Auswahl der wissenschaftlichen Literatur zum Thema „Continuous parking occupancy prediction“
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Zeitschriftenartikel zum Thema "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, Nr. 03 (23.03.2024): 1–5. http://dx.doi.org/10.55041/ijsrem29597.
Der volle Inhalt der QuelleZhao, Ziyao, Yi Zhang und Yi Zhang. „A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale“. Journal of Advanced Transportation 2020 (14.02.2020): 1–12. http://dx.doi.org/10.1155/2020/5624586.
Der volle Inhalt der QuelleYe, Wei, Haoxuan Kuang, Xinjun Lai und Jun Li. „A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms“. Mathematics 11, Nr. 21 (01.11.2023): 4510. http://dx.doi.org/10.3390/math11214510.
Der volle Inhalt der QuelleJin, Bowen, Yu Zhao und Jing Ni. „Sustainable Transport in a Smart City: Prediction of Short-Term Parking Space through Improvement of LSTM Algorithm“. Applied Sciences 12, Nr. 21 (31.10.2022): 11046. http://dx.doi.org/10.3390/app122111046.
Der volle Inhalt der QuelleM. S, Vinayprasad, Shreenath K. V und Dasangam Gnaneswar. „Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review“. December 2023 5, Nr. 4 (Dezember 2023): 369–84. http://dx.doi.org/10.36548/jismac.2023.4.006.
Der volle Inhalt der QuelleChannamallu, Sai Sneha, Sharareh Kermanshachi, Jay Michael Rosenberger und 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.
Der volle Inhalt der QuelleChannamallu, Sai Sneha, Vijay Kumar Padavala, Sharareh Kermanshachi, Jay Michael Rosenberger und 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.
Der volle Inhalt der QuelleSubapriya Vijayakumar und 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, Nr. 1 (26.04.2024): 134–50. http://dx.doi.org/10.37934/araset.44.1.134150.
Der volle Inhalt der QuelleQu, Haohao, Sheng Liu, Jun Li, Yuren Zhou und Rui Liu. „Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction“. Mathematics 10, Nr. 12 (12.06.2022): 2039. http://dx.doi.org/10.3390/math10122039.
Der volle Inhalt der QuelleXiao, Xiao, Zhiling Jin, Yilong Hui, Yueshen Xu und Wei Shao. „Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction“. Remote Sensing 13, Nr. 16 (23.08.2021): 3338. http://dx.doi.org/10.3390/rs13163338.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleDeep 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, und 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.
Der volle Inhalt der QuelleChen-HaoLiao und 廖振豪. „A Smart Parking Allocation Approach based on Parking Occupancy Prediction“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2j6a4g.
Der volle Inhalt der QuelleBuchteile zum Thema "Continuous parking occupancy prediction"
Lu, Eric Hsueh-Chan, und 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.
Der volle Inhalt der QuelleQu, Haohao, Sheng Liu, Zihan Guo, Linlin You und 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.
Der volle Inhalt der QuelleHe, Jian, und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Continuous parking occupancy prediction"
Srotyr, Martin, Michal Jerabek, Zdenek Lokaj und 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.
Der volle Inhalt der QuelleStojanovic, Nikola, Vladan Damjanovic und 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.
Der volle Inhalt der QuelleMufida, Miratul Khusna, Abdessamad Ait El Cadi, Thierry Delot und 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.
Der volle Inhalt der QuelleCervi, Thales, Luiz Melo Jr., Myriam Delgado, Semida Silveira und 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.
Der volle Inhalt der QuelleResce, Pierpaolo, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, Mirco Nanni, Luca Pappalardo, Daniel Weimer und 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.
Der volle Inhalt der QuelleRai, Anjani Kumar, Parul Madan, Irfan Khan, V. Divya Vani, Rajeev Singh und 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.
Der volle Inhalt der QuelleFarag, Mohamed, Amr Hilal und 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.
Der volle Inhalt der QuelleSaharan, Sandeep, Seema Baway und 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.
Der volle Inhalt der QuelleJose, Anu, und 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.
Der volle Inhalt der QuelleEider, Markus, Nicki Bodenschatz und 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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