Parking Resources Research Articles

Mathematical Modeling and Enhancing the Carrying Capacity of National Parks in the United States

A rapid growth in the number of visitors has been observed for many national parks in the United States, examples include Yellowstone National Park, Yosemite National Park, Rocky Mountain National Park and Arches National Park. As a result, public transit and parking resources have been stretched closer and closer to their capacities in these parks. In response, some National Park Services (NPS) have started to implement crowd control measurements, such as the timed entry reservation systems, where visitors have to reserve their slots to visit the park during certain time periods of the year. However, such policy does not touch on the fundamental issue of the subject, that is, the carrying capacity of the park. In this paper, we demonstrate that there will be even greater growth in the number of visitors in the near future, which means that a capacity boost is inevitable. Fortunately, we also observe that if we can fix some key bottlenecks, then we can dramatically increase the capacity of the park without destroying more natural land.

Reservation and allocation model considering the user cost and the utilization of parking space

In order to alleviate the problem of imbalance between parking supply and demand by improving the utilization rate of parking resources, and considering the game relationship between user benefits and system benefits in parking allocation, the optimization objective function of maximum parking space utilization and minimum user cost in parking reservation mode is established from the two aspects which are system optimization and user optimization respectively. Then, the optimal allocation parking integer programming model (OPA) considering users' preferences is established. An Augmented Lagrangian-Alternating Direction Method of Multipliers Algorithm is designed to solve the optimal solution of the model. Finally, in order to test the validity of the model, the proposed model and the classical distribution model are compared and analyzed under different supply and demand conditions. The results show that the performance of the proposed model in three performance metrics which are parking utilization, average user cost and request acceptance rate is significantly better than that of the classical allocation model. The research results of this paper can provide theoretical reference for the management of parking reservation platform.

Private parking space owners’ choice behavior of different sharing modes: hybrid choice model using justified latent variables

ABSTRACT Private parking spaces account for a large proportion of potential parking resources, whose utilization rate could be raised by sharing them. To investigate private parking space owners’ sharing choice behavior, this paper applies a framework of Combined Technology Acceptance Model and the Theory of Planned Behavior for a more comprehensive consideration of psychological attitudes on these suppliers in urban residential areas. A web-based survey was conducted to collect data. The justified factors are then incorporated in hybrid choice model (HCM) as latent variables. HCM results showed that incorporation of latent variables such as perceived risk provides better fitting effect than traditional multinominal models, and that revenues and community participance are influential factors for sharing choice. Our findings indicate behavioral intention framework could serve as argumentation of rational selection of latent variables. These findings could also support better implementation of shared parking from managerial and operational perspectives.

Data-Driven Dynamic Optimization for Real-Time Parking Reservation Considering Parking Unpunctuality

This paper examines a real-time parking reservation service that dynamically allocates a limited number of parking slots (which can be reused) to serve parking requests in a crowded area. The study takes into account two stochastic factors: (i) random arrivals of parking requests and (ii) driver parking unpunctuality, which are seldom simultaneously considered in the existing literature. This study formulates the dynamic parking resource allocation problem as a joint chance-constrained model to maximize the expected total revenue over a finite horizon. Due to the complex nature of the model considering the two stochastic factors, the study proposes a data-driven approach using joint chance constraint decomposition and sample average approximation to approximate the model to a deterministic mixed-integer programming model. Furthermore, a stratified ranked set sampling method is introduced to construct a high-quality sample set as the input to the deterministic model, and valid inequalities are designed to accelerate the solution process. Numerical experiments based on real-world data are conducted to validate the performance of the proposed approach under multiple parking scenarios and provide findings.

Two-stage approximation allocation approach for real-time parking reservations considering stochastic requests and reusable resources

The digital economy has greatly impacted the traditional parking industry. This study focuses on a real-time parking reservation system that allocates available parking slots (supplies) to serve the parking requests (demands) of drivers within a certain area. The challenge lies in dealing with the randomness of requests with varying parking durations and the reusability of parking resources, which have not been well researched. To address this problem, we propose a stochastic optimization model and a data-driven two-stage approximation approach. The first stage generates a pre-allocation scheme as a basis, while the second stage dynamically adjusts the scheme based on observed requests and available parking slots to improve the system’s performance throughout a finite operational horizon. The study has three main contributions. Firstly, this paper proposes a data-driven nonparametric approach based on sample average approximation (SAA) to solve the stochastic optimization model. Secondly, it develops a stratified ranked set sampling (SRSS) method to ensure the quality of the sample set and proves that the sample set sampled by the SRSS method is unbiased. Finally, an evaluation method is proposed to establish a confidence interval for the expected revenue obtained by the data-driven method. Experimental results based on real parking demand data show that our approach outperforms both a commonly used heuristic allocation policy in the parking industry and the standard SAA approach with two popular sampling methods and an evaluation method based on Student’s T-test in the literature.

Research on Parking Problems and Governance Strategies in Old Urban CommunityTaking T Community, Qinhuai District, Nanjing as an Example

With the rapid growth of urban residents' private cars, the problem of parking in the old communities has become increasingly prominent. Taking the T Community in the Qinhuai District of Nanjing as an example, this paper analyzes the current situation and problems of residential parking through an on-the-spot investigation. This paper uses questionnaires and interviews to analyze the parking needs of residents, and proposes a parking problem governance strategy, in order to provide a reference for alleviating the parking problem in the community. Key recommendations include excavating the parking space inside the community, expanding parking resources by rectifying road resources and sharing parking at different times and peaks, and optimizing management measures of community parking.

$\mathrm{W}^{2}$Parking: A Data-Driven Win-Win Contract Parking Sharing Mechanism Under Both Supply and Demand Uncertainties

With the rapid growth of the number of private vehicles, searching for accessible parking spaces becomes intractable for drivers, especially during high-demand hours. In recent years, we are witnessing a number of sharing economy services. Contract parking sharing, as an innovative sharing economy mode, has the potential to alleviate the difficult parking issue and make full use of the urban parking resources. However, the uncertainties of both drivers' parking demand and owners' sharing supply make it challenging to achieve efficient sharing. Thanks to IoT technology, many current parking lots now record vehicles' fine-grained parking data for billing purposes. Leveraging these fine-grained parking data, we exploit available contract parking spaces to share them with drivers that have temporary parking demand. Specifically, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{W^{2}}$</tex-math></inline-formula> Parking, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</u> in- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</u> in contract <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parking</u> sharing system, which includes two key components: (i) an idle time prediction model to estimate available periods of parking spaces and (ii) a parking sharing model to schedule temporary users to have access to these available parking spaces under both demand and supply uncertainties using dynamic programming combined with a 2-approximation algorithm with performance-bound guarantees. we evaluate our system on seven-month real-world parking data from 368 parking lots with 14,704 parking spaces. Extensive experimental results show that our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{W^{2}}$</tex-math></inline-formula> Parking achieves more than 90% of accuracy in parking time prediction, and the utilization rate of contract parking spaces is improved by 35%

Optimal Parking Slots Reservation and Allocation Problem for Periodic Parking Platforms with Preference Constraints

Various solutions, such as parking reservation systems, have been proposed to alleviate the difficulty in finding parking slots. In such systems, parking requests are submitted in advance by drivers, and the systems will reserve appropriate parking spots for drivers if their requests are accepted. However, the parking slots may be allocated unreasonably, which may lead to a waste of space and time resources. In addition, there is a game relationship between operator’s profit (OP) and users’ benefits (UB), which may affect the sustainable development of the system, if balanced improperly. Given the drivers’ arrival and departure time and their parking preference, the paper proposes a periodic reservation and allocation mode (PRAM) and establishes a dual-objective binary integer linear model to solve the reservation and allocation problem. The model aims to maximize the comprehensive benefits of the operator and users and to take full advantage of parking resources. We proposed a TOPSIS-SA algorithm (Technique for Order Preference by Similarity to an Ideal Solution and Simulated Annealing algorithm) to solve our model. Numerical experiments show that our model performs better than the baseline models on all performance metrics such as total operating profit, users’ average walking distance, acceptance rate, and utilization of parking slots.

Exploring customer perception and satisfaction on the functions of parking application software

ABSTRACT The emergence of parking application software (parking APP), considerably decreases unnecessary vehicle circling and enhances parking resource utilization. Despite the abundant functions available, limited research has considered customer requirements and satisfaction regarding the functions of parking APPs. This research exactly examined the customer perception, as well as how satisfied customers were with the functions provided by the parking APPs. Initially, the attribute classification of functions was identified utilizing Kano model based on a questionnaire survey in Hunan Province, China. The integrated functions were then used as an antecedent to evaluate customer satisfaction by structural equation model (SEM). The data analysis suggests the perceived quality is positively influenced by the four main functions of query, navigation, evaluation, and account. The results reveal that customers have less demand for automatic space grabbing and reverse vehicle tracking in small and medium-sized cities.

Research on intelligent transportation and shared parking spaces inurban areas based on multi-source data integration

Shared parking is a new way to effectively mitigate the difficulty in urban parking and traffic congestion. With the development and application of modern information technologies, intelligent optimization is adopted to make shared urban parking becomes possible. An intelligent idea of integrating multi-source data is proposed to implement shared parking. An intelligent-sharing urban parking framework is built, and intelligent-optimization matching solution method for urban shared parking demand is proposed. In order to better systematically optimize the allocation efficiency of urban shared parking resources, a dynamic and intelligent-optimized adjustment strategy is proposed for the sharing periods, shared pricing and penalties for prolonged parking in buildings with various properties based on the utilisation rate of parking slots in shared areas and the occurrence rate of prolonged parking and emergencies. Finally, to implement the novel intelligent-optimized and shared parking mode integrating multi-source data in urban areas, advice and directions are given for constructing a better management and security system.

A Trust-Based Secure Parking Allocation for IoT-Enabled Sustainable Smart Cities

Smart parking is a crucial component of smart cities that aims to enhance the efficiency and sustainability of urban environments. It employs technology such as sensors and IoT devices to optimize the use of parking resources and improve drivers’ experiences. By reducing traffic congestion, decreasing air pollution, and enhancing accessibility, smart parking systems can contribute to the overall well-being of urban areas. IoT-enabled smart parking refers to the application of IoT technology to optimize and improve parking efficiency in smart cities. However, security and privacy challenges in IoT-enabled smart parking pose risks and concerns related to the collection and use of data by parking systems, such as unauthorized access or misuse of data, potential data breaches, and the need to ensure responsible data collection and usage to maintain user trust and confidence. To address these challenges, we propose a novel hybrid approach to trust management using machine learning algorithms to enhance the security and privacy of the system. Our approach consists of SVM and ANNs, taking into account credibility, availability, and honesty as key parameters. Furthermore, we use ensemble machine learning to select the best-predicted model from different trained models, leading to efficient performance and a trustworthy environment. Our results show that the proposed hybrid SVM classifier with a trust parameters approach achieved an accuracy of 96.43% in predicting and eliminating malicious or compromised nodes.

Research on Parking Recommendation Methods Considering Travelers’ Decision Behaviors and Psychological Characteristics

Intelligent parking services can provide parking recommendations and reservations for travelers. They are an effective method for solving the cruising for parking problems in big cities. This research conducted a sequential parking decision behavior survey and analyzed travelers’ parking choices and reservation behaviors at different stages of the travel process. Then, a parking recommendation model was established to consider the travelers’ psychological thresholds and the attention to parking factors. The effects of different parking recommendation schemes in different situations were further explored based on parking simulations. It was concluded that travelers were more willing to accept and use the parking recommendation system. A total of 56% of travelers chose to make parking reservations during the travel process. The satisfaction proportion of the psychological threshold for the parking reservation group was higher than that for the non-parking reservation group. A dynamic parking recommendation scheme with a regulation threshold can change the recommendation strategy according to the overall utilization of parking lots. The implementation of the scheme can not only improve travelers’ parking experience, but it can also effectively balance the utilization of parking resources. It can be applied to different parking utilization situations and produce good performance. The research results provide references for the design and application of intelligent parking services in order to solve parking problems.

Spatio-temporal parking occupancy forecasting integrating parking sensing records and street-level images

The prediction of parking occupancy is of great importance to urban planning. As the number of cars increases and parking resources become limited, the lack of parking supply has become a challenge for urban design. Previous works ignore the correlation between car parks when predicting parking occupancy, which limits the accuracy of the prediction. To address this issue, this study proposes a Temporal-GCN-based correlated parking prediction model (CPPM) to forecast the temporal occupancy of car parks. In particular, the model utilises Convolutional Neural Networks (CNN) and Bayesian probabilities to extract street view similarities in car parks, as well as their spatial correlations, cosine similarity is used to calculate the activity type similarity, and Graph Convolutional Networks (GCN) and Gate Recurrent Units (GRU) are integrated to predict spatio-temporal car park occupancy, taking into account both temporal parking records, similarities in car parks, and their spatial correlations. We conducted two case studies in Ningbo and Beijing, China, integrating over 10 million parking sensing records and corresponding street view images of parking lots to predict parking occupancy. The results show that our model has outstanding performance over the baselines and can be extended for various types of car parks in cities of different sizes and different levels of development. The results also reveal the parking preferences of the citizens of Ningbo and Beijing, which is valuable for a quantitative understanding of commuters’ parking patterns and behaviour and can be used as a guide for urban planning and management.

Matching Supply and Demand in Online Parking Reservation Platforms

Our work concerns online parking reservation platforms proposed in the last decade to cope with the parking challenge in cities worldwide. Enlisting parking resources from commercial operators (e.g., lots) and individuals (e.g., doorways) and letting drivers make online reservations through mobile apps, those platforms seek to ease transactions between the two sides and best match parking supply with parking demand. This way they maximize their value for drivers and parking space providers but also their revenue out of charged commissions. We distinguish between two types of commissions these platforms typically charge, fixed per transaction and proportional to its value, and formulate the respective optimization problems for the platform revenue maximization. We show that the two problems are NP-hard and design a novel algorithm that can treat both by combining greedy and dynamic programming principles. We study its optimality properties both analytically and experimentally, showing that the algorithm closely tracks optimal solutions for small and moderate problem sizes at run times that are several orders of size smaller than those needed by off-the-shelf ILP solvers. We then analyze real parking data we collected for the period 2018-2020 from the Bournemouth city in UK to realistically model the rich spatiotemporal dynamics of parking demand such as the location, start times and duration of parking events. These datasets drive the experimental evaluation of the proposed algorithm, which reports gains of up to 35% compared to the de facto parking resource reservation policy in such platforms. Notably, the highest gains are achieved when the platform operates under constrained supply conditions.

BPR: Blockchain-Enabled Efficient and Secure Parking Reservation Framework With Block Size Dynamic Adjustment Method

The parking lot is one of the important components of the intelligent transportation system (ITS). The current parking lots mainly use instant parking, which has low parking efficiency, during peak hours, which leads to traffic congestion. To guarantee the stable operation of parking lots, we propose a blockchain-enabled parking reservation framework, called BPR. Traditional parking reservation systems may exist the condition of malicious reservations, and resulting in wasted parking spaces. Therefore, we design a reputation mechanism to manage the parking reservation behavior of vehicles and reduce the number of malicious nodes. In addition, to balance the performance of the blockchain at different times (especially during peak hours), we use deep learning (DL) to dynamically adjust the block size to make the blockchain run more efficiently and stably. We deploy the system in Hyperledger Fabric and conduct effectiveness experiments. The comprehensive evaluation results and analysis show that the proposed reputation mechanism can effectively curb malicious nodes from reserving parking spaces and reduce the waste of parking resources. And the block size will be dynamically adjusted to balance the performance of the blockchain at different periods, this method is also applicable to other blockchain performance-sensitive scenes. Finally, this paper is compared with related work to demonstrate the innovation and feasibility of this work from various aspects.

Equalized Utilization Model of Regional Shared Parking Space

In view of the contradiction between dense parking demand and limited parking resources, according to the time-varying characteristics of parking demand of different building types in the city, a dual objective parking allocation model with the highest equilibrium degree of shared parking utilization and the minimum walking distance was established. The model defines the boundary constraints of the walking distance and the equilibrium utilization, weights the multi-objective function, and then uses the genetic algorithm tool to solve it. Taking Tangshan Second Hospital and its surrounding areas as an example, the basic data such as the total number of parking Spaces, parking demand and walking distance after parking in each parking lot were obtained. The simulation output of parking space utilization and parking allocation in the area was carried out by the simulation algorithm, and the feasibility of the model was verified. The results show that the model can be used to allocate shared parking Spaces in urban areas, which can effectively balance the utilization rate of parking lots in the region and alleviate the problem of uneven and inadequate parking resources utilization in the region.

Enhanced Cloud Car Parking System using ML and Advance Neural Network

It is a challenging challenge for the users to find a parking spot to park their vehicles because of the rapid increase in vehicle density, particularly during the busiest times of the day. Moreover, a smartphone application is available that enables users to check parking space availability and reserve a spot accordingly. By lowering fuel use and pollution in cities, smart parking can boost the economy. Using parking resources more effectively is referred to as parking management. Finding the root of the issue is the first step in effective parking management. In light of the rising number of vehicles on the road, finding adequate parking is one of the sectors with the quickest growth rates. Traffic has typically been a nightmare. intelligent parking solutions from major hardware vendors to address these problems. On the other side, these are unsuited for their intended use and have led to significant parking problems at numerous businesses. The strategy makes an effort to provide a workable solution to the smart parking issue.

Examining parking occupancy prediction models: a comparative analysis

Parking is a persistent challenge in urban areas, especially on college campuses, as limited parking spaces and increasing vehicular traffic are at the forefront of campus administrators’ concerns. Lack of parking availability leads to frustration among students, faculty, and staff and results in increased traffic congestion, environmental pollution, and reduced overall productivity. Accurate prediction of parking occupancy plays a vital role in optimizing parking resource allocation, minimizing congestion, and improving the overall campus experience. Thus, this study comprehensively analyzes parking occupancy predictions for a college campus garage, using several different models. The dataset spans from January 2022 to March 2023, providing insights into the models' performance for more than one year. The research aims to utilize random forest, decision tree, linear regression, and support vector regression (SVR) models and compare their effectiveness, using evaluation metrics such as root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). Random forest outperformed decision tree, SVR and linear regression models in terms of accuracy, as indicated by the lower MAE values. Additionally, random forest achieved a better classification performance, and a higher R2 suggests a stronger correlation between the predicted and actual occupancy values. Conversely, SVR displayed the weakest performance among the four models, with a high MAE and negative R2 value. The findings of this research will aid in improving parking management systems and contribute to the development of efficient and sustainable parking solutions on college campuses and in urban areas.

The use of Digital Twin in the sustainable development of the city on the example of managing parking resources

Sustainable city development is an approach to city planning and management that takes into account the balance between economic, social and environmental aspects in order to ensure sustainable social development, environmental protection and ensuring a high quality of life for residents.The aim of the article is to draw attention to the problems in the field of parking vehicles in a smart city, which should be solved as soon as possible. Problems related to parking in the city are a common challenge that can affect the comfort of living of residents, traffic, natural environment and transport efficiency. Many cities lack parking spaces in relation to the number of vehicles, leading to difficulties in finding a parking space. This can lead to traffic jams that increase the time it takes to find a parking space, increase traffic-related emissions and reduce the quality of life for residents.The research method to solve the problem related to the management of parking spaces is focused on the analysis of the city's problems and guidelines resulting from the sustainable development of the European Union. In the application part, open data resources were used to carry out the simulation in the proprietary application solution. The result of the article, which is a scientific research, is the presentation of the author's application in the Digital Twin concept for the simulation of parking space management.The simulation is the result of research conducted using open data published on the Internet.The conclusion of the research is that in the process of effective management of parking spaces, simulation in the Digital Twin concept can be used. There is great potential in this approach.

Unmanned Aerial Vehicle Computation Task Scheduling Based on Parking Resources in Post-Disaster Rescue

Natural disasters bring huge loss of life and property to human beings. Unmanned aerial vehicles (UAVs) own the advantages of high mobility, high flexibility, and rapid deployment, and are important equipment during post-disaster rescue. However, UAVs usually have restricted battery and computing power. They are not fit for performing compute-intensive tasks during rescue. Since there are widespread parking resources in a city, multiple parked vehicles working together to compute the applications from UAVs in a post-disaster rescue is investigated to ensure the quality of experience (QoE) of the UAVs. To execute uploaded task effectively, surviving parked vehicles within the monitoring range of an UAV are arranged into a cluster as much as possible. Then, the task execution cost is analyzed. Furthermore, a deep reinforcement learning (DRL)-based offloading policy is constructed, which interacts with the environment in an intelligent way to achieve optimization goals. The simulation experiments show that the proposed offloading scheme has a higher task completion rate and a lower task execution cost than other baselines schemes.