enhance decision-making in selecting the optimal parking slot for users. Fuzzy logic enables the system to consider various factors such as proximity to the destination, parking space size, and distance from the entrance. By incorporating fuzzy logic into the decision-making process, the system can provide personalized recommendations tailored to individual user preferences and parking requirements. Based on the fuzzy inputs namely distance from entrance, proximity to exit and space, the system analyzes the best parking slot and assigns “rank” which is also fuzzy output. The decision can be made based on the rank provided to every slot of the parking lot. Slots with the highest rank should be preferred for parking because they are more suitable as they provide ease to parking the cars and can be suitable to customized needs of the users. The model can be deployed with LCD screens at various parking lots in order to save fuel and time of the commuters.

Machine Learning Models in the large-scale prediction of parking space availability for sustainable cities

The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion has revealed that more than 70% of it can be attributed to prolonged searches for parking spaces. Consequently, accurate prediction of parking space availability in advance can play a vital role in assisting drivers to find vacant parking spaces quickly. Such solutions hold the potential to reduce traffic congestion and mitigate its detrimental impacts on the environment, economy, and public health. Machine learning algorithms have emerged as promising approaches for predicting parking space availability. However, comparative studies on those machine learning models to evaluate the best suited for a large-scale prediction and within a given prediction time period are missing.In this study, we compared nine machine learning algorithms to assess their efficiency in predicting long-term, large-scale parking space availability. Our comparison was based on two approaches: using on-street parking data alone and 2) incorporating data from external sources (such as weather data). We used automatic machine learning models to compare the performance of different algorithms according to the prediction efficiency and execution time. Our results indicated that the automated machine learning models implemented were well fitted to our data. Notably, the Extra Tree and Random Forest algorithms demonstrated the highest efficiency among the models tested. Moreover, we observed that the Random Forest algorithm exhibited less computational demand than the Extra Tree algorithm, making it particularly advantageous in terms of execution time. Therefore, this work suggests that the Random Forest algorithm is the most suitable machine learning model in terms of efficiency and execution time for accurately predicting large-scale, long-term parking space availability.

Visual Parking Occupancy Detection Using Extended Contextual Image Information via a Multi-Branch Output ConvNeXt Network.

Along with society's development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver's health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches.

Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model

For cities, the problem of “difficult parking and chaotic parking” increases carbon emissions and reduces quality of life. Accurately and efficiently predicting the availability of vacant parking spaces (VPSs) can help motorists reduce the time spent looking for a parking space and reduce greenhouse gas pollution. This paper proposes a deep learning model called DWT-ConvGRU-BRC to predict the future availability of VPSs in multiple parking lots. The model first uses a discrete wavelet transform (DWT) to denoise the historical parking data and then extracts the temporal correlation of the parking lots themselves and the spatial correlation between different parking lots using a convolutional gated recurrent unit network (ConvGRU) while using a BN-ReLU-Conv (1 × 1) module to further improve the propagation and reuse of features in the prediction process. In addition, the model uses availability, temperature, humidity, wind speed, weekdays, and weekends as inputs to improve the accuracy of the forecasts. The model performance is evaluated through a case study of 11 parking lots in Santa Monica. The DWT-ConvGRU-BRC model outperforms the LSTM and GRU baseline methods, with an average testing MAPE of 2.12% when predicting multiple parking lot occupancies over the subsequent 60 min.

Parking management system

This article presents a parking management system that detects occupied parking spaces and reports the number of free spaces. The system indicates the numbers of free parking spaces by means of information digital boards and a web system that provides information in real time. A system is presented through an example prototype, using an Arduino microcontroller and ultrasonic distance sensors that detect occupied and vacant parking spaces.

Automatic Car Parking System

This device is capable of automatically locating the open, vacant parking spaces. In an automated automobile parking, if the slot is unoccupied, new cars are permitted to enter; otherwise, if the system cannot find an empty slot, the entry is blocked with a servo barrier.

Parking Lot Allocation Using Rematching and Dynamic Parking Fee Design

In urban areas, traffic congestion is caused by vehicles slowing down as drivers search for vacant parking spaces. In this study, we propose a novel smart-parking system that allocates parking lots based on the matching theory, considering both the drivers’ and parking managers’ preferences. Some drivers may be unable to be allocated to parking lots because of the high traffic volume in urban areas that causes many parking lots to be full. Therefore, we propose a prior reservation system that can reallocate parking lots considering the waiting time until vacant parking spaces appear. The driver’s preference is redetermined considering the waiting time obtained by both the parked drivers’ and reallocated drivers’ information. We also present a parking lot allocation algorithm, including rematching, and discuss stability and strategy-proofness, which are important properties of matching. Furthermore, we propose a dynamic parking fee design, considering different parking statuses (“full,” “congested,” and “available”). By combining the “price increase” with the “price decrease” based on parking status, the proposed dynamic parking fee aims not only to level out parking utilization but also to secure the parking managers’ profit. Finally, numerical simulations confirm the effectiveness of the proposed method using rematching and dynamic parking fee design.

Drivers' Subjective Assessment of the Ease of Finding a Vacant Parking Space in an Area Equipped with Vehicle Detection Devices.

The growing traffic on city streets leads to traffic disruptions, lowering the level of road safety, as well as the problem of finding a vacant parking space. Drivers looking for a vacant parking space on the street generate so-called search traffic. Paid parking zones are introduced to increase the availability of parking spaces for more drivers in many cities around the world. The development in the technology and information sector has contributed to the development of systems guiding drivers to vacant parking spaces. This article aims to analyze drivers’ subjective assessment of the ease of finding a vacant parking space in an area equipped with vehicle detection devices. Data from the Municipal Roads Authority in Gliwice (Poland) were obtained for the study, covering the use of parking spaces in the paid parking zone covered by dynamic parking information. Moreover, a survey was conducted among users of the paid parking zone in Gliwice. The answers of the respondents were used to build a logit model that allows determining the probability of a driver’s positive subjective assessment of the ease of finding a vacant parking space in an area equipped with vehicle detection devices. The results from the model allow the characterization of drivers who positively assess the ease of finding a vacant parking space in the area equipped with vehicle detection devices. In addition, it is possible to reach a group of drivers who negatively assessed the ease of finding a vacant parking space to learn about the factors that may cause them to change their assessment to a positive one. The research results allow city authorities to better manage parking spaces equipped with vehicle detection devices in the paid parking zone. This may change the negative assessment of the ease of finding a vacant parking space into a positive one.

Microcontroller Based Parking Lot Monitoring System Prototype

Parking area is one of the facilities that must be available in specific or public places such as parks, recreation areas, malls, offices, business areas and agencies, and other places. Currently, the existing parking facilities generally do not provide information on the presence or absence of vacant parking spaces. This often causes problems in finding parking spaces, where vehicle users, especially four-wheeled vehicles (cars) will look for empty parking spaces by walking around so they can spend a lot of time. This study aims to create a parking lot monitoring system that is equipped with a microcontroller-based monitoring system so that vehicle users can find out how many parking spaces are available and where the parking lot is located. This parking monitoring system was developed using the prototyping method so that determining the need for the actual system is easier to realize. The test results of this device show that this prototype can provide information regarding the number of vacant parking lots and their locations. The impact of this research is that it can eliminate the accumulation of vehicles in front of the parking lot entrance, because vehicle users can easily and quickly find a parking location.

Improved Optimal Path Finding Algorithm of Multistorey Car Park Based on MCP Protocol

To address the urgent issue of car owners wasting a lot of time because they cannot find empty parking spaces in the largely multistorey car park, the paper conceives a solution including algorithm ParkIG. This scheme uses the infrared photoelectric switch to monitor the parking space status. At the same time, we define a special communication protocol named MCP to analyze the parking space data when analyzing the data transmitted by the photoelectric switch. Algorithm ParkIG is used in finding the nearest empty parking space and optimal path planning. Based on the MCP protocol and navigation system, the algorithm ParkIG initially identifies the layer of multistorey where the car is positioned. Our algorithm ParkIG greatly reduces the time and space size of the search domain and update domain when searching for vacant parking spaces in optimal path planning and ends the algorithm running to avoid a lot of computations that are not essential when the algorithm found an optimal path from the entrance to empty of parking space. Simulation experiments show that the performance of our scheme has been significantly improved, such as the time that it takes for the car owner to search for a vacant parking space is decreased by about 25.8% and reduced the space size of search domain and update domain by 70% compared to the algorithm Dijkstra.

Shared parking for ride-sourcing platforms to reduce cruising traffic

Ride-sourcing platforms create cruising traffic that exacerbates road congestion in large cities. This paper considers shared parking services for ride-sourcing vehicles to reduce the cruising traffic. We propose a novel business model that integrates shared parking services into the ride-sourcing platform. In the proposed business model, the platform (a) provides app-based interfaces to connect passengers, drivers and garage operators (private or commercial garages); (b) matches passengers to ride-sourcing vehicles for ride services; (c) matches idle vehicles to vacant parking spaces to reduce cruising traffic; and (d) determines the ride fare, driver payment, and parking rates to maximize its profit. We model the integrated platform as a three-sided market (i.e., passenger, driver and garage) that involves two matching processes: passenger-driver matching and driver-garage matching. An economic equilibrium model is proposed to characterize the incentives of passengers, drivers, garage operators, and the ride-sourcing platform. Optimal pricing strategies and the corresponding market outcomes are derived by solving the platform’s profit maximization problem. Based on the model, we show that integrated parking and ride-sourcing service can offer a Pareto improvement: in certain regime, it leads to higher passenger surplus, higher driver surplus, higher garage surplus, higher platform profit, and reduced cruising traffic. Further, we investigate the impacts of matching efficiency between drivers and garages on the proposed business model. We show that a higher matching efficiency always leads to increased platform profit and driver surplus, but it may not necessarily benefit passengers or garage operators in all cases. The above insights are validated through realistic numerical studies for San Francisco.

Design A Smart Reservation for Parking System

Nowadays, the smartphone device has become the most used device for the convenience of the user, smart parking is one such application that helps the consumer to find car parking space in an urban area. Mosul University, in particular, is one of these places. Common problems are the lack of information about vacant parking spaces and there is no way to search for them online. The goal of this work is to produce an Android and iOS app that uses ultrasonic sensors connected to the Arduino MEGA 2560 microcontroller to send parking occupancy values ​​to cloud, in an online database executed using Google Firebase. Finally, this application can book and pay online.

The position and role on patent citation network of the parking lot guidance system

ABSTRACT During the peak period of urban traffic, about one-third of vehicles are looking for parking spaces, resulting in air and noise pollution, and even traffic accidents due to emotional stress of drivers; one of the reasons is the lack of guidance information for vacant parking spaces, which leads to inefficient parking behavior. The “Guide System” of smart parking can guide drivers to quickly enter the parking space to improve parking efficiency, which can greatly reduce urban air and noise pollution, and even reduce traffic accidents and casualties. This study discusses the technological development of smart parking “navigation system”, through the social network analysis formed by patent citation, and then analyzes and discusses the technology status and role of individual companies in the industry. This research uses two analysis indicators of technology overlap and network centrality and uses patent citation networks to analyze the technology status of individual companies. Technology overlap is composed of technology knowledge status (TKS) and technology knowledge reliability (TKR), and social network centrality. This research uses the navigation system of smart parking to explore whether this model can locate the technology role and location of individual companies. Companies with special industries/equipment/unique technologies are scattered in special fields.

Community based parking: Finding and predicting available parking spaces based on the Internet of Things and crowdsensing

In smart parking guidance systems, the ability to estimate the availability of vacant parking spaces is important to make effective guidance. In this paper, we propose a general architecture for building crowdsensing-based parking guiding system, in which the occupancy state of parking lots can be detected by smart vehicles equipped with sensors and wireless communication devices, or by parking meters and parking fee-paying terminals, and the state information can be used to estimate the probability of finding available spaces for incoming smart vehicles. Five representative scenarios that can be used in such a framework were investigated. The problem to estimate the availability of parking spaces in each scenario was modeled as an M/M/c/c queuing problem with closed-form analytical solutions. The scenarios were validated in a simulation platform and their performance in various parking environments was investigated. Experimental results revealed that the crowdsensing-based parking prediction method can lead to 30.91% or more relative improvement on average estimation error than steady-state prediction in typical parking environments.

Car Parking Space Detection using Digital Image Processing

: Nowadays, people are facing problems finding parking spaces available in a parking lot because of the massive rise in the occupancy of cars and the increase in urbanization. We have embedded techniques of image processing in each phase of the method. It will benefit all drivers entering a parking lot from the information given by the system about the location of parking spaces available and the number of vacant parking spaces.

Detection Of Vacant Parking Spaces Through The Use Of Convolutional Neural Network

This paper focuses on the application of computer vision and convolutional neural network techniques in the automotive industry to reduce the amount of time required to locate a vacant parking spot and to reduce driving time. The main motivation for a vacant parking spot detector is such that today’s drivers are facing major difficulties in finding available spots in largely populated cities. This often time leads to increased congestion and frustration for the driver because they are forced to continue their search for a parking spot. Our approach is able to solve this issue and provide the driver with useful information through the use of transfer learning methodologies. The main contribution of this paper is to examine and improve on previously implemented transfer learning methods in order to better increase the detection accuracy. This paper differs from previous attempts such that it considers all environmental factors such as weather and time of day. Other models are not able to handle these conditions with a high accuracy and subsequently falter. When compared to previous attempts, our implementation focuses solely on the reliance of transfer learning. The results indicate that our model is capable of identifying vacant parking spaces under all conditions with competitive accuracies. The proposed model is able to surpass the accuracy of the latest attempt at solving this issue.