Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning
The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/8878494 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567808737673216 |
|---|---|
| author | Jiayu Liu Xingju Wang Yanting Li Xuejian Kang Lu Gao |
| author_facet | Jiayu Liu Xingju Wang Yanting Li Xuejian Kang Lu Gao |
| author_sort | Jiayu Liu |
| collection | DOAJ |
| description | The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data. |
| format | Article |
| id | doaj-art-8aee0dc5e3164679a43a874cb115bb90 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8aee0dc5e3164679a43a874cb115bb902025-02-03T01:00:28ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/88784948878494Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep LearningJiayu Liu0Xingju Wang1Yanting Li2Xuejian Kang3Lu Gao4School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaSchool of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, ChinaDepartment of Construction Management, University of Houston, 4730 Calhoun Road No. 300 Houston, Houston, TX 77204-4021, USAThe accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data.http://dx.doi.org/10.1155/2021/8878494 |
| spellingShingle | Jiayu Liu Xingju Wang Yanting Li Xuejian Kang Lu Gao Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning Journal of Advanced Transportation |
| title | Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning |
| title_full | Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning |
| title_fullStr | Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning |
| title_full_unstemmed | Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning |
| title_short | Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning |
| title_sort | method of evaluating and predicting traffic state of highway network based on deep learning |
| url | http://dx.doi.org/10.1155/2021/8878494 |
| work_keys_str_mv | AT jiayuliu methodofevaluatingandpredictingtrafficstateofhighwaynetworkbasedondeeplearning AT xingjuwang methodofevaluatingandpredictingtrafficstateofhighwaynetworkbasedondeeplearning AT yantingli methodofevaluatingandpredictingtrafficstateofhighwaynetworkbasedondeeplearning AT xuejiankang methodofevaluatingandpredictingtrafficstateofhighwaynetworkbasedondeeplearning AT lugao methodofevaluatingandpredictingtrafficstateofhighwaynetworkbasedondeeplearning |