{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:10:36Z","timestamp":1775067036011,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology Development Fund (TDF) program of Higher Education Commission of Pakistan (HEC)","award":["TDF02-261"],"award-info":[{"award-number":["TDF02-261"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial\u2013temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM\u2013GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.<\/jats:p>","DOI":"10.3390\/s22093348","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T22:20:20Z","timestamp":1651098020000},"page":"3348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3181-8118","authenticated-orcid":false,"given":"Noureen","family":"Zafar","sequence":"first","affiliation":[{"name":"Pakistan Institute of Engineering and Applied Sciences, Islamabad 44000, Pakistan"},{"name":"University Institute of Information Technology, Pir Mehr Ali Shah University of Arid Agriculture, Rawalpindi 46000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5142-3965","authenticated-orcid":false,"given":"Irfan Ul","family":"Haq","sequence":"additional","affiliation":[{"name":"Pakistan Institute of Engineering and Applied Sciences, Islamabad 44000, Pakistan"}]},{"given":"Jawad-ur-Rehman","family":"Chughtai","sequence":"additional","affiliation":[{"name":"Pakistan Institute of Engineering and Applied Sciences, Islamabad 44000, Pakistan"}]},{"given":"Omair","family":"Shafiq","sequence":"additional","affiliation":[{"name":"School of Information Technology, Carleton University, Ottawa, ON K1S 5B6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"ref_1","first-page":"9078547","article-title":"Multisource data framework for road traffic state estimation","volume":"2018","author":"Mitsakis","year":"2018","journal-title":"J. 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