Relevant bibliographies by topics / Continuous parking occupancy prediction

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Academic literature on the topic 'Continuous parking occupancy prediction'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Continuous parking occupancy prediction"

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Khandhar, Aangi B. "A Review on Parking Occupancy Prediction and Pattern Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 23, 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 management, and enhanced user experience. Through the integration of predictive analytics, decision-makers can anticipate parking demand, optimize parking space utilization, and alleviate congestion in urban areas.This project explores the application of data- driven approaches to address challenges in parking management, including predicting peak parking times, identifying trends in parking occupancy, and optimizing parking infrastructure. By harnessing the power of data analysis, the project aims to enhance urban mobility, reduce environmental impact, and improve overall quality of life. Keywords: Parking occupancy prediction, Pattern analysis, Urban management systems, Data analysis techniques, Machine learning algorithms, Traffic management, Urban planning.
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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 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. To compare the predictive performances of these models, real-world data of four parks in Shenzhen, Shanghai, and Dongguan were collected over 8 weeks to estimate the correlation between the parking lot attributes and forecast results. As per the case studies, among the four models considered, SVM offers stable and accurate prediction performance for almost all types and scales of parking lots. For commercial, mixed functional, and large-scale parking lots, FM1 with SVM made the best prediction. For office and medium-scale parking lots, FM2 with SVM made the best prediction.
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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 (November 1, 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 occupancy prediction, namely hybrid graph convolution network with long short-term memory and temporal pattern attention (HGLT). In addition to the local view of adjacency, we construct a similarity matrix using the Pearson correlation coefficient between parking lots as the global view. Then, we design an integrated neural network focusing on graph structure and temporal pattern to assign proper weights to the different spatial features in both views. Comprehensive evaluations on a real-world dataset show that HGLT reduces prediction error by about 30.14% on average compared to other state-of-the-art models. Moreover, it is demonstrated that the global view is effective in predicting parking occupancy.
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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 (October 31, 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 proposed. The data used in this model is from Melbourne public data sets, and parking occupancy rates are predicted through historical parking data, weather information, and location information. At the same time, three commonly prediction models, i.e., simple LSTM model, multiple linear regression model (MLR), and support vector regression (SVR), are also used as comparison models. Taking MAE and RMSE as evaluation indexes, the parking occupancy rate during 10 min, 20 min, and 30 min are predicted. The experimental results show that the improved LSTM method achieves better accuracy and stability in the prediction of parking lots. The average MAE and RMSE of the proposed LSTM prediction during intervals of 10 min, 20 min, and 30 min with the weather influence feature algorithm is lower than that of simple LSTM, MLR and that of SVR.
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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 (December 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) and Automatic License Plate Recognition (ALPR) to make check-in and check-out easier. It can also include Wireless Sensor Networks (WSN), wired sensors, or visual occupancy detection to provide real-time occupancy status. The smart parking management system can offer useful services through mobile or web applications such as parking occupancy monitoring, reservation, payment gateway, occupancy prediction, automated check-in and check-out, and parking record management. The purpose of this review paper is to summarize the works undertaken in the field of smart IoT parking systems and educate the technological community about the technologies, features, and procedures for implementing the smart parking management system. In the paper, we aim to summarise the works on occupancy monitoring in smart IoT parking systems addressing the advantages and issues of the present occupancy monitoring methods, also suggesting future inclusions for smart IoT parking systems.
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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 (April 26, 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 distinct economic as well as environmental issues. One of pivotal ones is optimal parking space identification. To address on this problem, in this work, Time-aware Long Short-Term Memory and Kronecker product Gated Recurrent Unit (TLSTM-KGRU) for smart parking in internet of transportation things is proposed. The TLSTM-KGRU method is split into two sections. In the first section, smart parking occupancy is derived using Time-aware Long Short-Term Memory (Time-aware LSTM) for Kuala Lumpur Convention Centre car parking sensor dataset. Following which the resultant smart car occupancy results are subjected to Linear Interpolations and Kronecker product Gated Recurrent Unit for predicting smart parking. When compared against other predictive methods such as SGRU-LSTM and CPSP using DELM, our experimental outcomes denote that TLSTM-KGRU method has improved performance for smart parking occupancy forecast as it not only enhances sensitivity and specificity but also reduces prediction time with minimum delay.
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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 (June 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, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102× faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.
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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 (August 23, 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 gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.
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Dissertations / Theses on the topic "Continuous parking occupancy prediction"

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Mufida, Miratul Khusna. "Deep learning for continuous parking occupancy forecasting in urban environments." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0024.

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L'apprentissage profond a été largement adopté dans divers domaines en raison de sa capacité à extraire des caractéristiques complexes à partir de grandes quantités de données. Dans cette thèse, nous proposons une approche basée sur l'apprentissage profond pour la prédiction continue de l'occupation des parkiL'apprentissage profond a été largement adopté dans divers domaines en raison de sa capacité à extraire des caractéristiques complexes à partir de grandes quantités de données. Dans cette thèse, nous proposons une approche basée sur l'apprentissage profond pour la prédiction continue de l'occupation des parkings. Nous avons donc collecté un large ensemble de données sur l'occupation des parkings (tant pour les parkings couverts que pour les parkings en bord de rue) provenant de différentes villes de deux pays différents et les avons utilisées pour entraîner des modèles de réseaux neuronaux profonds. Nos expériences montrent que l'approche proposée surpasse les modèles classiques et basés sur l'apprentissage machine en termes de précision des prévisions et de performances en temps réel. De plus, notre approche peut également être facilement intégrée aux systèmes de stationnement intelligents existants pour améliorer leur efficacité et leur commodité.Pour un déploiement au niveau de la ville, nous proposons également un cadre permettant de partager les modèles entre les parkings en analysant leur similarité spatiale et temporelle. En identifiant les caractéristiques spatiales et temporelles pertinentes de chaque parking (profil de stationnement) et en les regroupant en conséquence, notre approche permet le développement de modèles précis de prévision de l'occupation pour un ensemble de parkings, ce qui permet de réduire les coûts de calcul et d'améliorer la transférabilité des modèles. Nos expériences démontrent l'efficacité de la stratégie proposée en termes de réduction des coûts de déploiement des modèles tout en maintenant une bonne qualité des prévisions.En conclusion, ce travail démontre l'efficacité de l'apprentissage profond pour résoudre le problème de la prévision continue de l'occupation des parkings et met en évidence son potentiel pour les futures applications de stationnement intelligentes
Deep learning has been widely adopted in various fields for its ability to extract complex features from large amounts of data. In this thesis, we propose a deep learning-based approach for continuous parking occupancy prediction. We therefore collected a large dataset of parking occupancy data (for both off-street and on-street parking) from various cities in two different countries and used it to train deep neural network models. Our experiments show that the proposed approach outperforms classical and machine learning baseline models in terms of forecast accuracy and real-time performance. Furthermore, our approach can also be easily integrated into existing smart parking systems to improve their efficiency and convenience. For a city-level deployment, we also propose a framework for sharing models amongst parking lots by analyzing their spatial and temporal profiles similarity. By identifying the relevant spatial and temporal characteristics of each parking lot (parking profile) and grouping them accordingly, our approach allows the development of accurate occupancy forecasting models for a set of parking lots, thereby reducing computational costs and improving model transferability. Our experiments demonstrate the effectiveness of the proposed strategy in reducing model deployment costs while maintaining a good quality of the forecast. In conclusion, this work demonstrates the effectiveness of deep learning in addressing the problem of continuous parking occupancy forecasting and highlights its potential for future smart parking applications
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Nilsson, Mathias, and Corswant Sophie von. "How Certain Are You of Getting a Parking Space? : A deep learning approach to parking availability prediction." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166989.

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Traffic congestion is a severe problem in urban areas and it leads to the emission of greenhouse gases and air pollution. In general, drivers lack knowledge of the location and availability of free parking spaces in urban cities. This leads to people driving around searching for parking places, and about one-third of traffic congestion in cities is due to drivers searching for an available parking lot. In recent years, various solutions to provide parking information ahead have been proposed. The vast majority of these solutions have been applied in large cities, such as Beijing and San Francisco. This thesis has been conducted in collaboration with Knowit and Dukaten to predict parking occupancy in car parks one hour ahead in the relatively small city of Linköping. To make the predictions, this study has investigated the possibility to use long short-term memory and gradient boosting regression trees, trained on historical parking data. To enhance decision making, the predictive uncertainty was estimated using the novel approach Monte Carlo dropout for the former, and quantile regression for the latter. This study reveals that both of the models can predict parking occupancy ahead of time and they are found to excel in different contexts. The inclusion of exogenous features can improve prediction quality. More specifically, we found that incorporating hour of the day improved the models’ performances, while weather features did not contribute much. As for uncertainty, the employed method Monte Carlo dropout was shown to be sensitive to parameter tuning to obtain good uncertainty estimates.
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Chen-HaoLiao and 廖振豪. "A Smart Parking Allocation Approach based on Parking Occupancy Prediction." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2j6a4g.

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Book chapters on the topic "Continuous parking occupancy prediction"

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Lu, Eric Hsueh-Chan, and Chen-Hao Liao. "A Parking Occupancy Prediction Approach Based on Spatial and Temporal Analysis." In Intelligent Information and Database Systems, 500–509. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75417-8_47.

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Qu, Haohao, Sheng Liu, Zihan Guo, Linlin You, and Jun Li. "Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn." In Knowledge Science, Engineering and Management, 159–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_13.

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He, Jian, and Jiahao Bai. "Prediction Technology for Parking Occupancy Based on Multi-dimensional Spatial-Temporal Causality and ANN Algorithm." In Green, Pervasive, and Cloud Computing, 244–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64243-3_19.

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Conference papers on the topic "Continuous parking occupancy prediction"

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Srotyr, Martin, Michal Jerabek, Zdenek Lokaj, and Tomas Zelinka. "Prediction system of occupancy of parking spaces." In 2015 Smart Cities Symposium Prague (SCSP). IEEE, 2015. http://dx.doi.org/10.1109/scsp.2015.7181543.

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Stojanovic, Nikola, Vladan Damjanovic, and Srdan Vukmirovic. "Parking Occupancy Prediction using Computer Vision with Location Awareness." In 2021 20th International Symposium INFOTEH-JAHORINA (INFOTEH). IEEE, 2021. http://dx.doi.org/10.1109/infoteh51037.2021.9400669.

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Mufida, Miratul Khusna, Abdessamad Ait El Cadi, Thierry Delot, and Martin Trépanier. "Towards a continuous forecasting mechanism of parking occupancy in urban environments." In IDEAS 2021: 25th International Database Engineering & Applications Symposium. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472163.3472265.

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Cervi, Thales, Luiz Melo Jr., Myriam Delgado, Semida Silveira, and Ricardo Lüders. "Prediction of Car Parking Occupancy in Urban Areas Using Geostatistics." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2022. http://dx.doi.org/10.5753/sbsi_estendido.2022.222975.

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Public on-street car parking is an important shared resource of a city infrastructure with a significant impact on traffic. This paper proposes a geostatistical model aimed to predict parking occupancy rates for different periods of the day. In the study case, the occupancy representation considers the georeferenced position of spots for a particular area of Los Angeles (USA). Different models are compared and their parameters are estimated using the available dataset of the parking area. The final model is chosen to generate a kriging map that helps to understand and predict the occupancy rates. The end goal is to open doors for modeling and predicting urban phenomenons with Geostatistics to help with planning public parking policies in high density urban areas.
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Resce, Pierpaolo, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, Mirco Nanni, Luca Pappalardo, Daniel Weimer, and Yuanting Liu. "Connected vehicle simulation framework for parking occupancy prediction (demo paper)." In SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3557915.3560995.

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Rai, Anjani Kumar, Parul Madan, Irfan Khan, V. Divya Vani, Rajeev Singh, and Narendra Singh. "Parking Slot Estimation and Occupancy Prediction using LSTM and CNN." In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2023. http://dx.doi.org/10.1109/ic3i59117.2023.10397745.

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Farag, Mohamed, Amr Hilal, and Samy El-Tawab. "Parking Occupancy Prediction and Traffic Assignment in a University Environment." In 2022 10th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC). IEEE, 2022. http://dx.doi.org/10.1109/jac-ecc56395.2022.10044079.

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Saharan, Sandeep, Seema Baway, and Neeraj Kumarz. "OP3 S: On-Street Occupancy based Parking Prices Prediction System for ITS." In 2020 IEEE Globecom Workshops (GC Wkshps). IEEE, 2020. http://dx.doi.org/10.1109/gcwkshps50303.2020.9367433.

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Jose, Anu, and Vidya V. "A Stacked Long Short-Term Memory Neural Networks for Parking Occupancy Rate Prediction." In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021. http://dx.doi.org/10.1109/csnt51715.2021.9509621.

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Eider, Markus, Nicki Bodenschatz, and Andreas Berl. "Evaluation of Machine Learning Algorithms for the Prediction of Simulated Company Parking Space Occupancy." In 2021 11th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2021. http://dx.doi.org/10.1109/acit52158.2021.9548487.

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