Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting
To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8801840/ |
| _version_ | 1818603762104664064 |
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| author | Wentian Zhao Yanyun Gao Tingxiang Ji Xili Wan Feng Ye Guangwei Bai |
| author_facet | Wentian Zhao Yanyun Gao Tingxiang Ji Xili Wan Feng Ye Guangwei Bai |
| author_sort | Wentian Zhao |
| collection | DOAJ |
| description | To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic flow. Moreover, we design the model with the Taguchi method to develop an optimized structure of the TCN model, which not only reduces the number of experiments, but also yields high accuracy of forecasting results. With the real-world traffic flow data collected from highways in Birmingham City of U.K., we compare our model with four deep learning based models including LSTM models, GRU models, SAE models, DeepTrend and CNN-LSTM models in terms of the mean absolute error (MAE) and mean relative error (MRE) regarding the actual flow data. The experimental results demonstrate that our framework achieves the state-of-art performance with superior accuracy in short-term traffic flow forecasting. |
| first_indexed | 2024-12-16T13:28:19Z |
| format | Article |
| id | doaj.art-580239645df84f5da917dca8c1170ed2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:28:19Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-580239645df84f5da917dca8c1170ed22022-12-21T22:30:10ZengIEEEIEEE Access2169-35362019-01-01711449611450710.1109/ACCESS.2019.29355048801840Deep Temporal Convolutional Networks for Short-Term Traffic Flow ForecastingWentian Zhao0Yanyun Gao1Tingxiang Ji2Xili Wan3https://orcid.org/0000-0001-9160-8246Feng Ye4https://orcid.org/0000-0002-2436-2300Guangwei Bai5School of Computer Science and Technology, Nanjing Tech University, Nanjing, ChinaSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu, ChinaSchool of Computer Science and Technology, Nanjing Tech University, Nanjing, ChinaSchool of Computer Science and Technology, Nanjing Tech University, Nanjing, ChinaDepartment of Electrical and Computer Engineering, University of Dayton, Dayton, OH, USASchool of Computer Science and Technology, Nanjing Tech University, Nanjing, ChinaTo reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic flow. Moreover, we design the model with the Taguchi method to develop an optimized structure of the TCN model, which not only reduces the number of experiments, but also yields high accuracy of forecasting results. With the real-world traffic flow data collected from highways in Birmingham City of U.K., we compare our model with four deep learning based models including LSTM models, GRU models, SAE models, DeepTrend and CNN-LSTM models in terms of the mean absolute error (MAE) and mean relative error (MRE) regarding the actual flow data. The experimental results demonstrate that our framework achieves the state-of-art performance with superior accuracy in short-term traffic flow forecasting.https://ieeexplore.ieee.org/document/8801840/Deep learningtemporal convolutional networksshort-term forecasting |
| spellingShingle | Wentian Zhao Yanyun Gao Tingxiang Ji Xili Wan Feng Ye Guangwei Bai Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting IEEE Access Deep learning temporal convolutional networks short-term forecasting |
| title | Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting |
| title_full | Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting |
| title_fullStr | Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting |
| title_full_unstemmed | Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting |
| title_short | Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting |
| title_sort | deep temporal convolutional networks for short term traffic flow forecasting |
| topic | Deep learning temporal convolutional networks short-term forecasting |
| url | https://ieeexplore.ieee.org/document/8801840/ |
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