Artigos de revistas sobre o tema "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, 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 fonteInam, Saba, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari e Nazia Nawaz. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction". Sustainability 14, n.º 12 (15 de junho de 2022): 7317. http://dx.doi.org/10.3390/su14127317.
Texto completo da fonteAli, Ghulam, Tariq Ali, Muhammad Irfan, Umar Draz, Muhammad Sohail, Adam Glowacz, Maciej Sulowicz, Ryszard Mielnik, Zaid Bin Faheem e Claudia Martis. "IoT Based Smart Parking System Using Deep Long Short Memory Network". Electronics 9, n.º 10 (15 de outubro de 2020): 1696. http://dx.doi.org/10.3390/electronics9101696.
Texto completo da fonteIsmail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi e A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network". IOP Conference Series: Materials Science and Engineering 1176, n.º 1 (1 de agosto de 2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Texto completo da fonteIsmail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi e A. Abdul-Aziz. "Predicting vehicle parking space availability using multilayer perceptron neural network". IOP Conference Series: Materials Science and Engineering 1176, n.º 1 (1 de agosto de 2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Texto completo da fonteBouhamed, Omar, Manar Amayri e Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning". Sensors 22, n.º 9 (21 de abril de 2022): 3186. http://dx.doi.org/10.3390/s22093186.
Texto completo da fonteKytölä, Ulla, e Anssi Laaksonen. "Prediction of Restraint Moments in Precast, Prestressed Structures Made Continuous". Nordic Concrete Research 59, n.º 1 (1 de dezembro de 2018): 73–93. http://dx.doi.org/10.2478/ncr-2018-0016.
Texto completo da fonteElomiya, Akram, Jiří Křupka, Stefan Jovčić e 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 (março de 2024): 107670. http://dx.doi.org/10.1016/j.engappai.2023.107670.
Texto completo da fontePešić, Saša, Milenko Tošić, Ognjen Iković, Miloš Radovanović, Mirjana Ivanović e Dragan Bošković. "BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction". International Journal on Artificial Intelligence Tools 28, n.º 06 (setembro de 2019): 1960005. http://dx.doi.org/10.1142/s0218213019600054.
Texto completo da fonteYang, Shuguan, Wei Ma, Xidong Pi e 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 (outubro de 2019): 248–65. http://dx.doi.org/10.1016/j.trc.2019.08.010.
Texto completo da fonteNiu, Zhipeng, Xiaowei Hu, Mahmudur Fatmi, Shouming Qi, Siqing Wang, Haihua Yang e 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 (outubro de 2023): 103832. http://dx.doi.org/10.1016/j.tra.2023.103832.
Texto completo da fonteKasper-Eulaers, Margrit, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland e Per Egil Kummervold. "Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5". Algorithms 14, n.º 4 (31 de março de 2021): 114. http://dx.doi.org/10.3390/a14040114.
Texto completo da fonteJabbar, Saba Qasim, e Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming". Journal of Engineering 27, n.º 1 (1 de janeiro de 2021): 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.
Texto completo da fonteJabbar, Saba Qasim, e Dheyaa Jasim Kadhim. "A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming". Journal of Engineering 27, n.º 1 (1 de janeiro de 2021): 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.
Texto completo da fonteSprodowski, Tobias, e Jürgen Pannek. "Analytical Aspects of Distributed MPC Based on an Occupancy Grid for Mobile Robots". Applied Sciences 10, n.º 3 (4 de fevereiro de 2020): 1007. http://dx.doi.org/10.3390/app10031007.
Texto completo da fonteYu, Shanshan, e Hao Wang. "Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network". Computational Intelligence and Neuroscience 2022 (11 de maio de 2022): 1–12. http://dx.doi.org/10.1155/2022/5508623.
Texto completo da fonteZhou, Junjie, Siyue Shuai, Lingyun Wang, Kaifeng Yu, Xiangjie Kong, Zuhua Xu e Zhijiang Shao. "Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs". Applied Sciences 12, n.º 11 (25 de maio de 2022): 5340. http://dx.doi.org/10.3390/app12115340.
Texto completo da fonteColeman, Sylvia, Marianne Touchie, John Robinson e Terri Peters. "Rethinking Performance Gaps: A Regenerative Sustainability Approach to Built Environment Performance Assessment". Sustainability 10, n.º 12 (18 de dezembro de 2018): 4829. http://dx.doi.org/10.3390/su10124829.
Texto completo da fonteJacoby, Margarite, Sin Yong Tan, Mohamad Katanbaf, Ali Saffari, Homagni Saha, Zerina Kapetanovic, Jasmine Garland et al. "WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction". Journal of Sensor and Actuator Networks 10, n.º 4 (6 de dezembro de 2021): 71. http://dx.doi.org/10.3390/jsan10040071.
Texto completo da fonteKhan, Arshad Mahmood, Qingting Li, Zafeer Saqib, Nasrullah Khan, Tariq Habib, Nadia Khalid, Muhammad Majeed e 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, n.º 5 (2 de maio de 2022): 715. http://dx.doi.org/10.3390/f13050715.
Texto completo da fonteKitali, Angela E., Priyanka Alluri, Thobias Sando e Wensong Wu. "Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model". Transportation Research Record: Journal of the Transportation Research Board 2673, n.º 11 (24 de junho de 2019): 901–14. http://dx.doi.org/10.1177/0361198119849053.
Texto completo da fonteTosin Michael Olatunde, Azubuike Chukwudi Okwandu, Dorcas Oluwajuwonlo Akande e Zamathula Queen Sikhakhane. "REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION". Engineering Science & Technology Journal 5, n.º 4 (10 de abril de 2024): 1243–56. http://dx.doi.org/10.51594/estj.v5i4.1015.
Texto completo da fonteSchank, Cody J., Michael V. Cove, Marcella J. Kelly, Clayton K. Nielsen, Georgina O’Farrill, Ninon Meyer, Christopher A. Jordan 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, n.º 03 (19 de julho de 2019): 184–92. http://dx.doi.org/10.1017/s0376892919000055.
Texto completo da fonteRajeeve, Sridevi, Matt Wilkes, Nicole Zahradka, Kseniya Serebyrakova, Katerina Kappes, Hayley Jackson, Nicole Buchenholz 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, n.º 16_suppl (1 de junho de 2023): e13626-e13626. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13626.
Texto completo da fonteChowdhury, Soumya, Parth Brahmaxatri e J. Selvin Paul Peter. "Car parking occupancy prediction". International journal of health sciences, 5 de maio de 2022, 6323–30. http://dx.doi.org/10.53730/ijhs.v6ns1.6954.
Texto completo da fonteYe, Wei, Haoxuan Kuang, Jun Li, Xinjun Lai e Haohao Qu. "A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism". IET Intelligent Transport Systems, 10 de outubro de 2023. http://dx.doi.org/10.1049/itr2.12433.
Texto completo da fonteSEBATLI SAĞLAM, Aslı, e Fatih ÇAVDUR. "PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS". Endüstri Mühendisliği, 8 de abril de 2023. http://dx.doi.org/10.46465/endustrimuhendisligi.1241453.
Texto completo da fonteGutmann, Sebastian, Christoph Maget, Matthias Spangler e Klaus Bogenberger. "Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion". Frontiers in Future Transportation 2 (2 de julho de 2021). http://dx.doi.org/10.3389/ffutr.2021.693708.
Texto completo da fonteKasera, Rohit Kumar, e Tapodhir Acharjee. "Parking slot occupancy prediction using LSTM". Innovations in Systems and Software Engineering, 10 de setembro de 2022. http://dx.doi.org/10.1007/s11334-022-00481-3.
Texto completo da fonteANAR, Yusuf Can, Ercan AVŞAR e Abdurrahman Özgür POLAT. "Parking Lot Occupancy Prediction Using Long Short-Term Memory and Statistical Methods". MANAS Journal of Engineering, 17 de novembro de 2021. http://dx.doi.org/10.51354/mjen.986631.
Texto completo da fonteShao, Wei, Yu Zhang, Pengfei Xiao, Kyle Kai Qin, Mohammad Saiedur Rahaman, Jeffrey Chan, Bin Guo, Andy Song e Flora D. Salim. "Transferrable contextual feature clusters for parking occupancy prediction". Pervasive and Mobile Computing, agosto de 2023, 101831. http://dx.doi.org/10.1016/j.pmcj.2023.101831.
Texto completo da fonteMartín Calvo, Pablo, Bas Schotten e 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 de agosto de 2021, 036119812110296. http://dx.doi.org/10.1177/03611981211029644.
Texto completo da fonteLi, Jun, Haohao Qu e 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.
Texto completo da fonteZeng, Chao, Changxi Ma, Ke Wang e 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.
Texto completo da fonteLeobin Joseph, Ajay Krishna, Maschio Berty, Pramod P e Velusamy A. "Advanced Parking Slot Management System Using Machine Learning". International Journal of Advanced Research in Science, Communication and Technology, 26 de abril de 2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.
Texto completo da fonteLeobin Joseph, Ajay Krishna, Maschio Berty, Pramod P e Velusamy A. "Advanced Parking Slot Management System Using Machine Learning". International Journal of Advanced Research in Science, Communication and Technology, 26 de abril de 2022, 497–502. http://dx.doi.org/10.48175/ijarsct-3299.
Texto completo da fonteGuerrero, Sebastian E., Shashank Pulikanti, Bridget Wieghart, Joseph G. Bryan e Tim Strow. "Modeling Truck Parking Demand at Commercial and Industrial Establishments". Transportation Research Record: Journal of the Transportation Research Board, 23 de agosto de 2022, 036119812211035. http://dx.doi.org/10.1177/03611981221103597.
Texto completo da fonteLyu, Mengqi, Yanjie Ji, Chenchen Kuai e 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), janeiro de 2024. http://dx.doi.org/10.1016/j.jtte.2022.05.004.
Texto completo da fonteErrousso, Hanae, El Arbi Abdellaoui Alaoui, Siham Benhadou e Hicham Medromi. "Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values". Progress in Artificial Intelligence, 25 de setembro de 2022. http://dx.doi.org/10.1007/s13748-022-00291-5.
Texto completo da fonteBalmer, Michael, Robert Weibel e Haosheng Huang. "Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study". Geo-spatial Information Science, 15 de julho de 2021, 1–20. http://dx.doi.org/10.1080/10095020.2021.1937337.
Texto completo da fonteCanlı, H., e 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 de setembro de 2021. http://dx.doi.org/10.1007/s13369-021-06125-1.
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