Zeitschriftenartikel zum Thema „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.
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 QuelleInam, Saba, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari und 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.
Der volle Inhalt der QuelleAli, Ghulam, Tariq Ali, Muhammad Irfan, Umar Draz, Muhammad Sohail, Adam Glowacz, Maciej Sulowicz, Ryszard Mielnik, Zaid Bin Faheem und 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.
Der volle Inhalt der QuelleIsmail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi und A. Abdul-Aziz. „Predicting vehicle parking space availability using multilayer perceptron neural network“. IOP Conference Series: Materials Science and Engineering 1176, Nr. 1 (01.08.2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Der volle Inhalt der QuelleIsmail, M. H., T. R. Razak, R. A. J. M. Gining, S. S. M. Fauzi und A. Abdul-Aziz. „Predicting vehicle parking space availability using multilayer perceptron neural network“. IOP Conference Series: Materials Science and Engineering 1176, Nr. 1 (01.08.2021): 012035. http://dx.doi.org/10.1088/1757-899x/1176/1/012035.
Der volle Inhalt der QuelleBouhamed, Omar, Manar Amayri und 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.
Der volle Inhalt der QuelleKytölä, Ulla, und Anssi Laaksonen. „Prediction of Restraint Moments in Precast, Prestressed Structures Made Continuous“. Nordic Concrete Research 59, Nr. 1 (01.12.2018): 73–93. http://dx.doi.org/10.2478/ncr-2018-0016.
Der volle Inhalt der QuelleElomiya, Akram, Jiří Křupka, Stefan Jovčić und 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 (März 2024): 107670. http://dx.doi.org/10.1016/j.engappai.2023.107670.
Der volle Inhalt der QuellePešić, Saša, Milenko Tošić, Ognjen Iković, Miloš Radovanović, Mirjana Ivanović und Dragan Bošković. „BLEMAT: Data Analytics and Machine Learning for Smart Building Occupancy Detection and Prediction“. International Journal on Artificial Intelligence Tools 28, Nr. 06 (September 2019): 1960005. http://dx.doi.org/10.1142/s0218213019600054.
Der volle Inhalt der QuelleYang, Shuguan, Wei Ma, Xidong Pi und 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 (Oktober 2019): 248–65. http://dx.doi.org/10.1016/j.trc.2019.08.010.
Der volle Inhalt der QuelleNiu, Zhipeng, Xiaowei Hu, Mahmudur Fatmi, Shouming Qi, Siqing Wang, Haihua Yang und 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 (Oktober 2023): 103832. http://dx.doi.org/10.1016/j.tra.2023.103832.
Der volle Inhalt der QuelleKasper-Eulaers, Margrit, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland und 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.
Der volle Inhalt der QuelleJabbar, Saba Qasim, und Dheyaa Jasim Kadhim. „A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming“. Journal of Engineering 27, Nr. 1 (01.01.2021): 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.
Der volle Inhalt der QuelleJabbar, Saba Qasim, und Dheyaa Jasim Kadhim. „A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming“. Journal of Engineering 27, Nr. 1 (01.01.2021): 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.
Der volle Inhalt der QuelleSprodowski, Tobias, und Jürgen Pannek. „Analytical Aspects of Distributed MPC Based on an Occupancy Grid for Mobile Robots“. Applied Sciences 10, Nr. 3 (04.02.2020): 1007. http://dx.doi.org/10.3390/app10031007.
Der volle Inhalt der QuelleYu, Shanshan, und 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.
Der volle Inhalt der QuelleZhou, Junjie, Siyue Shuai, Lingyun Wang, Kaifeng Yu, Xiangjie Kong, Zuhua Xu und 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.
Der volle Inhalt der QuelleColeman, Sylvia, Marianne Touchie, John Robinson und 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.
Der volle Inhalt der QuelleJacoby, 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, Nr. 4 (06.12.2021): 71. http://dx.doi.org/10.3390/jsan10040071.
Der volle Inhalt der QuelleKhan, Arshad Mahmood, Qingting Li, Zafeer Saqib, Nasrullah Khan, Tariq Habib, Nadia Khalid, Muhammad Majeed und 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 (02.05.2022): 715. http://dx.doi.org/10.3390/f13050715.
Der volle Inhalt der QuelleKitali, Angela E., Priyanka Alluri, Thobias Sando und 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.
Der volle Inhalt der QuelleTosin Michael Olatunde, Azubuike Chukwudi Okwandu, Dorcas Oluwajuwonlo Akande und 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.
Der volle Inhalt der QuelleSchank, 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, Nr. 03 (19.07.2019): 184–92. http://dx.doi.org/10.1017/s0376892919000055.
Der volle Inhalt der QuelleRajeeve, 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, Nr. 16_suppl (01.06.2023): e13626-e13626. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e13626.
Der volle Inhalt der QuelleChowdhury, Soumya, Parth Brahmaxatri und J. Selvin Paul Peter. „Car parking occupancy prediction“. International journal of health sciences, 05.05.2022, 6323–30. http://dx.doi.org/10.53730/ijhs.v6ns1.6954.
Der volle Inhalt der QuelleYe, Wei, Haoxuan Kuang, Jun Li, Xinjun Lai und 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.
Der volle Inhalt der QuelleSEBATLI SAĞLAM, Aslı, und Fatih ÇAVDUR. „PREDICTION OF PARKING SPACE AVAILABILITY USING ARIMA AND NEURAL NETWORKS“. Endüstri Mühendisliği, 08.04.2023. http://dx.doi.org/10.46465/endustrimuhendisligi.1241453.
Der volle Inhalt der QuelleGutmann, Sebastian, Christoph Maget, Matthias Spangler und Klaus Bogenberger. „Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion“. Frontiers in Future Transportation 2 (02.07.2021). http://dx.doi.org/10.3389/ffutr.2021.693708.
Der volle Inhalt der QuelleKasera, Rohit Kumar, und 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.
Der volle Inhalt der QuelleANAR, Yusuf Can, Ercan AVŞAR und 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.
Der volle Inhalt der QuelleShao, Wei, Yu Zhang, Pengfei Xiao, Kyle Kai Qin, Mohammad Saiedur Rahaman, Jeffrey Chan, Bin Guo, Andy Song und Flora D. Salim. „Transferrable contextual feature clusters for parking occupancy prediction“. Pervasive and Mobile Computing, August 2023, 101831. http://dx.doi.org/10.1016/j.pmcj.2023.101831.
Der volle Inhalt der QuelleMartín Calvo, Pablo, Bas Schotten und 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.
Der volle Inhalt der QuelleLi, Jun, Haohao Qu und 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.
Der volle Inhalt der QuelleZeng, Chao, Changxi Ma, Ke Wang und 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.
Der volle Inhalt der QuelleLeobin Joseph, Ajay Krishna, Maschio Berty, Pramod P und 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.
Der volle Inhalt der QuelleLeobin Joseph, Ajay Krishna, Maschio Berty, Pramod P und 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.
Der volle Inhalt der QuelleGuerrero, Sebastian E., Shashank Pulikanti, Bridget Wieghart, Joseph G. Bryan und 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.
Der volle Inhalt der QuelleLyu, Mengqi, Yanjie Ji, Chenchen Kuai und 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), Januar 2024. http://dx.doi.org/10.1016/j.jtte.2022.05.004.
Der volle Inhalt der QuelleErrousso, Hanae, El Arbi Abdellaoui Alaoui, Siham Benhadou und 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.
Der volle Inhalt der QuelleBalmer, Michael, Robert Weibel und 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.
Der volle Inhalt der QuelleCanlı, H., und 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, 04.09.2021. http://dx.doi.org/10.1007/s13369-021-06125-1.
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