Short-Term Traffic Flow Forecasting Model Based on GA-TCN

Zhang, Rongji; Sun, Fang Hong; Song, Ziwen; Wang, Xiaolin; Du, Yingcui; Dong, Shulong

doi:10.1155/2021/1338607

Cited by 20 publications

(7 citation statements)

References 23 publications

(34 reference statements)

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“…(4) Deep learning based method [9,10,11]: This is an advanced machine learning method based on neural networks, particularly suitable for handling complex nonlinear relationships. Analyze time sequence data and other related factors through in-depth learning algorithms (such as Recurrent neural network [12], Long short-term memory network [13,14], GCN [15]) to predict traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Prediction model for expressway traffic flow of regional central cities based on time-segments

Zhu

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Accurate traffic flow prediction can effectively alleviate the traffic pressure on expressways. Compared with general cities, the traffic flow on expressways in regional central cities is highly unbalanced, which makes accurate prediction of traffic flow very difficult. For this issue, we observed that traffic flow mainly varies greatly in different time periods, and the proportion of high flow time periods is relatively small. The prediction model will treat high flow data as outliers, resulting in poor prediction performance. Therefore, this paper proposes a time segment based expressway traffic flow prediction model, which maps the high and low traffic flows to different time segments. Divide the training dataset into multiple time segments, and then train the model separately based on the partitioned dataset. The experiment shows that the expressway traffic flow prediction model based on the method proposed in this paper has better performance than similar models.

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Section: Related Workmentioning

confidence: 99%

Prediction model for expressway traffic flow of regional central cities based on time-segments

Zhu

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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“…e GA algorithm is also frequently employed in traffic flow model optimization problems. Zhang et al [37] used GA to optimize the filter weights and parameters of the temporal convolutional neural network (TCN) to find the optimal adaptation of traffic flow prediction models, thereby enhancing their accuracy. Using GA, Zhou et al [38] optimized critical parameters such as penalty parameters of support vector regression in the hybrid traffic flow prediction model to improve the merit-seeking capability during model training.…”

Section: State Of Artmentioning

confidence: 99%

An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction

Hou

Zhang

et al. 2022

Discrete Dynamics in Nature and Society

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Traffic flow is chaotic due to nonstationary realistic factors, and revealing the internal nonlinear dynamics of chaotic data and making high-accuracy predictions is the key to traffic control and inducement. Given that high-quality phase space reconstruction is the foundation of predictive modeling. Firstly, an improved C-C method based on the fused norm search domain is proposed to address the issue that the C-C method in the phase space reconstruction algorithm does not meet the Euclidean metric accuracy and reduces the reconstruction quality when the infinite norm metric is used. Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). Finally, an improved probabilistic dynamic reproduction-based genetic algorithm (PDRGA) is proposed to address the problem of the hybrid model falling into a local optimum when learning the phase space law. Experiments are conducted in three aspects: phase space reconstruction quality analysis, comparison of optimization algorithm convergence, and prediction model performance comparison. The experimentation with two data sets demonstrates that the improved C-C method combines the advantages of the high accuracy metric of the L2 norm with the low operational complexity of the infinite norm, achieving a balance between reconstruction quality and algorithm efficiency. The proposed PDRGA optimization algorithm is a lightweight improvement of the traditional genetic algorithm (GA) and solves the problem that the model tends to fall into a local optimum by optimizing the initial weights of CDBN. Meanwhile, the five error evaluation indexes of the proposed PDRGA-CDBN-LSTM hybrid model are lower than those of the baseline model, providing a new modeling idea for chaotic traffic flow prediction.

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“…However, TCNs may not be as flexible in the context of traffic timing due to variations in the amount of historical information needed for model predictions across different domains. When TCNs (temporal convolutional networks) face a dynamic transportation network, their performance may be poor because their perceptual field is not large enough to describe the dynamics, complexity, and capture the global contextual information 12 .…”

Section: Introductionmentioning

confidence: 99%

“…

across different domains. When TCNs (temporal convolutional networks) face a dynamic transportation network, their performance may be poor because their perceptual field is not large enough to describe the dynamics, complexity, and capture the global contextual information 12 .The most advanced approach employs a graphical convolutional neural network (GNN) for spatial modeling reuse and combines LSTM to deal with anomaly prediction in time series 3 . There is also a method of passing adversarial training, learning the spatiotemporal features of traffic dynamics and traffic anomalies, respectively 13 .

…”

mentioning

confidence: 99%

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Ren,

Li,

Peng

et al. 2024

Sci Rep

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Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

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Short-Term Traffic Flow Forecasting Model Based on GA-TCN

Cited by 20 publications

References 23 publications

Prediction model for expressway traffic flow of regional central cities based on time-segments

Prediction model for expressway traffic flow of regional central cities based on time-segments

An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

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