Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas

• Main Authors: Chughtai, J.-U.-R (Author), Haq, I.U (Author), Shafiq, O. (Author), Zafar, N. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Application program interfaces; Application programming interfaces (API); Application programs; CNN; Convolutional neural network; Convolutional neural networks; Data set; Feature space; Gated recurrent unit; GRU; Heterogeneous data sources; Intelligent systems; Intelligent vehicle highway systems; Internet of things; IoT; ITS; Long short-term memory; LSTM; Mean square error; Model-based OPC; Smart city; Speed prediction; Time series analysis; Traffic control; Traffic speed; Urban areas
• Online Access: View Fulltext in Publisher

 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–temporal 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–GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.