Parking Space Research Articles

Optimal clustering analysis for airport parking

Air travelers are often hurried and at risk of missing their flights. Many of them are quite sensitive to service quality and overall travel times. In larger airports the demand for parking spaces forces many users to park quite far from passenger terminals. Thus, it is desirable to allocate available parking spaces at any given time in ways that minimize the users' access distances. Although the major layout and allocation decisions for airport parking are highly constrained by available land and physical parking infrastructure, a well-designed parking clustering scheme based on expected parking durations could assist airport operators in effectively allocating limited space, which minimizes the travelers' average access distances between parking facilities and an airport terminal by employing grouping concepts based on the expected parking time durations of vehicles. In subdividing the available parking facilities, the boundaries between different user groups (e.g., short, medium, and long term) are optimized based on the distributions of (1) expected vehicle dwell times and (2) available parking capacity at various access distances from airport terminals. Even with only two clusters (e.g., short-term and long-term parking), results show that average access distances from parking spaces to terminals can be reduced by 25–44%. Although additional subdivisions of the available parking areas can yield additional savings of travelers’ time and access distances, the marginal benefits decrease rapidly as the number of subdivisions grows.

Sustainable operation of on-street parking within Hilla City by Simulation Model (VISSIM) with Surrogate Safety Assessment Model (SSAM)

Abstract The preservation of sustainability poses an ongoing and persistent problem. To attain sustainability, it is imperative to prioritize the preservation of road traffic safety, the prevention of crashes, the utilization of pollution-free vehicles, and the secure transportation of commodities. The research approach employed in this study involves collecting data about many factors, including air pollution, noise levels, incident frequency, and traffic volumes. The study focuses on 20 links with on-street parking facilities in Hilla City, Iraq. This study uses the VISSIM microsimulation model SSAM software. This study used PTV VISSIM (Thesis) 2024 (SP 02). This license(Thesis) of PTV VISSIM software enables one to conduct the research unlimitedly, which is good for obtaining accurate results. The simulation time used in this study is 3600 seconds. PTV ViSSIM software enables the emulation of traffic movements on selected streets that contain on-street parking. Six streets in Hilla City are divided into 20 links. The streets were monitored using video recordings to collect real traffic data, which was then used to estimate delay and emissions. Additionally, trajectory files were created for future analysis. The SSAM program can be used to determine the conflicts in each link. The simulation models were effectively calibrated and validated using actual data. This study assesses the sustainable, social, economic, and environmental aspects of roadways, including on-street parking spaces. This project intends to construct a sustainable composite index utilizing existing data collection methods. Existing data on urban transportation sustainability considers economic factors like delay, social factors like safety, and environmental factors like noise and air pollution. The composite sustainability index incorporates economic, social, and environmental data, effectively tackling the difficulties related to normalization, weighting, and aggregation. This study aims to evaluate the long-term viability of transportation in Hilla’s central business area (CBD) by considering the incorporation of on-street parking. The Composite Sustainability Transportation Index (ICST) results for links with on-street parking in Hilla City indicate that the sustainability operation is evaluated at different levels: low (8 links), moderate (7 links), and high (5 links).

A parking incentive allocation problem for ridesharing systems

Ridesharing occurs when people with similar schedules and itineraries travel together to reduce their commuting costs. In this paper, we study how parking spaces can be used to incentivize drivers to participate in ridesharing systems. We develop a Parking Incentive Allocation (PIA) system to promote and allocate parking lots to ridesharing drivers in a stochastic and dynamic environment. The optimization problem is formulated at each period as a multi-stage stochastic decision-dependent program. To overcome the complexity of the model, we propose one greedy policy, and three approximations including two stochastic policies and an expected-value policy. We evaluate the effectiveness of the four policies on the data generated from GPS information collected by the MTL Trajet project, which studies residents’ travel patterns throughout the city of Montreal. The computational results indicate that on average, the approximate policies can improve the total distance saving by more than 20% over various problem settings. Additionally, the results show that the performance of the PIA system is significantly influenced by the attractiveness of the parking incentive to drivers.

PERCEPTION DIMENSIONS OF THE USE OF ELECTRONIC PARKING IN TRADITIONAL MARKETS AS AN EFFORT TO INCREASE REGIONAL ORIGINAL INCOME

Electronic parking is one of the public service innovations that has the potential to increase local revenue in Ponorogo Regency. This innovation takes the form of an application that regulates parking in market areas that tend to be congested by utilizing technology such as parking sensors and applications, drivers can easily find available parking spaces and make payments electronically and also manually. The phenomenon in market parking management research is that it is not yet optimal in terms of service to the community and maximizing regional income from the market parking levy sector. This research aims to examine the dimensions of public perception regarding the use of market electronic parking as a strategy to increase Regional Original Income and evaluate user perceptions of the trust and economic benefits of electronic parking at Tonatan Market, Ponorogo. This research uses a method with a qualitative approach. The data collection technique uses an interactive approach using triangulation techniques such as observation, interviews, questionnaires and document retrieval. Samples were taken from Tonathan market parking users totaling seven informants. The research results show that there are people who support and do not support electronic parking innovation. The Department of Trade, Cooperatives and Micro Enterprises of Ponorogo Regency also supports the innovation of electronic parking because it can increase regional income.

Multi-Criteria Decision Analysis to Evaluate the Geographic Potential of Alternative Photovoltaic Types

This paper contributes to the expansion of green energy production, which is being pursued in order to mitigate climate change and carbon emissions from energy production. It addresses the delineation of areas that are suitable for the application of photovoltaics in the context of agricultural areas, water bodies, and parking spaces. Three specific photovoltaic types are examined in order to find out which criteria influence their geographic potential and whether spatial multi-criteria decision analysis methods are suitable for identifying suitable areas. The proposed approach consists of four steps: (1) collecting factors through expert interviews and questionnaires; (2) mapping the criteria to the spatial datasets; (3) deriving weighted scores for individual criteria through expert interviews; (4) applying the multi-criteria decision analysis method to compute and aggregate the final scores. We test our methodology at selected sites in the state of Styria, Austria. The test sites represent the topographical characteristics of the state and are about 5% of the size of Styria, approximately 820 km2. The key contributions are a weighted set of criteria that are relevant for the geographic potential of alternative photovoltaic types and the developed methodology to determine this potential.

The impact of the social-built environment on the inequity of bike-sharing use: A case study of Divvy system in Chicago

Bikeshare is increasingly recognized as a healthy travel behaviour worldwide. However, issues of inequity in bike-sharing usage exist and hinder the social benefits of bike-sharing system. This paper aims to unveil the spatiotemporal evolution of inequalities in bike-sharing usage and their social-built environment correlates, using Chicago’s Divvy system as a case study. Specifically, Gini coefficients and panel data regression models are applied to analyse equity concerns in bike-sharing uses and its social-built environmental factors. Thirty-two disadvantaged communities and forty-five non-disadvantaged communities are identified based on ethnicity, income, and education levels. The Gini index indicates a greater level of inequity and inconsistency in bike-sharing usage within disadvantaged communities compared to non-disadvantaged communities over time. Model results further reveal that built environment factors such as park space positively impact equitable bike-sharing uses in disadvantaged communities. In contrast, the social factor of educational levels in non-disadvantaged communities shows a negative relationship. These findings aim to promote essential, efficient, and equitable bike-sharing usage for Chicago, stakeholders and users.

Urban Regeneration of Old Neighborhoods: The Case of Old Al Ghanim in Doha, Qatar

ABSTRACT Qatar witnessed its first urban expansion era following economic prosperity amid the discovery and production of oil and its exportation in the early 1950s. However, the capital city of Qatar, Doha, beheld numerous challenges following the rapid growth that resulted in unsystematic urban sprawl in the city. Therefore, the country developed national expansion plans and policies for the geographical city change process by encouraging sustainable and regenerative approaches. Studies concentrating on urban regeneration in rapidly growing cities offer prospects by evaluating existing settings and proposing regeneration solutions. This research aims to assess the urban environment of an old neighborhood in Doha, called Fareej Al Ghanim by examining the existing conditions and providing recommendations for its regeneration. A qualitative research methodology is applied in this study to identify proper solutions for urban regeneration in Fareej Al Ghanim. The latter is an important case to analyze, study and recommend an appropriate regeneration strategy, which can be used as an example of best practice for other historic neighborhoods in Qatar, the Gulf and elsewhere. Namely, the urban regeneration of Old Al Ghanim neighborhood in Doha was studied through physical analysis of (A) urban settings (B) land uses and (C) current building conditions. The information was collected through data collection, site visits, walking tour assessments and map analyses. The findings identified the following; (1) poor infrastructure of the neighborhood, (2) multiple abandoned buildings in a deteriorating state, (3) lack of parking spaces and over congestion, (4) absence of cycling paths and pavements and (5) inexistence of open areas where people can sit and relax. Thus, the proposed recommendations for urban regeneration are based on (A) enhancing the physical environment of the area through land-use changes and (B) rehabilitating the surviving historic buildings and reconstructing the deteriorating ones. The findings offer policymakers, city planners, and designers an opportunity to improve old districts in Qatar, the Gulf and elsewhere.

PREDICTION OF DRIVING TIME OF ELECTRIC SCOOTER (E-SCOOTER) DRIVERS BY MACHINE LEARNING

This study aims to estimate the driving times of drivers who prefer electric scooter vehicles. In general, e-scooters reduce the loss of time caused by traffic jams because, thanks to their smaller size and maneuverability, these vehicles provide rapid progress in urban journeys. E-scooters also offer an advantage in finding a parking space and easy parking thanks to their more compact structure. In this study, ML algorithms were used to predict the driving times of drivers who prefer e-scooter vehicles. The AB model has performed well with a low Mean Square Error (MSE) value (0.005). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values are also relatively low (0.069 and 0.039, respectively), indicating that the model's predictions are close to the actual values. Also, the high R-squared-Coefficient of Determination (R2) value (0.947) suggests that this model explains the data quite well, and its predictions approach the actual values with high accuracy. On the other hand, the GB algorithm performed poorly compared to different algorithms, with its high margin of error and low accuracy rate. These results provide an advantage in time management by estimating the travel time a driver will make with the e-scooter. As a result, e-scooters offer drivers the opportunity to save time and manage their daily mobility more effectively, driving these vehicles attractive for transportation.

Air pollution impacts from warehousing in the United States uncovered with satellite data

Regulators, environmental advocates, and community groups in the United States (U.S.) are concerned about air pollution associated with the proliferating e-commerce and warehousing industries. Nationwide datasets of warehouse locations, traffic, and satellite observations of the traffic-related pollutant nitrogen dioxide (NO2) provide a unique capability to evaluate the air quality and environmental equity impacts of these geographically-dispersed emission sources. Here, we show that the nearly 150,000 warehouses in the U.S. worsen local traffic-related air pollution with an average near-warehouse NO2 enhancement of nearly 20% and are disproportionately located in marginalized and minoritized communities. Near-warehouse truck traffic and NO2 significantly increase as warehouse density and the number of warehouse loading docks and parking spaces increase. Increased satellite-observed NO2 near warehouses underscores the need for indirect source rules, incentives for replacing old trucks, and corporate commitments towards electrification. Future ground-based monitoring campaigns may help track impacts of individual or small clusters of facilities.

Perception study of urban green spaces in Singapore urban parks: Spatio-temporal evaluation and the relationship with land cover

In the current era of increasing urbanization, urban green spaces play a crucial role in enhancing human well-being. However, quantifying public perceptions from text data at spatio-temporal scales remains challenging, and the relationship between urban green space perception and spatial-physical attributes requires further exploration. This study systematically examines public perceptions of urban green spaces within Singapore's urban parks from 2018 to 2022. Utilizing Twitter data, it applies large language models to conduct textual content analysis related to urban green space. The findings reveal a positive trend, with individuals expressing favorable perceptions and satisfaction towards urban green spaces in Singapore. Specifically, this study demonstrates that people's perceptions of urban green spaces are influenced by vegetation density. Higher vegetation density heightens people's awareness of spatial presence, while shrub and grassland may lead to neglect of urban green spaces as individuals focus more on themselves. Additionally, due to the spatial heterogeneity of the area, there is no clear correlation between all land covers and public satisfaction with urban green spaces in Singapore. The results also indicate a significant decrease in public perception in 2020, followed by a subsequent recovery. This fluctuation is attributed to the substantial impact of the COVID-19 pandemic, suggesting that external socio-political, economic, and public health events can impact public green space needs and spatial perceptions. In conclusion, this study contributes to the understanding of urban green spaces by effectively analyzing textual content extracted from social media data using large language models. The insights gained contribute valuable to the following discussions regarding the planning and design of urban green spaces and urban parks.

Autonomous driving parking robot systems for urban environmental benefit evaluation

Many countries are now taking environmental issues seriously and working towards neutral. Therefore, this study aims to reduce carbon emissions by suggesting ways to reduce the use of passenger cars and induce the use of public transportation and walking modes. To this end, a valet parking robot system was designed in a transfer car park outside Seoul. The effect of the system was quantified. By implementing the valet parking robot system in the transfer parking lots, the time to find a parking space and the time to park after finding a parking space are reduced when using existing parking lots. In other words, the valet parking robot system can reduce out-of-vehicle time, and the reduction in travel time can induce a modal shift from existing car users to public transport. Furthermore, by reducing the number of cars in the city center, vehicle kilometer traveled(VKT) will be reduced, and the emission of pollutants that cause air pollution will also be reduced due to less traffic on public roads in the city center. This study analyzes this quantitatively and concludes that the implementation of a valet parking robot system can have a positive environmental impact.

Quantitative analysis of social service space equity in urban parks in China

This study aims to quantitatively analyze the equity of social service space in urban parks in China, in order to explore the equity issues faced by different social groups in accessing urban park services. The research background focuses on the importance of urban parks as social service spaces, particularly in improving residents’ quality of life and well-being. Through a comprehensive literature review, the study examines the social service functions of urban parks, the relationship between parks and social psychology, and the theoretical framework of equity. The study employs quantitative research methods, collects data on urban park usage and resident satisfaction, and defines relevant analysis variables. The data analysis section reveals the basic characteristics of park service space usage and resident well-being index through descriptive statistical methods. Subsequently, quantitative analysis is conducted to evaluate the current status of equity in urban park service space and explore the key factors influencing equity. The study reveals a significant correlation between social psychological factors, resident well-being index, and equity in park service space. Finally, the research conclusion emphasizes the importance of improving equity in social service space in urban parks and provides specific policy recommendations. At the same time, the study acknowledges its limitations and suggests future research directions. This study provides insights for urban planners and policymakers on how to enhance equity in urban park services and offers important strategic guidance for improving overall well-being of urban residents.

Park Heritage of the Island of Krk between Urban Transformations and Climate Change

The island of Krk in Primorje-Gorski Kotar County, Croatia, is also called the Golden Island because of its favorable geographical location, the diversity of natural and urban landscapes, the beauty of the coast and the sea, the wealth of tangible and intangible heritage, and especially because of the opportunities for living and working. During the last century and in this century, urban landscapes have been exposed to dramatic changes that transformed old castles or former smaller settlements of the local population into tourist centers and desirable places for permanent or temporary residence. A significant part of their complex structure is the cultural and historical heritage, within which the island’s park heritage has so far been insufficiently recognized and valued. Therefore, this paper examines forty selected public park spaces in the area of all local self-government units of the island of Krk in the context of urban transformations and climate change. It is concluded that the island’s park heritage has often been created as a result of urban transformations in which, despite being exposed to constant changes, it is mostly preserved, but that recently, new elements of this heritage are emerging. A new challenge is the threat of the consequences of climate change—increasingly frequent and long-lasting droughts, extreme precipitation and flash floods, stormy winds, rising sea level and salinization, which further emphasizes the need to preserve the park heritage of the island of Krk in the context of resistance to climate change.

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.

Application of High-Spatial-Resolution Imagery and Deep Learning Algorithms to Spatial Allocation of Urban Parks’ Supply and Demand in Beijing, China

The development of green spaces in urban parks can significantly enhance the quality of the urban and ecological environment. This paper utilizes 2021 Gaofen-7 (GF-7) satellite remote sensing images as its primary data source and uses deep learning algorithms for the precise extraction of the green space coverage within Beijing’s fifth ring road. It also incorporates the park points of interest (POI) information, road data, and other auxiliary data to extract green park space details. The analysis focuses on examining the relationship between supply and demand in the spatial allocation of green park spaces from an accessibility perspective. The main findings are as follows: (1) The application of deep learning algorithms improves the accuracy of green space extraction by 10.68% compared to conventional machine methods. (2) The distribution of parks and green spaces within the fifth ring road of Beijing is uneven, showing a clear pattern of “more in the north and less in the south”. The accessibility within a five-minute service radius achieves a coverage rate of 46.65%, with a discernible blind zone in the southeast. (3) There is an imbalance in the per capita green space location entropy within the fifth ring road of Beijing, there is a big difference in per capita green space location entropy (44.19), and social fairness needs to be improved. The study’s outcomes unveil the intricate relationship between service capacity and spatial allocation, shedding light on the supply and demand dynamics of parks and green spaces within Beijing’s fifth ring road. This insight will contribute to the construction of ecologically sustainable and aesthetically pleasing living spaces in modern megacities.

PERANCANGAN FASILITAS PARKIR PASAR BLAURAN SURABAYA

This study discusses the design of parking facilities at Pasar Blauran Surabaya in response to the problem of limited parking space that affects the smooth operation of the market and the convenience of visitors. Through a mixed qualitative and quantitative approach, this research conducts an in-depth analysis of the parking conditions at the market. Primary data were collected through field surveys, including observations of parking patterns and interviews with market traders and visitors. The results show that parking space at Pasar Blauran is still limited, leading visitors and traders to often choose unofficial parking spots, increasing the risk of congestion and traffic problems. The limitation of parking space at Pasar Blauran Surabaya is a serious issue that affects the market's operation and the comfort of visitors and traders. This research identifies the factors causing and the impacts of this issue and offers solutions. Through a mixed qualitative and quantitative approach, including literature reviews, field surveys, and data analysis, the main findings and recommendations are presented. The research site is Pasar Blauran, located in a densely populated area in South Surabaya. The results are expected to provide guidance for local governments, market managers, traders, and the community in improving the quality and sustainability of the market. Keywords: Pasar Blauran, Surabaya, parking facilities, limited parking space, parking conditions.

Theme park greening VR design based on entertainment robots and genetic algorithm optimization: Digital entertainment design experience

Theme parks should provide attractive green design and digital interactive experiences to attract tourists and improve their satisfaction. Therefore, this study aims to achieve a digital interactive experience in theme park greening design through the use of entertainment robots and genetic algorithm optimization. The study analyzed the characteristics of experiential theme parks and introduced landscape 3D modeling techniques for creating interactive 3D models of green spaces in theme parks. By using technologies such as sensors and cameras, entertainment robots can perceive the presence and behavior of tourists. Entertainment robots can interact with tourists in real-time, customize them according to their interests and needs, and provide unique and enjoyable experiences for tourists. By collecting and analyzing feedback and behavioral data from tourists, the advantages and improvement points of digital interactive experiences can be identified. Through comparative experimental analysis with traditional experience methods, it was found that digital interactive experience can significantly improve tourist participation and satisfaction. Through user data analysis of interactive experience, this design can effectively improve the greening design of theme parks, enhance tourist participation and entertainment experience.

The Impact of Exhaust Louvres and Emission Rates on The Effectiveness of Parking Lot Ventilation

Abstract This research investigated a couple of aspects of CO emissions from car vehicles. Firstly, it explored the influence of varying the number of vehicles in operation. Secondly, it analysed the significance of exhaust vents affecting the concentration of CO through parking space lots. The conducted analysis relied on simulation data obtained using ANSYS-Software, as opposed to experimental data. The accuracy of the natural ventilation model was validated. The impact of various running vehicles was examined, including those with half, partially, and fully-loaded. The objective was to determine the correlation between the number of running vehicles and the increase in CO concentration. In addition, several configurations of exhaust vents were examined for the fully loaded scenario. As a result, it was shown that the architectural layout has an impact on the natural ventilation system, which may not be suitable for human health, especially when dealing with greater car emission rates.

Implementasi Google Cloud Vision untuk Deteksi Ketersediaan Lahan Parkir Kendaraan Mobil di Perguruan Tinggi

The increasing number of car users in colleges has posed significant challenges in effectively managing parking lots. This research focuses on the implementation of Google Cloud Vision to detect and monitor the availability of car parking lots in a college environment. By utilizing cloud-based image processing services and machine learning algorithms from Google Cloud Vision, the proposed system aims to provide real-time analysis and reporting on parking slot availability. The methodology used involves capturing images of the parking area using strategically placed cameras, then uploading these images to Google Cloud Vision to detect the presence or absence of cars. The system is designed to be scalable, ensuring that it can handle different sizes and complexities of parking areas. Preliminary results show that the approach using Google Cloud Vision offers a high level of accuracy in identifying occupied and empty parking slots, thus providing a reliable tool for parking management in a college environment. This work discusses the development, implementation, and evaluation of the system, and highlights its potential in improving parking lot utilization efficiency and reducing the time users spend searching for available parking spaces.

Advanced Car Parking and Billing System

Abstract: The objective of this paper is to solve the issue of the car parking system in our country. We have proposed a system where we give the solution for management of parking space and how we can utilize the current space more effectively. This would lead to decrease in chaos and would help people to choose their parking space in advance and we would give them a choice to cancel their reservation of parking space. In this paper we intend to build a centralized system where all the non reserved and non-occupied spots for parking that are available can be used in a better way. We also looked at a few of the current systems and highlighted their shortcomings. Our framework differs from the ones that are currently in use in that we aim to make our solution as less people- dependent as possible, dependent as feasible by automating both the cars and the parking lot, but the majority of current frameworks necessitate that personnel leave the actual vehicle.