Deep Learning and Bayesian Calibration Approach to Hourly Passenger Occupancy Prediction in Beijing Metro: A Study Exploiting Cellular Data and Metro Conditions
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Pengpeng Jiao & Ruimin Li & Tuo Sun & Zenghao Hou & Amir Ibrahim, 2016. "Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, March.
- Petris, Giovanni, 2010. "An R Package for Dynamic Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i12).
- Richard A. Davis & Konstantinos Fokianos & Scott H. Holan & Harry Joe & James Livsey & Robert Lund & Vladas Pipiras & Nalini Ravishanker, 2021. "Count Time Series: A Methodological Review," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1533-1547, May.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Huaping Chen & Qi Li & Fukang Zhu, 2023. "A covariate-driven beta-binomial integer-valued GARCH model for bounded counts with an application," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(7), pages 805-826, October.
- Stefano Cabras, 2021. "A Bayesian-Deep Learning Model for Estimating COVID-19 Evolution in Spain," Mathematics, MDPI, vol. 9(22), pages 1-18, November.
- Lu Zeng & Zinuo Li & Jie Yang & Xinyue Xu, 2022. "CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
- Karmakar, Sayar & Gupta, Rangan & Cepni, Oguzhan & Rognone, Lavinia, 2023.
"Climate risks and predictability of the trading volume of gold: Evidence from an INGARCH model,"
Resources Policy, Elsevier, vol. 82(C).
- Sayar Karmakar & Rangan Gupta & Oguzhan Cepni & Lavinia Rognone, 2022. "Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model," Working Papers 202241, University of Pretoria, Department of Economics.
- Rostami-Tabar, Bahman & Disney, Stephen M., 2023. "On the order-up-to policy with intermittent integer demand and logically consistent forecasts," International Journal of Production Economics, Elsevier, vol. 257(C).
- Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
- Shao, Wei & Guo, Guangbao & Meng, Fanyu & Jia, Shuqin, 2013. "An efficient proposal distribution for Metropolis–Hastings using a B-splines technique," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 465-478.
- Ekici, Oya & Nemlioğlu, Karun, 2017. "Emerging economies’ short-term private external debt as evidence of economic crisis," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 232-246.
- Peter Knaus & Angela Bitto-Nemling & Annalisa Cadonna & Sylvia Fruhwirth-Schnatter, 2019. "Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP," Papers 1907.07065, arXiv.org, revised Nov 2020.
- Kleyton da Costa & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro Coelho & Andr'e de Melo Modenesi, 2020. "A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy," Papers 2010.13259, arXiv.org, revised Mar 2022.
- Andrés Ramírez Hassan & Javier Pantoja Robayo, 2013. "Co-movements between Latin American and U.S. stock markets: convergence after the financial crisis," Documentos de Trabajo de Valor Público 10931, Universidad EAFIT.
- Cabral, Celso Rômulo Barbosa & da-Silva, Cibele Queiroz & Migon, Helio S., 2014. "A dynamic linear model with extended skew-normal for the initial distribution of the state parameter," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 64-80.
- Zhang, Qian & Liu, Xiaoxiao & Spurgeon, Sarah & Yu, Dingli, 2021. "A two-layer modelling framework for predicting passenger flow on trains: A case study of London underground trains," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 119-139.
- Xianwang Li & Zhongxiang Huang & Saihu Liu & Jinxin Wu & Yuxiang Zhang, 2023. "Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)," Sustainability, MDPI, vol. 15(10), pages 1-30, May.
- Cabras, Stefano & Sunhe, Flor, 2021. "A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro," DES - Working Papers. Statistics and Econometrics. WS 33787, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Younghoon Kim & Zachary F. Fisher & Vladas Pipiras, 2023. "Latent Gaussian dynamic factor modeling and forecasting for multivariate count time series," Papers 2307.10454, arXiv.org.
- Yang, Kai & Yu, Xinyang & Zhang, Qingqing & Dong, Xiaogang, 2022. "On MCMC sampling in self-exciting integer-valued threshold time series models," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
- Jiajie Kong & Robert Lund, 2023. "Seasonal count time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 93-124, January.
- Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
- Huaping Chen & Fukang Zhu & Xiufang Liu, 2022. "A New Bivariate INAR(1) Model with Time-Dependent Innovation Vectors," Stats, MDPI, vol. 5(3), pages 1-22, August.
More about this item
Keywords
Bayesian Model Calibration;NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2023-11-27 (Big Data)
- NEP-TRE-2023-11-27 (Transport Economics)
- NEP-URE-2023-11-27 (Urban and Real Estate Economics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:38783. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.