Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method
Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/6575947 |
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| _version_ | 1832556282384482304 |
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| author | Yuhan Jia Jianping Wu Ming Xu |
| author_facet | Yuhan Jia Jianping Wu Ming Xu |
| author_sort | Yuhan Jia |
| collection | DOAJ |
| description | Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data. |
| format | Article |
| id | doaj-art-913d94006abf4420a81528c554179233 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-913d94006abf4420a81528c5541792332025-02-03T05:45:48ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/65759476575947Traffic Flow Prediction with Rainfall Impact Using a Deep Learning MethodYuhan Jia0Jianping Wu1Ming Xu2Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaAccurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.http://dx.doi.org/10.1155/2017/6575947 |
| spellingShingle | Yuhan Jia Jianping Wu Ming Xu Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method Journal of Advanced Transportation |
| title | Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method |
| title_full | Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method |
| title_fullStr | Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method |
| title_full_unstemmed | Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method |
| title_short | Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method |
| title_sort | traffic flow prediction with rainfall impact using a deep learning method |
| url | http://dx.doi.org/10.1155/2017/6575947 |
| work_keys_str_mv | AT yuhanjia trafficflowpredictionwithrainfallimpactusingadeeplearningmethod AT jianpingwu trafficflowpredictionwithrainfallimpactusingadeeplearningmethod AT mingxu trafficflowpredictionwithrainfallimpactusingadeeplearningmethod |