Exploring complicated dynamic spatiotemporal correlations has always been a challenging issue in traffic prediction. Besides, methods that make predictions directly from data with missing values, have received much attention due to the inevitable and pervasive nature of data incompleteness in real scenarios. In this paper, an end-to-end representation learning framework, named spatial–temporal periodical adaptive graph contrastive learning (ST-A-PGCL), is proposed to address such issues. ST-A-PGCL mainly consists of three independent branches to respectively model three long-term periodicities of traffic flow (recent, daily, and weekly periodicities). In each branch, the spatial and temporal correlations are extracted by improved adaptive graph convolution network (ImpAdapGCN) and fused seasonal-trend temporal convolution network (FST-TCN) in an encoder, respectively, to obtain hidden representation. Besides, each branch accepts one periodic segment which will be synthetically augmented with different missing patterns to simulate real scenarios (weak communication signal, detector malfunction, area-wide power failure, etc) and generate different views. These views will be fed into a periodical graph contrastive learning (PGCL) module to learn periodical similarity features based on Siamese network to defeat data incompleteness. Bidirectional gate recurrent unit (Bi-GRU) is selected to decode the hidden representations and generate final prediction results. Specifically, the overall framework is trained in end-to-end dual-task (traffic prediction and contrastive learning) process without requiring identifying the position of missing values. Our framework is evaluated across four real-world datasets and twenty baseline models. Experimental results show that the proposed ST-A-PGCL achieves superior prediction performance, especially in long-term prediction tasks with high missing rates. 

ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios

Congestion in the world’s traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. Modern scholarly challenges arise alongside chances to greatly enhance traffic prediction made possible by the integration of modern technologies into transportation systems. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, deep neural network architecture based on long short term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers have been structured to build the deep neural network, in order to predict the performance of the traffic flow in four distinct junctions which has a great impact on the Internet of vehicles’ applications. The structure comprised of sixteen-layers, five of them are GRU-layers and one bi-directional LSTM-layer. The dataset employed in this work involved four congested junctions. The dataset extended from the first of November 2016 to 30th of June 2017. Cleaning and preprocessing operations were achieved on the dataset before feeding it to the designed deep neural network of this paper. Results show that the suggested method produced a comparable performance with respect to state-of-the art approaches. 

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10108955.pdf

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

The rapid growth of urban areas has significantly compounded traffic challenges, amplifying concerns about congestion and the need for efficient traffic management. Accurate short-term traffic flow prediction remains important for strategic infrastructure planning within these expanding urban networks. This study explores a Transformer-based model designed for traffic flow prediction, conducting a comprehensive comparison with established models such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Time-Delay Neural Network (TDNN). Our approach integrates traditional time series values with derived time-related features, enhancing the model's predictive capabilities. The aim is to effectively capture temporal dependencies within operational data. Despite the effectiveness of existing models, internal complexities persist due to diverse road conditions that influence traffic dynamics. The proposed Transformer model consistently demonstrates competitive performance and offers adaptability when learning from longer time spans. However, the simpler BiLSTM model proved to be the most effective when applied to the utilized data. 

Traffic Flow Prediction in Urban Networks: Integrating Sequential Neural Network Architectures

Network traffic state prediction has been constantly challenged by complex spatiotemporal features of traffic information as well as imperfection in streaming data. This paper proposes a traffic flow prediction model for spatiotemporal graph networks based on fusion of attention mechanisms (STGNN-FAM) to simultaneously tackle these challenges. This model contains a spatial feature extraction layer, a bidirectional temporal feature extraction layer, and an attention fusion layer, which not only fully considers the temporal and spatial features of the traffic flow problem but also uses the attention mechanism to enhance the critical temporal and spatial features to achieve more accurate and robust predictions. Experimental results on a network traffic speed dataset PeMSD7 show that the proposed STGNN-FAM outperforms several important benchmarks in prediction accuracy and the ability to withstand interference in the data stream, especially for mid- and long-term prediction of 30 minutes and 45 minutes.

/pdf/stgnn-fam-a-traffic-flow-prediction-model-for-spatiotemporal-lzgedlj1.pdf

STGNN-FAM: A Traffic Flow Prediction Model for Spatiotemporal Graph Networks Based on Fusion of Attention Mechanisms

Short-term traffic flow prediction is of great significance for road construction and operation. Aiming at the possible problems of missing data or insufficient sample data, the short-term traffic flow prediction of parallel road sections is studied using the traffic flow data collected in Shandong Province and the transfer learning idea. Firstly, the spatiotemporal correlation of the traffic flow of the parallel road section is analyzed, and the correlation coefficient is calculated; Secondly, the long-term and short-term memory network model is used to predict the section flow of expressway; Finally, the model based transfer learning is used to predict the cross section flow of parallel roads. The prediction results show that the average absolute error, the mean square error, the root mean square error and the average absolute percentage error of the transfer learning based prediction algorithm decrease by 2.56%, 3.59%, 1.81% and 4.41% respectively compared with the direct prediction of the short-term memory network model. In addition, the prediction model based on transfer learning can significantly improve the prediction efficiency, and can provide feasible methods for traffic flow prediction in areas lacking equipment collection or data. 

Short-term traffic flow prediction of parallel roads based on transfer learning

For the real road environment, the proportion of traffic sign targets is small and not easy to be detected, and the recognition background is complex and changeable, resulting in the problems of low detection precision and poor robustness. In this paper, a traffic sign detection algorithm based on improved Yolov5-PB is proposed. The algorithm uses the ParC-Net as the backbone feature extraction network to fully extract the target feature information. The BiFPN structure is adopted to enhance the feature fusion ability of the network for multi-scale traffic signs. Simulation is carried out on TT100K and GTSDB traffic sign datasets, and the results show that the mAP@0.5 of Yolov5-PB algorithm reaches 85.6% and 72.2%, respectively, which are 3.1% and 19.2% higher than Yolov5. The precision reaches 85.3% and 71.6%, respectively, which are 0.3% and 21.8% higher than Yolov5. Furthermore, compared with the current mainstream target detection algorithms, the proposed algorithm can achieve better detection precision and meet the requirements of real-time detection, and has better robustness.

Yolov5-PB: a traffic sign detection algorithm based on deep learning

Short-term traffic flow prediction is an important component of intelligent transportation systems, which can support traffic trip planning and traffic management. Although existing predicting methods have been applied in the field of traffic flow prediction, they cannot capture the complex multifeatures of traffic flows resulting in unsatisfactory short-term traffic flow prediction results. In this paper, a multifeature fusion model based on deep learning methods is proposed, which consists of three modules, namely, a CNN-Bidirectional GRU module with an attention mechanism (CNN-BiGRU-attention) and two Bidirectional GRU modules with an attention mechanism (BiGRU-attention). The CNN-BiGRU-attention module is used to extract local trend features and long-term dependent features of the traffic flow, and the two BiGRU-attention modules are used to extract daily and weekly periodic features of the traffic flow. Moreover, a feature fusion layer in the model is used to fuse the features extracted by each module. And then, the number of neurons in the model, the loss function, and other parameters such as the optimization algorithm are discussed and set up through simulation experiments. Finally, the multifeature fusion model is trained and tested based on the training and test sets from the data collected from the field. And the results indicate that the proposed model can better achieve traffic flow prediction and has good robustness. Furthermore, the multifeature fusion model is compared and analyzed against the baseline models with the same dataset, and the experimental results show that the multifeature fusion model has superior predictive performance compared to the baseline models.

/pdf/a-multifeature-fusion-short-term-traffic-flow-prediction-202tabhc.pdf

A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Qiu Meng

Author Tools

Chat about Author

Papers

Yolov5-PB: a traffic sign detection algorithm based on deep learning

A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings