Literatura científica selecionada sobre o tema "Continuous parking occupancy prediction"
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Artigos de revistas sobre o assunto "Continuous parking occupancy prediction"
Khandhar, Aangi B. "A Review on Parking Occupancy Prediction and Pattern Analysis". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 03 (23 de março de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29597.
Texto completo da fonteZhao, Ziyao, Yi Zhang e Yi Zhang. "A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale". Journal of Advanced Transportation 2020 (14 de fevereiro de 2020): 1–12. http://dx.doi.org/10.1155/2020/5624586.
Texto completo da fonteYe, Wei, Haoxuan Kuang, Xinjun Lai e Jun Li. "A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms". Mathematics 11, n.º 21 (1 de novembro de 2023): 4510. http://dx.doi.org/10.3390/math11214510.
Texto completo da fonteJin, Bowen, Yu Zhao e Jing Ni. "Sustainable Transport in a Smart City: Prediction of Short-Term Parking Space through Improvement of LSTM Algorithm". Applied Sciences 12, n.º 21 (31 de outubro de 2022): 11046. http://dx.doi.org/10.3390/app122111046.
Texto completo da fonteM. S, Vinayprasad, Shreenath K. V e Dasangam Gnaneswar. "Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review". December 2023 5, n.º 4 (dezembro de 2023): 369–84. http://dx.doi.org/10.36548/jismac.2023.4.006.
Texto completo da fonteChannamallu, Sai Sneha, Sharareh Kermanshachi, Jay Michael Rosenberger e 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.
Texto completo da fonteChannamallu, Sai Sneha, Vijay Kumar Padavala, Sharareh Kermanshachi, Jay Michael Rosenberger e 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.
Texto completo da fonteSubapriya Vijayakumar e 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, n.º 1 (26 de abril de 2024): 134–50. http://dx.doi.org/10.37934/araset.44.1.134150.
Texto completo da fonteQu, Haohao, Sheng Liu, Jun Li, Yuren Zhou e Rui Liu. "Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction". Mathematics 10, n.º 12 (12 de junho de 2022): 2039. http://dx.doi.org/10.3390/math10122039.
Texto completo da fonteXiao, Xiao, Zhiling Jin, Yilong Hui, Yueshen Xu e Wei Shao. "Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction". Remote Sensing 13, n.º 16 (23 de agosto de 2021): 3338. http://dx.doi.org/10.3390/rs13163338.
Texto completo da fonteTeses / dissertações sobre o assunto "Continuous parking occupancy prediction"
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.
Texto completo da fonteDeep 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
Nilsson, Mathias, e 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.
Texto completo da fonteChen-HaoLiao e 廖振豪. "A Smart Parking Allocation Approach based on Parking Occupancy Prediction". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2j6a4g.
Texto completo da fonteCapítulos de livros sobre o assunto "Continuous parking occupancy prediction"
Lu, Eric Hsueh-Chan, e 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.
Texto completo da fonteQu, Haohao, Sheng Liu, Zihan Guo, Linlin You e 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.
Texto completo da fonteHe, Jian, e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Continuous parking occupancy prediction"
Srotyr, Martin, Michal Jerabek, Zdenek Lokaj e 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.
Texto completo da fonteStojanovic, Nikola, Vladan Damjanovic e 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.
Texto completo da fonteMufida, Miratul Khusna, Abdessamad Ait El Cadi, Thierry Delot e 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.
Texto completo da fonteCervi, Thales, Luiz Melo Jr., Myriam Delgado, Semida Silveira e 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.
Texto completo da fonteResce, Pierpaolo, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, Mirco Nanni, Luca Pappalardo, Daniel Weimer e 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.
Texto completo da fonteRai, Anjani Kumar, Parul Madan, Irfan Khan, V. Divya Vani, Rajeev Singh e 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.
Texto completo da fonteFarag, Mohamed, Amr Hilal e 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.
Texto completo da fonteSaharan, Sandeep, Seema Baway e 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.
Texto completo da fonteJose, Anu, e 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.
Texto completo da fonteEider, Markus, Nicki Bodenschatz e 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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