{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:22:00Z","timestamp":1773807720394,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2-3","license":[{"start":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T00:00:00Z","timestamp":1654300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T00:00:00Z","timestamp":1654300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sign Process Syst"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11265-022-01774-3","type":"journal-article","created":{"date-parts":[[2022,6,4]],"date-time":"2022-06-04T08:02:45Z","timestamp":1654329765000},"page":"281-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Prediction of Bus Passenger Traffic using Gaussian Process Regression"],"prefix":"10.1007","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7077-7390","authenticated-orcid":false,"given":"Vidya","family":"G S","sequence":"first","affiliation":[]},{"given":"Hari","family":"V S","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,4]]},"reference":[{"key":"1774_CR1","doi-asserted-by":"crossref","unstructured":"Pavlyuk, D. (2017). Short-term traffic forecasting using multivariate autoregressive models. Procedia Engineering, 178, 57\u201366.","DOI":"10.1016\/j.proeng.2017.01.062"},{"key":"1774_CR2","doi-asserted-by":"crossref","unstructured":"Rodriguez-Deniz, H., Jenelius, E., & Villani, M. (2017). Urban network travel time prediction via online multi-output gaussian process regression. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1\u20136). IEEE.","DOI":"10.1109\/ITSC.2017.8317796"},{"key":"1774_CR3","doi-asserted-by":"crossref","unstructured":"Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013). Predicting taxi\u2013passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems, 14, 1393\u20131402.","DOI":"10.1109\/TITS.2013.2262376"},{"key":"1774_CR4","doi-asserted-by":"crossref","unstructured":"Xue, R., Sun, D.\u00a0J., & Chen, S. (2015). Short-term bus passenger demand prediction based on time series model and interactive multiple model approach. Discrete Dynamics in Nature and Society, 2015.","DOI":"10.1155\/2015\/682390"},{"key":"1774_CR5","doi-asserted-by":"crossref","unstructured":"Feng, G. (2015). Network traffic prediction based on neural network. In 2015 International Conference on Intelligent Transportation, Big Data and Smart City (pp. 527\u2013530). IEEE.","DOI":"10.1109\/ICITBS.2015.136"},{"key":"1774_CR6","doi-asserted-by":"crossref","unstructured":"Terry, N., & Choe, Y. (2021). Splitting gaussian processes for computationally-efficient regression. PLOS ONE, 16, 1\u201317.","DOI":"10.1371\/journal.pone.0256470"},{"key":"1774_CR7","doi-asserted-by":"crossref","unstructured":"Maritz, J., Lubbe, F., & Lagrange, L. (2018). A practical guide to gaussian process regression for energy measurement and verification within the bayesian framework. Energies, 11.","DOI":"10.3390\/en11040935"},{"key":"1774_CR8","unstructured":"Quinonero\u00a0Candela, J., & Rasmussen, C. (2005). A unifying view of sparse approximate gaussian process regression. Journal of Machine Learning Research, 6, 1935\u20131959."},{"key":"1774_CR9","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xu, W., Yin, F., Lin, J., & Cui, S. (2017). High-accuracy wireless traffic prediction: A gp-based machine learning approach. In GLOBECOM 2017 - 2017 IEEE Global Communications Conference (pp. 1\u20136).","DOI":"10.1109\/GLOCOM.2017.8254808"},{"key":"1774_CR10","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Lu, Y., & Xu, J. (2022). Gaussian process dynamic modeling and backstepping sliding mode control for magnetic levitation system of maglev train1. Journal of Theoretical and Applied Mechanics, (pp. 49\u201362).","DOI":"10.15632\/jtam-pl\/143676"},{"key":"1774_CR11","doi-asserted-by":"crossref","unstructured":"Subrahmanyam, K.\u00a0V., Ramsenthil, C., Girach\u00a0Imran, A., Chakravorty, A., Sreedhar, R., Ezhilrajan, E., Bala\u00a0Subrahamanyam, D., Ramachandran, R., Kumar, K.\u00a0K., Rajasekhar, M. et\u00a0al. (2021). Prediction of heavy rainfall days over a peninsular indian station using the machine learning algorithms. Journal of Earth System Science, 130, 1\u20139.","DOI":"10.1007\/s12040-021-01725-9"},{"key":"1774_CR12","doi-asserted-by":"crossref","unstructured":"Zazoum, B. (2021). Solar photovoltaic power prediction using different machine learning methods. Energy Reports, .","DOI":"10.1016\/j.egyr.2021.11.183"},{"key":"1774_CR13","doi-asserted-by":"crossref","unstructured":"Pooja, W., Snehal, N., Sonam, K., Wagh, S., & Singh, N. (2021). Effect of increased number of covid-19 tests using supervised machine learning models. In 2021 Australian & New Zealand Control Conference (ANZCC) (pp. 131\u2013136). IEEE.","DOI":"10.1109\/ANZCC53563.2021.9628387"},{"key":"1774_CR14","doi-asserted-by":"crossref","unstructured":"Topaloglu, B., Kaya, G.\u00a0T., Sutcu, F., & Deger, Z.\u00a0T. (2021). Machine learning-based assessment of energy behavior of rc shear walls. arXiv preprint arXiv:2111.08295, .","DOI":"10.1016\/j.istruc.2022.08.114"},{"key":"1774_CR15","doi-asserted-by":"crossref","unstructured":"Goudarzi, S., Kama, N., Anisi, M.\u00a0H., Zeadally, S., & Mumtaz, S. (2019). Data collection using unmanned aerial vehicles for internet of things platforms. Computers & Electrical Engineering, 75, 1\u201315.","DOI":"10.1016\/j.compeleceng.2019.01.028"},{"key":"1774_CR16","doi-asserted-by":"crossref","unstructured":"Mumtaz, S., Lundqvist, H., Huq, K. M.\u00a0S., Rodriguez, J., & Radwan, A. (2014). Smart direct-lte communication: An energy saving perspective. Ad Hoc Networks, 13, 296\u2013311.","DOI":"10.1016\/j.adhoc.2013.08.008"},{"key":"1774_CR17","doi-asserted-by":"crossref","unstructured":"Duan, W., Gu, J., Wen, M., Zhang, G., Ji, Y., & Mumtaz, S. (2020). Emerging technologies for 5g-iov networks: Applications, trends and opportunities. IEEE Network, 34, 283\u2013289.","DOI":"10.1109\/MNET.001.1900659"},{"key":"1774_CR18","doi-asserted-by":"crossref","unstructured":"Hu, J., Li, X., & Ou, Y. (2014). Online gaussian process regression for time-varying manufacturing systems. In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) (pp. 1118\u20131123). IEEE.","DOI":"10.1109\/ICARCV.2014.7064462"},{"key":"1774_CR19","doi-asserted-by":"crossref","unstructured":"Bayati, A., Asghari, V., Nguyen, K., & Cheriet, M. (2016). Gaussian process regression based traffic modeling and prediction in high-speed networks. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1\u20137).","DOI":"10.1109\/GLOCOM.2016.7841857"},{"key":"1774_CR20","doi-asserted-by":"crossref","unstructured":"Hoque, K.\u00a0E., & Aljamaan, H. (2021). Impact of hyperparameter tuning on machine learning models in stock price forecasting. IEEE Access, 9, 163815\u2013163830.","DOI":"10.1109\/ACCESS.2021.3134138"},{"key":"1774_CR21","doi-asserted-by":"crossref","unstructured":"Andugula, P., Durbha, S.\u00a0S., Lokhande, A., & Suradhaniwar, S. (2017). Gaussian process based spatial modeling of soil moisture for dense soil moisture sensing network. In 2017 6th International Conference on Agro-Geoinformatics (pp. 1\u20135).","DOI":"10.1109\/Agro-Geoinformatics.2017.8047014"},{"key":"1774_CR22","doi-asserted-by":"crossref","unstructured":"Ghasemi, P., Karbasi, M., Zamani Nouri, A., Sarai Tabrizi, M., & Azamathulla, H.\u00a0M. (2021). Application of gaussian process regression to forecast multi-step ahead spei drought index. Alexandria Engineering Journal, 60, 5375\u20135392.","DOI":"10.1016\/j.aej.2021.04.022"},{"key":"1774_CR23","doi-asserted-by":"crossref","unstructured":"Cai, H., Jia, X., Feng, J., Li, W., Hsu, Y.-M., & Lee, J. (2019). Gaussian process regression for numerical wind speed prediction enhancement. Renewable Energy, 146.","DOI":"10.1016\/j.renene.2019.08.018"},{"key":"1774_CR24","doi-asserted-by":"crossref","unstructured":"Raissi, M., Babaee, H., & Karniadakis, G.\u00a0E. (2019). Parametric Gaussian process regression for big data. Computational Mechanics, 64, 409\u2013416.","DOI":"10.1007\/s00466-019-01711-5"},{"key":"1774_CR25","unstructured":"Graas, R., Sun, J., & Hoekstra, J. (2021). Quantifying accuracy and uncertainty in data-driven flight trajectory predictions with gaussian process regression. In 11th SESAR Innovation Days."},{"key":"1774_CR26","unstructured":"Xie, G., & Chen, X. (2021). Efficient and robust online trajectory prediction for non-cooperative unmanned aerial vehicles. Journal of Aerospace Information Systems, (pp. 1\u201311)."},{"key":"1774_CR27","doi-asserted-by":"crossref","unstructured":"Rong, H., Teixeira, A., & Guedes Soares, C. (2022). Maritime traffic probabilistic prediction based on ship motion pattern extraction. Reliability Engineering and System Safety, 217, 108061.","DOI":"10.1016\/j.ress.2021.108061"},{"key":"1774_CR28","doi-asserted-by":"crossref","unstructured":"Ak\u00e7ay, M.\u00a0T., Akgundogdu, A., & Ti\u0307ryaki\u0307, H. (2021). Estimation of the average speed for a railway signaling system by using gaussian process regression methods with bayesian optimization. Demiryolu M\u00fchendisli\u011fi, 14, 274\u2013286.","DOI":"10.47072\/demiryolu.942730"},{"key":"1774_CR29","doi-asserted-by":"crossref","unstructured":"Xie, H., Hu, D., & Song, K. (2021). An iterative optimization algorithm for vehicle speed prediction considering driving style and historical data effects. In 2021 40th Chinese Control Conference (CCC) (pp. 6094\u20136100).","DOI":"10.23919\/CCC52363.2021.9550541"},{"key":"1774_CR30","doi-asserted-by":"crossref","unstructured":"Soldevila, I.\u00a0E., Knoop, V.\u00a0L., & Hoogendoorn, S. (2021). Car-following described by blending data-driven and analytical models: a gaussian process regression approach. Transportation research record, 2675, 1202\u20131213.","DOI":"10.1177\/03611981211032648"},{"key":"1774_CR31","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.\u00a0E. (2003). Gaussian processes in machine learning. In Summer school on machine learning (pp. 63\u201371). Springer.","DOI":"10.1007\/978-3-540-28650-9_4"},{"key":"1774_CR32","doi-asserted-by":"crossref","unstructured":"Lin, C., Li, T., Chen, S., Liu, X., Lin, C., & Liang, S. (2019). Gaussian process regression-based forecasting model of dam deformation. Neural Computing and Applications, 31.","DOI":"10.1007\/s00521-019-04375-7"},{"key":"1774_CR33","doi-asserted-by":"crossref","unstructured":"Church, K., & Gale, W. (1995). Poisson mixtures. Natural Language Engineering, 1.","DOI":"10.1017\/S1351324900000139"},{"key":"1774_CR34","doi-asserted-by":"crossref","unstructured":"Mihaylova, L., Boel, R., & HEGYI, A. (2006). An unscented kalman filter for freeway traffic estimation. In H.\u00a0Van\u00a0Zuylen, & F.\u00a0Middelham (Eds.), Proceedings of 11th IFAC Symposium on Control in Transportation Systems (pp. 31\u201336).","DOI":"10.3182\/20060829-3-NL-2908.00006"},{"key":"1774_CR35","doi-asserted-by":"crossref","unstructured":"Mihaylova, L., & Boel, R. (2004). A particle filter for freeway traffic estimation. In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) (pp. 2106\u20132111 Vol.2). volume\u00a02.","DOI":"10.1109\/CDC.2004.1430359"},{"key":"1774_CR36","unstructured":"Thrun, S., Burgard, W., Fox, D., & Arkin, R. (2005). Probabilistic Robotics. Intelligent Robotics and Autonomous Agents series. MIT Press."},{"key":"1774_CR37","unstructured":"Osvaldo., M., & Safari (2018). Bayesian Analysis with Python. Intelligent Robotics and Autonomous Agents series. Packt Publishing Ltd."},{"key":"1774_CR38","doi-asserted-by":"crossref","unstructured":"Fletcher, R., & Powell, M. J.\u00a0D. (1963). A Rapidly Convergent Descent Method for Minimization. The Computer Journal, 6, 163\u2013168.","DOI":"10.1093\/comjnl\/6.2.163"},{"key":"1774_CR39","doi-asserted-by":"crossref","unstructured":"Gelman, A., Carlin, J.\u00a0B., Stern, H.\u00a0S., & Rubin, D.\u00a0B. (1995). Bayesian data analysis. Chapman and Hall\/CRC.","DOI":"10.1201\/9780429258411"},{"key":"1774_CR40","unstructured":"Wipf, D., & Nagarajan, S. (2007). A new view of automatic relevance determination. Advances in neural information processing systems, 20."},{"key":"1774_CR41","doi-asserted-by":"crossref","unstructured":"Fang, K.-T., Kotz, S., & Ng, K.\u00a0W. (2018). Symmetric multivariate and related distributions. Chapman and Hall\/CRC.","DOI":"10.1201\/9781351077040"},{"key":"1774_CR42","unstructured":"Archambeau, C., & Bach, F. (2011). Multiple gaussian process models. arXiv preprint arXiv:1110.5238."},{"key":"1774_CR43","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yan, F., & Qi, Y. (2011). Sparse matrix-variate t process blockmodels. Proceedings of the AAAI Conference on Artificial Intelligence, 25.","DOI":"10.1609\/aaai.v25i1.7919"},{"key":"1774_CR44","doi-asserted-by":"crossref","unstructured":"Yu, S., Tresp, V., & Yu, K. (2007). Robust multi-task learning with t-processes. In Proceedings of the 24th International Conference on Machine Learning (p. 1103\u20131110). Association for Computing Machinery.","DOI":"10.1145\/1273496.1273635"},{"key":"1774_CR45","unstructured":"Zhang, Y., & Yeung, D. (2010). Multi-task learning using generalized t process. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (pp. 964\u2013971). PMLR volume\u00a09 of Proceedings of Machine Learning Research."},{"key":"1774_CR46","doi-asserted-by":"crossref","unstructured":"Douak, F., Melgani, F., & Benoudjit, N. (2013). Kernel ridge regression with active learning for wind speed prediction. Applied energy, 103, 328\u2013340.","DOI":"10.1016\/j.apenergy.2012.09.055"},{"key":"1774_CR47","doi-asserted-by":"crossref","unstructured":"Stuke, A., Todorovi\u0107, M., Rupp, M., Kunkel, C., Ghosh, K., Himanen, L., & Rinke, P. (2019). Chemical diversity in molecular orbital energy predictions with kernel ridge regression. The Journal of chemical physics, 150, 204121.","DOI":"10.1063\/1.5086105"}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-022-01774-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-022-01774-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-022-01774-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:14:17Z","timestamp":1744200857000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-022-01774-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,4]]},"references-count":47,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["1774"],"URL":"https:\/\/doi.org\/10.1007\/s11265-022-01774-3","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"value":"1939-8018","type":"print"},{"value":"1939-8115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,4]]},"assertion":[{"value":"24 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors did not receive support from any organization for the submitted work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}