A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System

Yao, Baozhen; Ma, Ankun; Feng, Rui; Shen, Xiaopeng; Zhang, Mingheng; Yao, Yansheng

doi:10.3389/fpubh.2021.804298

Cited by 3 publications

(1 citation statement)

References 41 publications

(43 reference statements)

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“…The model using DNN with the LSTM or autoencoder layers is categorized by comparatively high resistance and accuracy to miss data from temporary measurement points that are located in the urban road network. In [14], a new DL design called Ensemble Attention-based Graph Time Convolutional Networks (EAGTCN) is designed. At the initial stage, by spatial blocks, the global spatial pattern is captured which are attached by a three-dimensional ensemble attention layer as well as a Graph Convolution Network (GCN).…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Alruban,

Mengash,

Eltahir

et al. 2024

IEEE Access

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Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks.

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Alruban,

Mengash,

Eltahir

et al. 2024

IEEE Access

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Deep Learning Based IoT System for Real-time Traffic Risk Notifications

Islam,

Klupka,

Mohammadi

et al. 2024

2024 25th International Symposium on Quality Electronic Design (ISQED)

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Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

Lan

Zhang

et al. 2023

Sustainability

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Traffic-flow prediction plays an important role in the construction of intelligent transportation systems (ITS). So, in order to improve the accuracy of short-term traffic flow prediction, a prediction model (GWO-attention-LSTM) based on the combination of optimized attention mechanism and long short-term memory (LSTM) is proposed. The model is based on LSTM and uses the attention mechanism to assign individual weight to the feature information extracted via LSTM. This can increase the prediction model’s focus on important information. The initial weight parameters of the attention mechanism are also optimized using the grey wolf optimizer (GWO). By simulating the hunting process of grey wolves, the GWO algorithm calculates the hunting position of the grey wolf and maps it to the initial weight parameters of the attention mechanism. In this way, the short-time traffic flow prediction model is constructed. The traffic flow data of the trunk roads in the center of Qingdao (China) are used as the research object. Multiple sets of comparison models are set up for prediction analysis. The results show that the GWO-attention-LSTM model has obvious advantages over other models. The prediction error MAE values of the GWO-attention-LSTM model decreased by 7.32% and 14.35% on average compared with the attention-LSTM model and LSTM model. It is concluded that the GWO-attention-LSTM model has better model performance and can provide effective help for traffic management control and traffic flow theory research.

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A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System

Cited by 3 publications

References 41 publications

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Deep Learning Based IoT System for Real-time Traffic Risk Notifications

Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

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