Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method
Remote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/5721058 |
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| _version_ | 1850176716878643200 |
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| author | Jiandong Zhao Yuan Gao Jinjin Tang Lingxi Zhu Jiaqi Ma |
| author_facet | Jiandong Zhao Yuan Gao Jinjin Tang Lingxi Zhu Jiaqi Ma |
| author_sort | Jiandong Zhao |
| collection | DOAJ |
| description | Remote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time. |
| format | Article |
| id | doaj-art-bdb3e590bcb6410d904c7aae64d4cb7d |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-bdb3e590bcb6410d904c7aae64d4cb7d2025-08-20T02:19:12ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/57210585721058Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching MethodJiandong Zhao0Yuan Gao1Jinjin Tang2Lingxi Zhu3Jiaqi Ma4School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaDepartment of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH, USARemote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.http://dx.doi.org/10.1155/2018/5721058 |
| spellingShingle | Jiandong Zhao Yuan Gao Jinjin Tang Lingxi Zhu Jiaqi Ma Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method Journal of Advanced Transportation |
| title | Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method |
| title_full | Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method |
| title_fullStr | Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method |
| title_full_unstemmed | Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method |
| title_short | Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method |
| title_sort | highway travel time prediction using sparse tensor completion tactics and k nearest neighbor pattern matching method |
| url | http://dx.doi.org/10.1155/2018/5721058 |
| work_keys_str_mv | AT jiandongzhao highwaytraveltimepredictionusingsparsetensorcompletiontacticsandknearestneighborpatternmatchingmethod AT yuangao highwaytraveltimepredictionusingsparsetensorcompletiontacticsandknearestneighborpatternmatchingmethod AT jinjintang highwaytraveltimepredictionusingsparsetensorcompletiontacticsandknearestneighborpatternmatchingmethod AT lingxizhu highwaytraveltimepredictionusingsparsetensorcompletiontacticsandknearestneighborpatternmatchingmethod AT jiaqima highwaytraveltimepredictionusingsparsetensorcompletiontacticsandknearestneighborpatternmatchingmethod |