Open Access

ARTICLE

A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

Mohammed Gollapalli¹, Atta-ur-Rahman^2,*, Dhiaa Musleh², Nehad Ibrahim², Muhammad Adnan Khan³, Sagheer Abbas⁴, Ayesha Atta⁵, Muhammad Aftab Khan⁶, Mehwash Farooqui⁶, Tahir Iqbal⁷, Mohammed Salih Ahmed⁶, Mohammed Imran B. Ahmed⁶, Dakheel Almoqbil⁸, Majd Nabeel², Abdullah Omer²
1 Department of Computer Information System (CIS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
2 Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
3 Pattern Recognition and Machine Learning Lab, Department of Software Engineering, Gachon University, Inchon, Korea
4 Department of Computer Science, National College of Business Administration and Economics, Lahore, Punjab,54000, Pakistan
5 Department of Computer Science, Government College University (GCU), Lahore, Punjab, 54000, Pakistan
6 Department of Computer Engineering (CE), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
7 College of Business Administration, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
8 Department of Networks and Communications (NC), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
* Corresponding Author: Atta-ur-Rahman. Email:

Computers, Materials & Continua 2022, 73(1), 295-310. https://doi.org/10.32604/cmc.2022.027925

Received 28 January 2022; Accepted 30 March 2022; Issue published 18 May 2022

Abstract

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment.

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Keywords

Neuro-fuzzy; machine learning; congestion prediction; AI; cloud computing; smart cities

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