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Journal articles on the topic 'Continuous parking occupancy prediction'

Author: Grafiati

Published: 25 May 2024

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1

Khandhar, Aangi B. "A Review on Parking Occupancy Prediction and Pattern Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29597.

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Parking occupancy prediction and pattern analysis is a crucial component of modern urban management systems. Utilizing advanced data analysis techniques, this project aims to develop a predictive model for forecasting parking occupancy levels and analyzing patterns within parking data. By leveraging machine learning algorithms and statistical methods, the project seeks to provide insights into parking behavior and optimize resource allocation in urban areas. The implementation of parking occupancy prediction and pattern analysis contributes to efficient urban planning, improved traffic managem

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Zhao, Ziyao, Yi Zhang, and Yi Zhang. "A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale." Journal of Advanced Transportation 2020 (February 14, 2020): 1–12. http://dx.doi.org/10.1155/2020/5624586.

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Parking issues have been receiving increasing attention. An accurate parking occupancy prediction is considered to be a key prerequisite to optimally manage limited parking resources. However, parking prediction research that focuses on estimating the occupancy for various parking lots, which is critical to the coordination management of multiple parks (e.g., district-scale or city-scale), is relatively limited. This study aims to analyse the performance of different prediction methods with regard to parking occupancy, considering parking type and parking scale. Two forecasting methods, FM1 an

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Ye, Wei, Haoxuan Kuang, Xinjun Lai, and Jun Li. "A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms." Mathematics 11, no. 21 (2023): 4510. http://dx.doi.org/10.3390/math11214510.

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The near-future parking space availability is informative for the formulation of parking-related policy in urban areas. Plenty of studies have contributed to the spatial–temporal prediction for parking occupancy by considering the adjacency between parking lots. However, their similarities in properties remain unspecific. For example, parking lots with similar functions, though not adjacent, usually have similar patterns of occupancy changes, which can help with the prediction as well. To fill the gap, this paper proposes a multi-view and attention-based approach for spatial–temporal parking o

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Jin, Bowen, Yu Zhao, and Jing Ni. "Sustainable Transport in a Smart City: Prediction of Short-Term Parking Space through Improvement of LSTM Algorithm." Applied Sciences 12, no. 21 (2022): 11046. http://dx.doi.org/10.3390/app122111046.

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The carbon emission of fuel vehicles is a major consideration that affects the dual carbon goal in urban traffic. The problem of “difficult parking and disorderly parking” in static traffic can easily lead to traffic congestion, an increase in vehicle exhaust emissions, and air pollution. In particulate, when vehicles make an invalid detour and wait for parking with long hours, it often causes extra energy consumption and carbon emission. In this paper, adding a weather influence feature, a short-term parking occupancy rate prediction algorithm based on the long short-term model (LSTM) is prop

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M. S, Vinayprasad, Shreenath K. V, and Dasangam Gnaneswar. "Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review." December 2023 5, no. 4 (2023): 369–84. http://dx.doi.org/10.36548/jismac.2023.4.006.

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Major cities in India have a significant number of vehicles, and the rate of ownership is increasing every day. However, the lack of proper parking infrastructure in these cities causes problems such as difficulty in finding parking spaces. According to the Urban Mobility Survey 2023 by Times Network, nearly 74% of vehicle owners in metropolitan cities struggle to find a parking slot. Various measures have been implemented to address this issue. One of the most promising measures is a smart parking management system. This system can use technologies like Radio Frequency Identification (RFID) a

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Channamallu, Sai Sneha, Sharareh Kermanshachi, Jay Michael Rosenberger, and Apurva Pamidimukkala. "Parking occupancy prediction and analysis - a comprehensive study." Transportation Research Procedia 73 (2023): 297–304. http://dx.doi.org/10.1016/j.trpro.2023.11.921.

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Channamallu, Sai Sneha, Vijay Kumar Padavala, Sharareh Kermanshachi, Jay Michael Rosenberger, and Apurva Pamidimukkala. "Examining parking occupancy prediction models: a comparative analysis." Transportation Research Procedia 73 (2023): 281–88. http://dx.doi.org/10.1016/j.trpro.2023.11.919.

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Subapriya Vijayakumar and Rajaprakash Singaravelu. "Time Aware Long Short-Term Memory and Kronecker Gated Intelligent Transportation for Smart Car Parking." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (2024): 134–50. http://dx.doi.org/10.37934/araset.44.1.134150.

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Technology desires to improve quality of life and impart citizen’s health as well as happiness. The concept of Internet of Things (IoT) refers to smart world where prevailing objects are said to be embedded and hence interact with each other (i.e., between objects and human beings) to achieve an objective. In the period of IoT as well as smart city, there is requirement for Intelligent Transport System-based (ITS) ingenious smart parking or car parking space prediction (CPSP) for more feasible cities. With the increase in population and mushroom growth in vehicles are bringing about several di

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Qu, Haohao, Sheng Liu, Jun Li, Yuren Zhou, and Rui Liu. "Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction." Mathematics 10, no. 12 (2022): 2039. http://dx.doi.org/10.3390/math10122039.

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Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique,

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Xiao, Xiao, Zhiling Jin, Yilong Hui, Yueshen Xu, and Wei Shao. "Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction." Remote Sensing 13, no. 16 (2021): 3338. http://dx.doi.org/10.3390/rs13163338.

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With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and g

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Inam, Saba, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari, and Nazia Nawaz. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction." Sustainability 14, no. 12 (2022): 7317. http://dx.doi.org/10.3390/su14127317.

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Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has

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Ali, Ghulam, Tariq Ali, Muhammad Irfan, et al. "IoT Based Smart Parking System Using Deep Long Short Memory Network." Electronics 9, no. 10 (2020): 1696. http://dx.doi.org/10.3390/electronics9101696.

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Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform

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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.

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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the ne

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Ismail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi, and A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network." IOP Conference Series: Materials Science and Engineering 1176, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.

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Abstract In this study, we have investigated potential use of Multilayer Perceptron (MLP) to predict parking space availability for use within Field Programmable Gate Array (FPGA) accelerated embedded devices. While previous studies have explored the use of MLP for classification problem in FPGA, very little studies concentrated on the potential use of MLP in regression problem, especially in parking space forecasting. Therefore we formulated five Multi-Layer Perceptron (MLP) models with varying hidden units to perform single-step prediction to forecast parking space availability within the ne

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15

Bouhamed, Omar, Manar Amayri, and Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning." Sensors 22, no. 9 (2022): 3186. http://dx.doi.org/10.3390/s22093186.

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Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along wit

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Kytölä, Ulla, and Anssi Laaksonen. "Prediction of Restraint Moments in Precast, Prestressed Structures Made Continuous." Nordic Concrete Research 59, no. 1 (2018): 73–93. http://dx.doi.org/10.2478/ncr-2018-0016.

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Abstract This paper studies restraint moments developing in simple-span precast, prestressed beams made continuous. Methods of evaluating restraint moments produced by creep and differential shrinkage are presented. Shrinkage and creep properties of composite structures, beam and deck parts were tested and compared to values defined according to Eurocode models. Finally, the restraint moments were calculated with both material models for the two-span parking deck structure. The study confirmed the findings of previous studies: that the methods that are used overestimate the negative restraint

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17

Elomiya, Akram, Jiří Křupka, Stefan Jovčić, and Vladimir Simic. "Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models." Engineering Applications of Artificial Intelligence 129 (March 2024): 107670. http://dx.doi.org/10.1016/j.engappai.2023.107670.

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18

Pešić, Saša, Milenko Tošić, Ognjen Iković, Miloš Radovanović, Mirjana Ivanović, and Dragan Bošković. "BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction." International Journal on Artificial Intelligence Tools 28, no. 06 (2019): 1960005. http://dx.doi.org/10.1142/s0218213019600054.

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Running costs of buildings represent a significant outlay for all businesses, thus finding a way to run facilities as efficiently as possible is vital. IoT-enabled Building Management Systems provide means for process and resource usage automation leading to overall efficiency improvements. Inferring spatial and temporal occupancy in all its forms (binary, numerical or continuous) is one of the key contextual inputs required for smart building management systems. In this work, we showcase design, implementation and experimental validation of a smart building occupancy detection and forecasting

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19

Yang, Shuguan, Wei Ma, Xidong Pi, and Sean Qian. "A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources." Transportation Research Part C: Emerging Technologies 107 (October 2019): 248–65. http://dx.doi.org/10.1016/j.trc.2019.08.010.

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Niu, Zhipeng, Xiaowei Hu, Mahmudur Fatmi, et al. "Parking occupancy prediction under COVID-19 anti-pandemic policies: A model based on a policy-aware temporal convolutional network." Transportation Research Part A: Policy and Practice 176 (October 2023): 103832. http://dx.doi.org/10.1016/j.tra.2023.103832.

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21

Kasper-Eulaers, Margrit, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland, and Per Egil Kummervold. "Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5." Algorithms 14, no. 4 (2021): 114. http://dx.doi.org/10.3390/a14040114.

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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs

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22

Jabbar, Saba Qasim, and Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming." Journal of Engineering 27, no. 1 (2021): 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.

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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While

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Jabbar, Saba Qasim, and Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming." Journal of Engineering 27, no. 1 (2021): 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.

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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While

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Sprodowski, Tobias, and Jürgen Pannek. "Analytical Aspects of Distributed MPC Based on an Occupancy Grid for Mobile Robots." Applied Sciences 10, no. 3 (2020): 1007. http://dx.doi.org/10.3390/app10031007.

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In this paper, we evaluate theoretical aspects of a distributed system of noncooperative robots controlled by a distributed model predictive control scheme, which operates in a shared space. Here, for collision avoidance, the future predicted state trajectories are projected on a grid and exchanged via discrete cell indexes to reduce the communication burden. The predicted trajectories are obtained locally by each robot and carried out in the continuous space. Therefore, the quantisation does not impose the quality of the solution. We derive sufficient conditions to show convergence and practi

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Yu, Shanshan, and Hao Wang. "Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (May 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/5508623.

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This paper analyzes and studies the structure and parameters of the VGGNet network model and selects the most commonly used and efficient VGG-16 as the prototype of the improved model. A multiscale sampling layer is added at the end of the VGG-16 convolution part so that the model can input images of any size for training and testing while reducing the number of neurons in the fully connected layer. This improves the training speed of the model under the premise of ensuring the accuracy. This paper uses multisource street spatial data combined with geographic information spatial analysis techn

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Zhou, Junjie, Siyue Shuai, Lingyun Wang, et al. "Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs." Applied Sciences 12, no. 11 (2022): 5340. http://dx.doi.org/10.3390/app12115340.

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With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersectio

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Coleman, Sylvia, Marianne Touchie, John Robinson, and Terri Peters. "Rethinking Performance Gaps: A Regenerative Sustainability Approach to Built Environment Performance Assessment." Sustainability 10, no. 12 (2018): 4829. http://dx.doi.org/10.3390/su10124829.

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Globally, there are significant challenges to meeting built environment performance targets. The gaps found between the predicted performance of new or retrofit buildings and their actual performance impede an understanding of how to achieve these targets. This paper points to the importance of reliable and informative building performance assessments. We argue that if we are to make progress in achieving our climate goals, we need to reframe built environment performance with a shift to net positive goals, while recognising the equal importance of human and environmental outcomes. This paper

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Jacoby, Margarite, Sin Yong Tan, Mohamad Katanbaf, et al. "WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction." Journal of Sensor and Actuator Networks 10, no. 4 (2021): 71. http://dx.doi.org/10.3390/jsan10040071.

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Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful envir

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Khan, Arshad Mahmood, Qingting Li, Zafeer Saqib, et al. "MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia." Forests 13, no. 5 (2022): 715. http://dx.doi.org/10.3390/f13050715.

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Chilgoza pine is an economically and ecologically important evergreen coniferous tree species of the dry and rocky temperate zone, and a native of south Asia. This species is rated as near threatened (NT) by the International Union for Conservation of Nature (IUCN). This study hypothesized that climatic, soil and topographic variations strongly influence the distribution pattern and potential habitat suitability prediction of Chilgoza pine. Accordingly, this study was aimed to document the potential habitat suitability variations of Chilgoza pine under varying environmental scenarios by using

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Kitali, Angela E., Priyanka Alluri, Thobias Sando, and Wensong Wu. "Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (2019): 901–14. http://dx.doi.org/10.1177/0361198119849053.

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Secondary crashes (SCs) have increasingly been recognized as a major problem leading to reduced capacity and additional traffic delays. However, the limited knowledge on the nature and characteristics of SCs has largely impeded their mitigation strategies. There are two main issues with analyzing SCs. First, relevant variables are unknown, but, at the same time, most of the variables considered in the models are highly correlated. Second, only a small proportion of incidents results in SCs, making it an imbalanced classification problem. This study developed a reliable SC risk prediction model

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Tosin Michael Olatunde, Azubuike Chukwudi Okwandu, Dorcas Oluwajuwonlo Akande, and Zamathula Queen Sikhakhane. "REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION." Engineering Science & Technology Journal 5, no. 4 (2024): 1243–56. http://dx.doi.org/10.51594/estj.v5i4.1015.

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Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction o

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Schank, Cody J., Michael V. Cove, Marcella J. Kelly, et al. "A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir." Environmental Conservation 46, no. 03 (2019): 184–92. http://dx.doi.org/10.1017/s0376892919000055.

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SummarySpecies distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications a

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Rajeeve, Sridevi, Matt Wilkes, Nicole Zahradka, et al. "Early detection of CRS after CAR-T therapy using wearable monitoring devices: Preliminary results in relapsed/refractory multiple myeloma (RRMM)." Journal of Clinical Oncology 41, no. 16_suppl (2023): e13626-e13626. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13626.

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e13626 Background: Chimeric Antigen Receptor T-cell (CART) therapy is almost universally given inpatient due to risks of cytokine release syndrome (CRS). This burdens patients, increases bed occupancy, infection risk and costs. In an investigator initiated clinical trial (IIT), we evaluated the feasibility of using wearable devices for detecting CRS following autologous CART therapy in RRMM, in addition to standard of care (SoC). Reliable early CRS monitoring may help transition to outpatient CART. Methods: The remote wearable device (Current Health Inc.) was worn by patients (pts) as part of

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Chowdhury, Soumya, Parth Brahmaxatri, and J. Selvin Paul Peter. "Car parking occupancy prediction." International journal of health sciences, May 5, 2022, 6323–30. http://dx.doi.org/10.53730/ijhs.v6ns1.6954.

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Nowadays in modern cities, with the continuous growth of cars, parking slot availability is becoming a more and more difficult task. So, an efficient car parking occupancy detection system is becoming a necessity in order to reduce traffic congestion in parking lots. This paper proposes a system based on computer vision algorithms and basic image processing techniques that is capable of determining if a parking space is occupied or not, using aerial images captured through a camera. It aims to solve the issue of detecting a parking space that minimizes the time spent in searching parking lots

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Ye, Wei, Haoxuan Kuang, Jun Li, Xinjun Lai, and Haohao Qu. "A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism." IET Intelligent Transport Systems, October 10, 2023. http://dx.doi.org/10.1049/itr2.12433.

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AbstractParking occupancy prediction is an important reference for travel decisions and parking management. However, due to various related factors, such as commuting or traffic accidents, parking occupancy has complex change features that are difficult to model accurately, thus making it difficult for parking occupancy to be accurately predicted. Moreover, how to give appropriate weights to these changing features in prediction becomes a new challenge in the era of machine learning. To tackle these challenges, a parking occupancy prediction method called time series decomposition–long and sho

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SEBATLI SAĞLAM, Aslı, and Fatih ÇAVDUR. "PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS." Endüstri Mühendisliği, April 8, 2023. http://dx.doi.org/10.46465/endustrimuhendisligi.1241453.

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It may be critical for drivers to have information about the occupancy rates of the parking spaces around their destination in order to reduce the traffic density, a non-negligible part of which caused by the trips to find an available parking space. In this study, we predict parking occupancy rates (and thus, space availability) using three different techniques: (i) auto-regressive integrated moving average model, (ii) seasonal auto-regressive integrated moving average model and (iii) neural networks. In the implementation phase, we use the data set of the on-street parking spaces of the well

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Gutmann, Sebastian, Christoph Maget, Matthias Spangler, and Klaus Bogenberger. "Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion." Frontiers in Future Transportation 2 (July 2, 2021). http://dx.doi.org/10.3389/ffutr.2021.693708.

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For haul truck drivers it is becoming increasingly difficult to find appropriate parking at the end of a shift. Proper, legal, and safe overnight parking spots are crucial for truck drivers in order for them to be able to comply with Hours of Service regulation, reduce fatigue, and improve road safety. The lack of parking spaces affects the backbone of the economy because 70% of all United States domestic freight shipments (in terms of value) are transported by trucks. Many research projects provide real-time truck parking occupancy information at a given stop. However, truck drivers ultimatel

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Kasera, Rohit Kumar, and Tapodhir Acharjee. "Parking slot occupancy prediction using LSTM." Innovations in Systems and Software Engineering, September 10, 2022. http://dx.doi.org/10.1007/s11334-022-00481-3.

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ANAR, Yusuf Can, Ercan AVŞAR, and Abdurrahman Özgür POLAT. "Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods." MANAS Journal of Engineering, November 17, 2021. http://dx.doi.org/10.51354/mjen.986631.

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In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spo

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Shao, Wei, Yu Zhang, Pengfei Xiao, et al. "Transferrable contextual feature clusters for parking occupancy prediction." Pervasive and Mobile Computing, August 2023, 101831. http://dx.doi.org/10.1016/j.pmcj.2023.101831.

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41

Martín Calvo, Pablo, Bas Schotten, and Elenna R. Dugundji. "Assessing the Predictive Value of Traffic Count Data in the Imputation of On-Street Parking Occupancy in Amsterdam." Transportation Research Record: Journal of the Transportation Research Board, August 30, 2021, 036119812110296. http://dx.doi.org/10.1177/03611981211029644.

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On-street parking policies have a huge impact on the social welfare of citizens. Accurate parking occupancy data across time and space is required to properly set such policies. Different imputation and forecasting models are required to obtain this data in cities that use probe vehicle measurements, such as Amsterdam. In this paper, the usage of traffic data as an explanatory variable is assessed as a potential improvement to existing parking occupancy prediction models. Traffic counts were obtained from 164 traffic cameras throughout the city. Existing models for predicting parking occupancy

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42

Li, Jun, Haohao Qu, and Linlin You. "An Integrated Approach for the Near Real-Time Parking Occupancy Prediction." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–10. http://dx.doi.org/10.1109/tits.2022.3230199.

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43

Zeng, Chao, Changxi Ma, Ke Wang, and Zihao Cui. "Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3171330.

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Leobin Joseph, Ajay Krishna, Maschio Berty, Pramod P, and Velusamy A. "Advanced Parking Slot Management System Using Machine Learning." International Journal of Advanced Research in Science, Communication and Technology, April 26, 2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.

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The continuous development of economy, personal vehicles have become an indispensable part of our daily lives. The commodity has become affordable to most working class providing comfortable way of life; however on the other hand multiple problems strike back which need to be solved. One problem is of parking spaces. A variety of sophisticated car parking systems are in use nowadays; however they all require a considerable design time, installation and maintenance cost. In many parking areas the management uses the counter at the checkpoint in order to track the number of vehicle that enter an

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45

Leobin Joseph, Ajay Krishna, Maschio Berty, Pramod P, and Velusamy A. "Advanced Parking Slot Management System Using Machine Learning." International Journal of Advanced Research in Science, Communication and Technology, April 26, 2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.

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Abstract:

The continuous development of economy, personal vehicles have become an indispensable part of our daily lives. The commodity has become affordable to most working class providing comfortable way of life; however on the other hand multiple problems strike back which need to be solved. One problem is of parking spaces. A variety of sophisticated car parking systems are in use nowadays; however they all require a considerable design time, installation and maintenance cost. In many parking areas the management uses the counter at the checkpoint in order to track the number of vehicle that enter an

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46

Guerrero, Sebastian E., Shashank Pulikanti, Bridget Wieghart, Joseph G. Bryan, and Tim Strow. "Modeling Truck Parking Demand at Commercial and Industrial Establishments." Transportation Research Record: Journal of the Transportation Research Board, August 23, 2022, 036119812211035. http://dx.doi.org/10.1177/03611981221103597.

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In urban areas, there exists a mismatch between where trucks need to park and the availability of spaces, forcing many truck drivers to park in undesignated locations that are unsafe, disrupt traffic, and cause a nuisance to the community. This mismatch also decreases the productivity of the sector, as drivers spend more time searching for spaces, take longer detours, and end their workday early. Most commercial and industrial establishments currently do not allow trucks to park at their facilities beyond the loading or unloading of cargo, which generates parking demand in the surrounding area

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Lyu, Mengqi, Yanjie Ji, Chenchen Kuai, and Shuichao Zhang. "Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning." Journal of Traffic and Transportation Engineering (English Edition), January 2024. http://dx.doi.org/10.1016/j.jtte.2022.05.004.

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Errousso, Hanae, El Arbi Abdellaoui Alaoui, Siham Benhadou, and Hicham Medromi. "Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values." Progress in Artificial Intelligence, September 25, 2022. http://dx.doi.org/10.1007/s13748-022-00291-5.

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Balmer, Michael, Robert Weibel, and Haosheng Huang. "Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study." Geo-spatial Information Science, July 15, 2021, 1–20. http://dx.doi.org/10.1080/10095020.2021.1937337.

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Canlı, H., and S. Toklu. "Design and Implementation of a Prediction Approach Using Big Data and Deep Learning Techniques for Parking Occupancy." Arabian Journal for Science and Engineering, September 4, 2021. http://dx.doi.org/10.1007/s13369-021-06125-1.

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