Parking Space Availability Research Articles

Combining deep learning methods and rule-based systems for automatic parking space detection

This paper presents an Automatic Parking Space Detection (APSD) algorithm designed to reduce traffic in cities while offering an information system of available parking zones. The main aim of such a system lies in its ability to identify parking spaces in a distributed manner, achieved by installing multiple APSD systems across a fleet of vehicles. This fleet, during its regular operations, communicates the availability of parking spaces to a centralized information system. Our methodology employs a rule-based system that seamlessly integrates a variety of neural networks for different specific tasks. These tasks include depth estimation, road segmentation, and vehicle detection. This approach would fall into a modular category instead of an end-to-end solution, using the Málaga Urban Dataset in the experiments. We present a preliminary experiment for parameter settings and an ablation study to quantify each subsystem contribution to the results. The proposed system achieves a parking space detection F1 score of 0.726.

An Analysis of User Perceptions Towards Public Parking Management in Kampala City

The parking problems in Kampala City have become more pronounced due to the increasing vehicle numbers resulting from a growth in population and income levels. Understanding public perceptions and the various stakeholders’ parking needs and preferences is fundamental for identification of priority areas for improvement. This paper assessed the user perceptions of public parking management and the parking needs and preferences for various stakeholders in Kampala City. The study was evaluative and employed a cross sectional research design with a range of data diverse triangulation quantitative and qualitative techniques. From 328 questionnaires, participants were asked to assess fifteen (15) public parking management attributes from a point of view of their satisfaction. Also, a public parking stakeholder analysis was done and the level of influence by each stakeholder category in regards to parking management in the city and their parking needs and preferences were established. The study results revealed- that the customer satisfaction Index (CSI) of parkers was 52.4% indicating a relatively lower satisfaction level of the parking management in Kampala. From the fifteen public parking management attributes, four (4) of them to include affordability of parking, availability of parking spaces, accessibility to parking spaces and as well Safety and security were accorded the greatest importance. While thirteen (13) public parking stakeholders in Kampala City that included Ministry of Works and Transport (MoWT), Kampala Capital City Authority (KCCA), Multiplex Limited, Private Parking suppliers, public transport operators, Business Community, Commuters, Private Car Drivers, Logistics Vehicles, Traffic Police, Residents, shoppers and tourists and the academia were identified. The study recommends prioritization of those factors that parkers deem very important by the relevant authorities, stakeholder involvement in parking planning and management, establishment of an efficient enforcement and control mechanism, having a public parking vision with clear goals and objectives and as well the use of technology to enhance efficiency for both parkers and parking suppliers.

"Smart Parking Systems: Enhancing Efficiency and Sustainability in Urban Areas"

Abstract: Smart Parking systems are innovative solutions designed to address the challenges faced by drivers when searching for parking spaces, especially in busy urban areas during peak hours. These systems utilize IoT technology deployed on-site to monitor and indicate the availability status of individual parking spaces. Additionally, a mobile application is provided to enable users to conveniently check the availability of parking spaces and easily locate vacant slots. By implementing a Smart Parking system, drivers can save valuable time and avoid the frustrations associated with the daunting task of finding suitable parking spots. Moreover, these systems play a crucial role in alleviating traffic congestion and reducing air pollution in densely populated urban areas.

Smart Car Parking System

In this fast-growing economy, the number of vehicle users is growing tremendously, wasting a significant amount of time and fuel while searching for an available parking space. This work highlights the optimum utilization of parking slots without the physical intervention of humans by making use of an IoT-integrated embedded system. This study aimed to create a mobile application for car parking that uses NodeMCU as the Microcontroller, which is further integrated with Radio-Frequency Identification (RFID) and the Internet of Things (IoT) to detect available parking slots, saving people’s time. When combined with an interactive mobile application, this technology provides an efficient solution by highlighting the availability and occupancy of parking spaces as green and red, respectively. It contributes to enhancing the quality of life of people living in congested metropolitan cities by digitizing the conventional parking system.

Management Strategies for On-street Parking Facility at Gen. Sudirman Street Kuanino Kupang

Poor performance of on-street parking facilities at Gen. Sudirman Kuanino Street, Kupang, can disrupt traffic flow and cause various problems. The research method is to evaluate the performance of on-street parking facilities and their impact on road performance, transportation costs, and the environment. The results show that the parking index exceeds the parking space capacity, with the highest values of 150% (left) and 138% (right). This is due to the parking volume of building material store operational vehicles (pick-ups and trucks). The resulting impact is a decrease in road performance with a degree of saturation (DS) at 1.22. Hence, it affects transportation costs and vehicle exhaust emissions exceeding regulatory limits. The recommended structuring strategy is the application of rules to control the volume, space, and time of parking by prohibiting parking from building material store operational vehicles, requiring minimum availability of off-street parking spaces by every store, applying a progressive parking fee system, and planting green plants. If the structuring strategy is implemented, the highest parking index results are 84% (left) and 96% (right), and the road performance results increase from the DS value of 1.22 to 0.71.

Economic Analysis of On-Street Parking with Urban Delivery

The surge in online shopping has dramatically increased the demand for short-term curb access for package pickups and deliveries, leading to heightened competition for limited curb space. This paper addresses the problem of how the unique parking demand of deliverers, particularly their parking duration for delivery attempts linked to parking space availability, affects the dynamics of urban curb parking systems. We develop continuum models of a curb parking system and perform analytical analyses to understand the dynamics and steady-state properties of the system under the influence of increased urban deliveries. We conduct comparative statics to examine how various curb management measures, such as pricing, parking duration caps, and dedicated delivery bays, influence the equilibrium conditions, followed by comparisons of the theoretical capacity of these measures. We further demonstrate the working mechanism of delivery bays and their role in forestalling specific failures within a hybrid system with both general parkers and deliverers. Finally, we investigate curb management strategies in nonstationary operational contexts and prescribe the optimal strategies therein. Our findings offer valuable insights into the unique properties that deliverers introduce to curb parking dynamics, highlighting the need for a strategic reevaluation of current management practices. We find that pricing strategies for metered parking to general parkers prove to be more efficient and flexible compared with other interventions. Notably, our analysis suggests that when curb parking pricing is optimally calibrated, the necessity for dedicated delivery bays diminishes. Furthermore, we reveal that optimal curb management strategies could diverge in response to surges in demand, depending on whether the increase sources from general parkers or deliverers. To be effective, the sizing of delivery bays must align with the underlying causes of parking scarcity. History: This paper has been accepted for the Transportation Science Special Section on ISTTT25 Conference. Funding: This work was supported by the George Washington University [University Facilitating Fund]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0569 .

Energy Aware Smart Car Parking System Based Internet of Spatial Things

With the advent of smart and green cities, the development of energy-efficient smart parking systems has attracted researchers to reduce environmental pollution in smart cities due to reducing traffic congestion as well as waste of time and fuel consumption. This article investigates how to integrate the Internet of Spatial Things (IoST) with workload balancing and image processing in fog computing to build an energy-efficient smart parking system. The suggested system applies Q-Learning, a reinforcement learning method, to achieve workload balancing and then deploys image processing to detect vacant parking slots. The proposed method’s main objective of load balancing is to reduce energy consumption. The evaluation of the proposed system is done by comparing two case studies, fog-based and cloud-based IoST implementations, in the iFogSim simulator for various scenarios and scales. Moreover, it evaluates and compares the energy consumption of various devices. Experimental findings show that the devised system in fog-based IoST greatly reduces energy consumption with improves parking space availability in smart parking in contrast to the cloud-based deployment of smart car parking.

Prediction of Parking Space Availability Using Improved MAT-LSTM Network

The prediction of parking space availability plays a crucial role in information systems providing parking guidance. However, controversy persists regarding the efficiency and accuracy of mainstream time series prediction methods, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, a comparison was made between a temporal convolutional network (TCN) based on CNNs and a long short-term memory (LSTM) network based on RNNs to determine an appropriate baseline for predicting parking space availability. Subsequently, a multi-head attention (MAT) mechanism was incorporated into an LSTM network, attempting to improve its accuracy. Experiments were conducted on three real and two synthetic datasets. The results indicated that the TCN achieved the fastest convergence, whereas the MAT-LSTM method provided the highest average accuracy, namely 0.0330 and 1.102 × 10−6, on the real and synthetic datasets, respectively. Furthermore, the improved MAT-LSTM model accomplished an increase of up to 48% in accuracy compared with the classic LSTM model. Consequently, we concluded that RNN-based networks are better suited for predicting long-time series. In particular, the MAT-LSTM method proposed in this study holds higher application value for predicting parking space availability with a higher accuracy.

KAJIAN KEBUTUHAN DAN PENDAPATAN PARKIR PADA KAWASAN OBJEK WISATA PANTAI KELAN KABUPATEN BADUNG BALI

Badung Regency is one of the largest contributors to regional economic income, namely in the world of tourism, especially the South Badung area which is famous for its many beach tourism objects which attract many foreign and local tourists. Availability of parking spaces, of course, must have good parking services in order to optimally utilize the available parking space capacity. This research focuses on planning the need for parking space and parking revenue for two-wheeled vehicles and four-wheeled vehicles in the Kelan Beach tourist area, because parking is one of the supports for tourism so that visitors can feel comfortable and can realize an adequate parking pattern. The planned land area for parking two and four-wheeled vehicles at Kelan Beach is 2097.30 m² in total. The survey method used is the cordon count and patrol method. Characteristics of parking for two-wheeled vehicles include the highest volume, namely 461, accumulation of 141 vehicles, duration of 1.63 hours, capacity of 173, turnover rate of 0.18, index of 0.68 less than (<) 1.00, which means the need for parking is below capacity/sufficient, and four-wheeled characteristics include volume maximum of 163, accumulation of 33 vehicles, duration of 1.48 hours, capacity of 49, turnover rate of 0.23, index of 0.57 less than (<) 1.00 which means parking needs are below capacity and do not need expansion. The parking space requirements for two-wheeled and four-wheeled vehicles are 63 and 20 slots respectively. The parking layout at Kelan Beach tourist destination adopts a 90° angle pattern for both two-wheeled and four-wheeled vehicles, with respective parking space dimensions of 0.75 m x 2.00 m and 2.50 m x 5.00 m. The projected annual parking revenue is estimated at IDR.499,441,667.00, calculated based on fixed rates. Paving blocks are chosen for the parking surface, with a cost of IDR.441,345,000.00.

Parking Occupancy Detection using Computer Vision Techniques

The surge in vehicle numbers on roads contributes significantly to traffic congestion and management challenges, particularly evident in developing nations like India where the influx of cars exceeds road and parking capacity. Addressing these issues necessitates the implementation of sophisticated parking management systems. This project focuses on two key objectives: detecting vehicle occupancy within marked parking slots and analyzing parking data. Using the parking lot near IIT Kanpur main gate as a reference, video data was collected for 14 consecutive days, enabling the evaluation of vehicle occupancy and parking patterns. Object detection algorithms such as Mask-RCNN and YOLO-v5 were employed to identify occupied parking spaces within the lot. Various methods, including HAAR cascade-based classifiers, DNN-based systems utilizing ResNet classifiers, and RCNN with IoU, were tested for detecting vehicles within allotted slots. The data collected was stored in CSV format for analysis. This project aims to provide insights into detecting parking space availability and analyzing parking data to optimize time and fuel efficiency. In the Mask-RCNN approach, pre-occupied spaces are denoted by red boxes, while green boxes represent available parking spots. Similarly, YOLOv5 was utilized to count cars in video frames and identify available parking spaces. The YOLO Annotation Toolbox facilitated the extraction of parking space coordinates from recorded video frames, which were then visualized in QGIS for further analysis.

Robust Parking Space Allocation System Using Open CV and Scikit-learn

The proliferation of urbanisation has led to an increased demand for efficient parking management systems. In response, this project presents a Smart Parking Assistant system aimed at providing real-time space availability notifications to users through their smartphones. Leveraging computer vision techniques implemented via Open CV and machine learning algorithms from the Scikit-learn library, the system captures video feed from a webcam to detect and quantify the number of empty parking spaces in a designated area. Upon receiving a request from the user, the system processes the video feed to analyse the occupancy status of parking slots, utilising advanced image processing techniques to accurately identify empty spaces along with mask image of the parking lot. The model built using Scikit-learn efficiently categorises the available slots, enabling the system to relay the precise number of open spaces to the user's smartphone. Furthermore, the Smart Parking Assistant incorporates geo-spatial functionalities to enhance user experience. By integrating the Haversine distance formula, the system calculates the distances between the user's location and nearby parking areas. This information is then displayed to the user, allowing them to conveniently locate and navigate to the nearest available parking facility. The proposed system offers a comprehensive solution to address the challenges associated with parking management in urban environments. By harnessing the power of computer vision, machine learning, and geo-spatial technologies, it provides users with timely and accurate information regarding parking space availability, ultimately improving efficiency and convenience in urban parking scenarios.

What Lies Behind Fare Evasion in Curb Parking Behavior? Evidence from Nanjing, China

Frequent incidents of parking fare evasion pose ongoing challenges, placing parking management departments under pressure as a result of reduced parking revenues. The causal mechanisms behind parking fare evasion, as identified in existing studies, remain incompletely explained. To address this gap, our study utilized empirical data from Nanjing, China, employing a random forest methodology to elucidate the intricate and non-linear relationships among parking site attributes, weather conditions, and the built environment concerning curb parking fare evasion behavior. Additionally, we used the Shapley Additive Explanations method to clarify the pertinent variables. Our empirical findings emphasize the significant impact of parking site attributes, including departure time, parking space availability, and occupancy, on evasion behavior. Notably, our results underscore the necessity of strategically managing parking near scenic spots and points of interest (POIs) associated with accommodation during weekends. Conversely, on workdays, there is a need for strategic oversight of parking fees in the vicinity of government institutions, scenic spots, and workplace-related POIs. These research outcomes make valuable contributions to the existing literature on curb parking and provide actionable insights for improving urban parking management.

Patient satisfaction with primary health care services and primary health care providers.

To evaluate patients' satisfaction levels with primary healthcare services and providers in Jordan and assess differences in patients' satisfaction in relation to sociodemographic factors and accessibility to primary healthcare. A descriptive cross-sectional design was used in this study. A convenient sampling technique was utilized. A 34-item survey instrument was adopted and distributed to patients in nine primary healthcare centers in Amman in the period between October and December 2022. A total of 225 patients completed the survey. The mean total score for patient satisfaction with primary healthcare services was 25.22 (SD=4.13). There were significant differences in satisfaction with services in terms of educational level, visitation reason, mode of transportation, availability of parking, and suitably designed for patients with disabilities. Furthermore, the mean total score for patient satisfaction with primary healthcare providers was 22.85 (SD=5.86). There were significant differences in relation to visitation reason, mode of transportation, and parking space availability. It is important to improve patient satisfaction in primary healthcare facilities, and the Ministry of Health should implement policies for improving the quality of services provided by primary healthcare.

Research on Strategies to Address Urban Parking Issues in China

In the process of urbanization in China, the scarcity of land resources has led to an increasingly prominent contradiction between the growth of automobile stocks and parking space availability. The escalating number of private vehicles has become a significant contributor to traffic congestion. This paper aims to delve into the issue of urban parking difficulties and explore associated management strategies. Firstly, through a literature analysis approach, a comprehensive examination of domestic and international parking problems is conducted, revealing a severe situation in China that necessitates the formulation of pertinent strategies. Secondly, case studies on different regions are undertaken from the perspectives of current parking situations and management countermeasures, providing valuable insights and references for addressing China's parking challenges. In response to urban parking difficulties, this paper proposes two strategies: first, "source expansion" by increasing the supply of parking spaces, and second, "streamlining" by restricting private vehicle access. To address parking challenges, strategies such as constructing landscape eco-parking lots, intelligent multi-level garages, developing smart parking systems, and implementing tidal parking are suggested. Additionally, shared parking is considered an effective method to reduce the number of privately owned vehicles in urban areas. Furthermore, resolving urban parking issues should prioritize social benefits, advocate environmentally friendly transportation, and encourage more residents to enjoy higher-quality urban travel experiences.

Determinants of office rents: the role of physical and locational characteristics in Colombo

PurposeThis paper examines rent determinants and their relationship with commercial office property rents.Design/methodology/approachThe method adopted in this study differs from that of previous studies on this topic. Firstly, based on the survey of the viewpoints of experts, Relative Importance Index (RII) analysis was used to identify rent determinants and to rank and ensure their relevance and validity in the Sri Lankan context. Secondly, sampling of data related to 115 office properties collected from property tenants and landlords located within the central built-up area of Colombo City was conducted using a multi-methods approach to carry out an objective hedonic analysis of office rents.FindingsThis research utilizes RII and hedonic models to provide insights into determinants and relationships. Both analyses confirm that the three top drivers of commercial office rent are distance from the major town center, availability of parking space and the condition of the property. In addition to these three factors, hedonic models reveal that the age of the property and the availability of a conference hall also play a relevant role in explaining office rents. Given the disparities in the findings of the two methods, further examination was able to confirm that factors such as distance from the major town center, parking availability, age of the property, presence of a conference hall, building condition, floor size, business type and type of building are likely to influence commercial office rent. These findings reflect elements such as the quality, newness and better facilities of different office properties.Practical implicationsThis systematic study and analysis of office rent for the guidance of real estate investors can support sound investment decisions, potentially leading to more financially sound property development, reduced public debt levels and improved public-private financing. Further, the research findings offer valuable insights to real estate investors, developers and planners regarding location decisions for office development quality enhancements in future office developments.Originality/valueThis research provides fresh insights into the local scale office market, an area where limited evidence currently exists. Further, the methodology adopted provides evidence that hedonic analysis, supported by a multi-method approach, can mitigate the subjective judgments made by professionals.

The influence of social geographic changes on the availability of parking spaces: A case study of Novi Sad

The purpose of the paper was to illustrate a case in which the socio-geographical changes of the settlement are reflected on its streets, particularly concerning parking availability. The research aimed to address the increasing scarcity of parking spaces in planned areas of the city. To achieve this, semi-structured, in-depth interviews were conducted with parking space users. The analysis of the responses revealed certain patterns reflecting socio-geographical processes in the population of Novi Sad. The results showed that the lack of parking spaces is attributable to factors such as the growing number of cars, which is a consequence of gentrification, population aging, delayed departure from parental homes, etc. The challenges in finding parking spots adversaly affect the city?s sustainability. The work is significant for two main reasons: firstly, it illustrates phenomena accompanying cities? population and economic development, and secondly, it highlights the necessity, during spatial planning in urban areas, to consider contemporary demographic processes while also anticipating demographic trends. Proposed solutions include enhancing the role of public transportation and promoting alternative modes of transport. However, these solutions come with drawbacks such as time constraints, weather conditions, pricing, availability, and other factors.

A Region-wide Deployment of an Automated Real-time Truck Parking Availability Detection and Information Dissemination System

Commercial heavy vehicle-borne freight will continue to be the dominant cross-region shipping mode for materials and goods for the foreseeable future. Federal hours of service (HOS) regulations mandate scheduled minimum times for of-duty and in-transit rest periods to minimize operator fatigue. Without timely, accurate information disseminated to drivers and carriers on where to park, complying to HOS regulations can often become a very challenging task for drivers. This paper describes a region-wide deployment of a unique non-intrusive, multi-camera, truck parking space detection and availability information dissemination architecture. The architecture leverages multi-view Structure and Motion methods to reconstruct a Three-dimensional representation of the environment for estimating unoccupied spatial extents to deduce truck parking availability. Unlike 2D camera sensor-based methodologies, the advantage is its immediate adaptability to a variety of parking facilities and scenarios without the need for any subsequent re-training. The approach also mitigates errors arising from occlusions, and many other environmental conditions that confound many 2D camera-based approaches. The region-wide deployment, operated by a state transportation agency, has been online since early 2019, and has thus far substantiated the technical and day-to-day operational viability of the approach. The performance throughout this period indicates an overall accuracy at or above 90%. This paper provides a general overview of the deployed system architecture and performance characteristics across varied parking facility designs, parking behaviors, and other environmental conditions.

Predicting Vacant Parking Space Availability Zone-Wisely: A Graph Based Spatio-Temporal Prediction Approach

Intelligent parking guidance systems (IPGSs) based on vacant parking space (VPS) availability predictions are highly effective to alleviate the increasingly serious parking difficulties in metropolis. The spatial-temporal correlation analysis of VPS information of multiple parking lots in a region shows that not only the number of VPS in each parking lot has a stable temporal correlation, but also there is obvious spatial correlation among different parking lots. Given this, this paper takes full advantage of the space-time correlation and makes short-term (within 30 min) and long-term (over 30 min) predictions about the number of VPSs in multiple parking lots in an area parallelly. Specifically, this paper develops a deep gated graph recurrent neural network (G <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> RNN) model which has the ability to extract both spatial and temporal correlations concurrently between different parking lots. The advantages of the G <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> RNN model are that, first, spatial and temporal correlations between different parking lots can be accurately obtained simultaneously, and second, there is no need to grid the raw data since it uses graph-structured data. For long-term predictions, two different approaches, namely the single-step and iterative predictions, are investigated. The performance of the G <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> RNN based prediction method is extensively evaluated with practical data collected from eight public parking lots in Santa Monica. The results show that it can achieve considerably high accuracy in both short-term and long-term predictions.

Analisis Marketing Mix 4P Terhadap Tingkat Kepuasan Pasien Rawat Jalan Poli Bedah Umum Rumah Sakit Umum Daerah Dr. Mohamad Soewandhie

Outpatient satisfaction level takes an important role in hospital success. Outpatient satisfaction levels are determined by various factors, some of them are mentioned in the Marketing Mix 4p strategy that includes place, promotion, price, product. Marketing mix (place) includes strategic location, comfortability and cleanliness of public toilet, comfortability and cleanliness of consultation room, comfortability and cleanliness of waiting room and parking space availability. Marketing mix (promotion) includes any action that the company or hospital had taken to persuade patients to use the hospital services. Marketing mix (price) includes service cost, easy access to conduct transaction, suitability of prices compared to services received, medicine price and thorough explanation for proof of payment. Marketing mix (product) includes informed consent from healthcare workers, services received from nurses, services received from pharmacists, consultation services and availability of medicines. A study was needed to determine the relation between marketing mix strategy and level of outpatient satisfaction the purpose of this study was to determine if marketing mix 4p is related to outpatient satisfaction. This study is a quantitative study with Cross Sectional design and observational analytics design. The methods that were used to analyze in this study is statistical analysis. The data used in this study was primary taken by asking outpatient to fill in questionnaires. The study’s sample were 106 people who were chosen using the systematic random sampling technique. The study was conducted at the general surgery clinic at dr. Mohamad Soewandhie regional hospital starting from 20th of September 2023 to 31st October 2023. In this study, marketing mix 4p strategy was divided into 4 variables (place, promotion, price, product). Spearman Correlation tests for marketing mix (place) were made and resulting in a value of p = 0.337 (p &gt; 0.05) and coefficient correlation r = 0.094. Spearman Correlation tests for marketing mix (promotion) were made, and result in a value of p = 0.358 (p &gt; 0.05) and coefficient correlation r = 0.090. Spearman Correlation tests for marketing mix (price) were made, and a result of p value = 0.935 (p &gt; and coefficient correlation r = - 0.008. Spearman Correlation tests were made, and a result in a value of p = 0.44 (p &lt; 0.05) and coefficient correlation r = 0.764. Based on the results that were found it can be concluded that there was a significant association between marketing mix (product) and outpatient satisfaction and no significant associations were found between marketing mix (place, price, promotion) and outpatient

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.