Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning

Sura Mahmood Abdullah, Muthusamy Periyasamy, Nafees Ahmed Kamaludeen, S. K. Towfek, Raja Marappan, Sekar Kidambi Raju (), Amal H. Alharbi and Doaa Sami Khafaga
Additional contact information
Sura Mahmood Abdullah: Department of Computer Sciences, University of Technology, Baghdad 110066, Iraq
Muthusamy Periyasamy: Department of Cyber Security, Paavai Engineering College (Autonomous), Namakkal 637018, India
Nafees Ahmed Kamaludeen: Department of Computer Science, Jamal Mohamed College (Autonomous), Bharathidasan University, Tiruchirappalli 620020, India
S. K. Towfek: Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Raja Marappan: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Sekar Kidambi Raju: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Amal H. Alharbi: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Doaa Sami Khafaga: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Sustainability, 2023, vol. 15, issue 7, 1-21

Abstract: Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction.

Keywords: congestion prediction; traffic congestion; transportation systems; recurrent neural networks; bidirectional neural; traffic load; deep learning; gated recurrent unit (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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