Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Mahmoud Ragab (), Hesham A. Abdushkour, Louai Maghrabi, Dheyaaldin Alsalman, Ayman G. Fayoumi and Abdullah AL-Malaise AL-Ghamdi
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Mahmoud Ragab: Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Hesham A. Abdushkour: Nautical Science Department, Faculty of Maritime Studies, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Louai Maghrabi: Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia
Dheyaaldin Alsalman: Department of Cybersecurity, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 34801, Saudi Arabia
Ayman G. Fayoumi: Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Abdullah AL-Malaise AL-Ghamdi: Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Sustainability, 2023, vol. 15, issue 16, 1-17

Abstract: Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

Keywords: Intelligent Transportation System; traffic flow; prediction models; deep learning; artificial rabbits optimizer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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