Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model
An effective way to solve the problem of urban traffic congestion is to predict the road traffic status accurately and take effective traffic control measures in time. Considering the impact of visibility on traffic, the pavement status and time characteristics were finely divided, and a regression...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Engineering, Project, and Production Management (EPPM)
2022-05-01
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| Series: | Journal of Engineering, Project, and Production Management |
| Subjects: | |
| Online Access: | http://www.ppml.url.tw/EPPM_Journal/volumns/12_02_May_2022/ID_409_12_2_108_115.pdf |
| _version_ | 1818538724113252352 |
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| author | Xinghua Hu Xinghui Chen Gao Dai |
| author_facet | Xinghua Hu Xinghui Chen Gao Dai |
| author_sort | Xinghua Hu |
| collection | DOAJ |
| description | An effective way to solve the problem of urban traffic congestion is to predict the road traffic status accurately and take effective traffic control measures in time. Considering the impact of visibility on traffic, the pavement status and time characteristics were finely divided, and a regression decision tree was used to establish the traffic flow velocity prediction model with pavement status, time characteristics, and working day characteristics as characteristic parameters. Furthermore, based on the perspective of avoiding using velocity as a single parameter to classify the road traffic status levels, the Kmeans clustering algorithm was used to obtain the classification label results. Moreover, the traffic flow velocity and pavement status were used as characteristic parameters of the classification decision tree to establish the multi-parameter road traffic status prediction model. The experimental result showed that the prediction accuracy of the proposed road traffic status prediction model was 81.31%, and this method has good applicability and certain application value for road traffic status prediction. |
| first_indexed | 2024-12-11T21:32:39Z |
| format | Article |
| id | doaj.art-dc5ceba789144b51acb8215f2d0ccba2 |
| institution | Directory Open Access Journal |
| issn | 2221-6529 2223-8379 |
| language | English |
| last_indexed | 2024-12-11T21:32:39Z |
| publishDate | 2022-05-01 |
| publisher | Engineering, Project, and Production Management (EPPM) |
| record_format | Article |
| series | Journal of Engineering, Project, and Production Management |
| spelling | doaj.art-dc5ceba789144b51acb8215f2d0ccba22022-12-22T00:50:08ZengEngineering, Project, and Production Management (EPPM)Journal of Engineering, Project, and Production Management2221-65292223-83792022-05-0112210811510.32738/jeppm-2022-0010Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree ModelXinghua Hu0Xinghui Chen1Gao Dai2College of Traffic & Transportation, Chongqing Jiaotong UniversityCollege of Traffic & Transportation, Chongqing Jiaotong UniversityCollege of Traffic & Transportation, Chongqing Jiaotong UniversityAn effective way to solve the problem of urban traffic congestion is to predict the road traffic status accurately and take effective traffic control measures in time. Considering the impact of visibility on traffic, the pavement status and time characteristics were finely divided, and a regression decision tree was used to establish the traffic flow velocity prediction model with pavement status, time characteristics, and working day characteristics as characteristic parameters. Furthermore, based on the perspective of avoiding using velocity as a single parameter to classify the road traffic status levels, the Kmeans clustering algorithm was used to obtain the classification label results. Moreover, the traffic flow velocity and pavement status were used as characteristic parameters of the classification decision tree to establish the multi-parameter road traffic status prediction model. The experimental result showed that the prediction accuracy of the proposed road traffic status prediction model was 81.31%, and this method has good applicability and certain application value for road traffic status prediction.http://www.ppml.url.tw/EPPM_Journal/volumns/12_02_May_2022/ID_409_12_2_108_115.pdftraffic engineeringtraffic flow velocityroad traffic status predictionkmeans clusteringdecision tree |
| spellingShingle | Xinghua Hu Xinghui Chen Gao Dai Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model Journal of Engineering, Project, and Production Management traffic engineering traffic flow velocity road traffic status prediction kmeans clustering decision tree |
| title | Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model |
| title_full | Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model |
| title_fullStr | Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model |
| title_full_unstemmed | Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model |
| title_short | Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model |
| title_sort | road traffic status prediction approach based on kmeans decision tree model |
| topic | traffic engineering traffic flow velocity road traffic status prediction kmeans clustering decision tree |
| url | http://www.ppml.url.tw/EPPM_Journal/volumns/12_02_May_2022/ID_409_12_2_108_115.pdf |
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