Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques
Traditional short-term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short-time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO-VMD) based on the optimization of the gray wolf search algorithm. It a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2024/1928189 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846144750191640576 |
|---|---|
| author | Jinqiu Zhao Le Yu Shuhua Wang Zhonghao Zhang |
| author_facet | Jinqiu Zhao Le Yu Shuhua Wang Zhonghao Zhang |
| author_sort | Jinqiu Zhao |
| collection | DOAJ |
| description | Traditional short-term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short-time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO-VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short-time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep-learning traffic prediction framework based on the stacking of multiple temporal trend-aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend-aware temporal graph neural network (TTGAN). TGA dynamically models the space-time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR). |
| format | Article |
| id | doaj-art-c9a398c3b6dd4a2982c42df429b45f2c |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-c9a398c3b6dd4a2982c42df429b45f2c2024-12-02T06:54:46ZengWileyDiscrete Dynamics in Nature and Society1607-887X2024-01-01202410.1155/2024/1928189Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction TechniquesJinqiu Zhao0Le Yu1Shuhua Wang2Zhonghao Zhang3School of ArchitectureShenzhen Technology UniversityCollege of Transport and CommunicationsCollege of Transport and CommunicationsTraditional short-term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short-time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO-VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short-time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep-learning traffic prediction framework based on the stacking of multiple temporal trend-aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend-aware temporal graph neural network (TTGAN). TGA dynamically models the space-time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR).http://dx.doi.org/10.1155/2024/1928189 |
| spellingShingle | Jinqiu Zhao Le Yu Shuhua Wang Zhonghao Zhang Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques Discrete Dynamics in Nature and Society |
| title | Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques |
| title_full | Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques |
| title_fullStr | Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques |
| title_full_unstemmed | Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques |
| title_short | Enhancing Traffic Flow Forecasting with Deep-Learning and Noise Reduction Techniques |
| title_sort | enhancing traffic flow forecasting with deep learning and noise reduction techniques |
| url | http://dx.doi.org/10.1155/2024/1928189 |
| work_keys_str_mv | AT jinqiuzhao enhancingtrafficflowforecastingwithdeeplearningandnoisereductiontechniques AT leyu enhancingtrafficflowforecastingwithdeeplearningandnoisereductiontechniques AT shuhuawang enhancingtrafficflowforecastingwithdeeplearningandnoisereductiontechniques AT zhonghaozhang enhancingtrafficflowforecastingwithdeeplearningandnoisereductiontechniques |