Artykuły w czasopismach na temat „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, nr 03 (23.03.2024): 1–5. http://dx.doi.org/10.55041/ijsrem29597.
Pełny tekst źródłaZhao, Ziyao, Yi Zhang i 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.
Pełny tekst źródłaYe, Wei, Haoxuan Kuang, Xinjun Lai i Jun Li. "A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms". Mathematics 11, nr 21 (1.11.2023): 4510. http://dx.doi.org/10.3390/math11214510.
Pełny tekst źródłaJin, Bowen, Yu Zhao i 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.
Pełny tekst źródłaM. S, Vinayprasad, Shreenath K. V i Dasangam Gnaneswar. "Finding the Spot: IoT enabled Smart Parking Technologies for Occupancy Monitoring – A Comprehensive Review". December 2023 5, nr 4 (grudzień 2023): 369–84. http://dx.doi.org/10.36548/jismac.2023.4.006.
Pełny tekst źródłaChannamallu, Sai Sneha, Sharareh Kermanshachi, Jay Michael Rosenberger i 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.
Pełny tekst źródłaChannamallu, Sai Sneha, Vijay Kumar Padavala, Sharareh Kermanshachi, Jay Michael Rosenberger i 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.
Pełny tekst źródłaSubapriya Vijayakumar i 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.
Pełny tekst źródłaQu, Haohao, Sheng Liu, Jun Li, Yuren Zhou i 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.
Pełny tekst źródłaXiao, Xiao, Zhiling Jin, Yilong Hui, Yueshen Xu i 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.
Pełny tekst źródłaInam, Saba, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari i Nazia Nawaz. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction". Sustainability 14, nr 12 (15.06.2022): 7317. http://dx.doi.org/10.3390/su14127317.
Pełny tekst źródłaAli, Ghulam, Tariq Ali, Muhammad Irfan, Umar Draz, Muhammad Sohail, Adam Glowacz, Maciej Sulowicz, Ryszard Mielnik, Zaid Bin Faheem i Claudia Martis. "IoT Based Smart Parking System Using Deep Long Short Memory Network". Electronics 9, nr 10 (15.10.2020): 1696. http://dx.doi.org/10.3390/electronics9101696.
Pełny tekst źródłaIsmail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi i A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network". IOP Conference Series: Materials Science and Engineering 1176, nr 1 (1.08.2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Pełny tekst źródłaIsmail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi i A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network". IOP Conference Series: Materials Science and Engineering 1176, nr 1 (1.08.2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Pełny tekst źródłaBouhamed, Omar, Manar Amayri i Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning". Sensors 22, nr 9 (21.04.2022): 3186. http://dx.doi.org/10.3390/s22093186.
Pełny tekst źródłaKytölä, Ulla, i Anssi Laaksonen. "Prediction of Restraint Moments in Precast, Prestressed Structures Made Continuous". Nordic Concrete Research 59, nr 1 (1.12.2018): 73–93. http://dx.doi.org/10.2478/ncr-2018-0016.
Pełny tekst źródłaElomiya, Akram, Jiří Křupka, Stefan Jovčić i 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 (marzec 2024): 107670. http://dx.doi.org/10.1016/j.engappai.2023.107670.
Pełny tekst źródłaPešić, Saša, Milenko Tošić, Ognjen Iković, Miloš Radovanović, Mirjana Ivanović i Dragan Bošković. "BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction". International Journal on Artificial Intelligence Tools 28, nr 06 (wrzesień 2019): 1960005. http://dx.doi.org/10.1142/s0218213019600054.
Pełny tekst źródłaYang, Shuguan, Wei Ma, Xidong Pi i 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 (październik 2019): 248–65. http://dx.doi.org/10.1016/j.trc.2019.08.010.
Pełny tekst źródłaNiu, Zhipeng, Xiaowei Hu, Mahmudur Fatmi, Shouming Qi, Siqing Wang, Haihua Yang i Shi An. "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 (październik 2023): 103832. http://dx.doi.org/10.1016/j.tra.2023.103832.
Pełny tekst źródłaKasper-Eulaers, Margrit, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland i Per Egil Kummervold. "Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5". Algorithms 14, nr 4 (31.03.2021): 114. http://dx.doi.org/10.3390/a14040114.
Pełny tekst źródłaJabbar, Saba Qasim, i Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming". Journal of Engineering 27, nr 1 (1.01.2021): 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.
Pełny tekst źródłaJabbar, Saba Qasim, i Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming". Journal of Engineering 27, nr 1 (1.01.2021): 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.
Pełny tekst źródłaSprodowski, Tobias, i Jürgen Pannek. "Analytical Aspects of Distributed MPC Based on an Occupancy Grid for Mobile Robots". Applied Sciences 10, nr 3 (4.02.2020): 1007. http://dx.doi.org/10.3390/app10031007.
Pełny tekst źródłaYu, Shanshan, i Hao Wang. "Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network". Computational Intelligence and Neuroscience 2022 (11.05.2022): 1–12. http://dx.doi.org/10.1155/2022/5508623.
Pełny tekst źródłaZhou, Junjie, Siyue Shuai, Lingyun Wang, Kaifeng Yu, Xiangjie Kong, Zuhua Xu i Zhijiang Shao. "Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs". Applied Sciences 12, nr 11 (25.05.2022): 5340. http://dx.doi.org/10.3390/app12115340.
Pełny tekst źródłaColeman, Sylvia, Marianne Touchie, John Robinson i Terri Peters. "Rethinking Performance Gaps: A Regenerative Sustainability Approach to Built Environment Performance Assessment". Sustainability 10, nr 12 (18.12.2018): 4829. http://dx.doi.org/10.3390/su10124829.
Pełny tekst źródłaJacoby, Margarite, Sin Yong Tan, Mohamad Katanbaf, Ali Saffari, Homagni Saha, Zerina Kapetanovic, Jasmine Garland i in. "WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction". Journal of Sensor and Actuator Networks 10, nr 4 (6.12.2021): 71. http://dx.doi.org/10.3390/jsan10040071.
Pełny tekst źródłaKhan, Arshad Mahmood, Qingting Li, Zafeer Saqib, Nasrullah Khan, Tariq Habib, Nadia Khalid, Muhammad Majeed i Aqil Tariq. "MaxEnt Modelling and Impact of Climate Change on Habitat Suitability Variations of Economically Important Chilgoza Pine (Pinus gerardiana Wall.) in South Asia". Forests 13, nr 5 (2.05.2022): 715. http://dx.doi.org/10.3390/f13050715.
Pełny tekst źródłaKitali, Angela E., Priyanka Alluri, Thobias Sando i Wensong Wu. "Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model". Transportation Research Record: Journal of the Transportation Research Board 2673, nr 11 (24.06.2019): 901–14. http://dx.doi.org/10.1177/0361198119849053.
Pełny tekst źródłaTosin Michael Olatunde, Azubuike Chukwudi Okwandu, Dorcas Oluwajuwonlo Akande i Zamathula Queen Sikhakhane. "REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION". Engineering Science & Technology Journal 5, nr 4 (10.04.2024): 1243–56. http://dx.doi.org/10.51594/estj.v5i4.1015.
Pełny tekst źródłaSchank, Cody J., Michael V. Cove, Marcella J. Kelly, Clayton K. Nielsen, Georgina O’Farrill, Ninon Meyer, Christopher A. Jordan i in. "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, nr 03 (19.07.2019): 184–92. http://dx.doi.org/10.1017/s0376892919000055.
Pełny tekst źródłaRajeeve, Sridevi, Matt Wilkes, Nicole Zahradka, Kseniya Serebyrakova, Katerina Kappes, Hayley Jackson, Nicole Buchenholz i in. "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, nr 16_suppl (1.06.2023): e13626-e13626. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13626.
Pełny tekst źródłaChowdhury, Soumya, Parth Brahmaxatri i J. Selvin Paul Peter. "Car parking occupancy prediction". International journal of health sciences, 5.05.2022, 6323–30. http://dx.doi.org/10.53730/ijhs.v6ns1.6954.
Pełny tekst źródłaYe, Wei, Haoxuan Kuang, Jun Li, Xinjun Lai i Haohao Qu. "A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism". IET Intelligent Transport Systems, 10.10.2023. http://dx.doi.org/10.1049/itr2.12433.
Pełny tekst źródłaSEBATLI SAĞLAM, Aslı, i Fatih ÇAVDUR. "PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS". Endüstri Mühendisliği, 8.04.2023. http://dx.doi.org/10.46465/endustrimuhendisligi.1241453.
Pełny tekst źródłaGutmann, Sebastian, Christoph Maget, Matthias Spangler i Klaus Bogenberger. "Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion". Frontiers in Future Transportation 2 (2.07.2021). http://dx.doi.org/10.3389/ffutr.2021.693708.
Pełny tekst źródłaKasera, Rohit Kumar, i Tapodhir Acharjee. "Parking slot occupancy prediction using LSTM". Innovations in Systems and Software Engineering, 10.09.2022. http://dx.doi.org/10.1007/s11334-022-00481-3.
Pełny tekst źródłaANAR, Yusuf Can, Ercan AVŞAR i Abdurrahman Özgür POLAT. "Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods". MANAS Journal of Engineering, 17.11.2021. http://dx.doi.org/10.51354/mjen.986631.
Pełny tekst źródłaShao, Wei, Yu Zhang, Pengfei Xiao, Kyle Kai Qin, Mohammad Saiedur Rahaman, Jeffrey Chan, Bin Guo, Andy Song i Flora D. Salim. "Transferrable contextual feature clusters for parking occupancy prediction". Pervasive and Mobile Computing, sierpień 2023, 101831. http://dx.doi.org/10.1016/j.pmcj.2023.101831.
Pełny tekst źródłaMartín Calvo, Pablo, Bas Schotten i 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, 30.08.2021, 036119812110296. http://dx.doi.org/10.1177/03611981211029644.
Pełny tekst źródłaLi, Jun, Haohao Qu i 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.
Pełny tekst źródłaZeng, Chao, Changxi Ma, Ke Wang i 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.
Pełny tekst źródłaLeobin Joseph, Ajay Krishna, Maschio Berty, Pramod P i Velusamy A. "Advanced Parking Slot Management System Using Machine Learning". International Journal of Advanced Research in Science, Communication and Technology, 26.04.2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.
Pełny tekst źródłaLeobin Joseph, Ajay Krishna, Maschio Berty, Pramod P i Velusamy A. "Advanced Parking Slot Management System Using Machine Learning". International Journal of Advanced Research in Science, Communication and Technology, 26.04.2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.
Pełny tekst źródłaGuerrero, Sebastian E., Shashank Pulikanti, Bridget Wieghart, Joseph G. Bryan i Tim Strow. "Modeling Truck Parking Demand at Commercial and Industrial Establishments". Transportation Research Record: Journal of the Transportation Research Board, 23.08.2022, 036119812211035. http://dx.doi.org/10.1177/03611981221103597.
Pełny tekst źródłaLyu, Mengqi, Yanjie Ji, Chenchen Kuai i 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), styczeń 2024. http://dx.doi.org/10.1016/j.jtte.2022.05.004.
Pełny tekst źródłaErrousso, Hanae, El Arbi Abdellaoui Alaoui, Siham Benhadou i Hicham Medromi. "Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values". Progress in Artificial Intelligence, 25.09.2022. http://dx.doi.org/10.1007/s13748-022-00291-5.
Pełny tekst źródłaBalmer, Michael, Robert Weibel i Haosheng Huang. "Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study". Geo-spatial Information Science, 15.07.2021, 1–20. http://dx.doi.org/10.1080/10095020.2021.1937337.
Pełny tekst źródłaCanlı, H., i 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, 4.09.2021. http://dx.doi.org/10.1007/s13369-021-06125-1.
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