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

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.

Dynamic Spatiotemporal Correlation Graph Convolutional Network for Traffic Speed Prediction

“…However, the traditional RNN suffers from problems such as "vanishing gradient" and "exploding gradient", which limit its performance in modeling long-term dependencies [24]. To overcome these problems, Long Short-Term Memory (LSTM) [25,26] and Gated Recurrent Unit (GRU) [27,28] have been introduced into the traffic-prediction task. LSTM and GRU add a gating mechanism to RNN, which can better capture long-term dependencies.…”

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

“…For instance, Anjaneyulu and Kubendiran's proposed study presents a hybrid Xception support vector machine (XPSVM) classifier model with a high accuracy rate for short-term traffic congestion prediction [36]. Abdullah et al proposed a bidirectional recurrent neural network (BRNN) using gated recurrent units (GRUs) for simulating and forecasting traffic congestion in smart cities, aiming to improve traffic management efficiency [37].…”

Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

“…You can also use this data to identify patterns that can be used to improve the user experience, such as speeding up page loading times or tailoring content to be more relevant to the user. Abdullah, S. M., et al [25] has discussed the Soft GRU-Based Recurrent Neural Networks (RNNs) for Enhanced Congestion Prediction Using Deep Learning is a type of artificial neural network that uses deep learning techniques to predict traffic patterns and congested regions in urban areas. These soft GRU-based RNNs use transfer learning to learn complex patterns in the data quickly and accurately, allowing quick and accurate congestion prediction.…”