A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach
Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance...
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| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/11/6/815 |
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doaj-ef6dae7ebe3346629d7764714372965f2020-11-24T21:27:42ZengMDPI AGSymmetry2073-89942019-06-0111681510.3390/sym11060815sym11060815A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor ApproachMinghui Ma0Shidong Liang1Yifei Qin2School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaTraffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods.https://www.mdpi.com/2073-8994/11/6/815traffic flow dataabnormal datadata recoverymissing dataintelligent transportation systemtraffic information |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Minghui Ma Shidong Liang Yifei Qin |
| spellingShingle |
Minghui Ma Shidong Liang Yifei Qin A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach Symmetry traffic flow data abnormal data data recovery missing data intelligent transportation system traffic information |
| author_facet |
Minghui Ma Shidong Liang Yifei Qin |
| author_sort |
Minghui Ma |
| title |
A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach |
| title_short |
A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach |
| title_full |
A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach |
| title_fullStr |
A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach |
| title_full_unstemmed |
A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach |
| title_sort |
bidirectional searching strategy to improve data quality based on k-nearest neighbor approach |
| publisher |
MDPI AG |
| series |
Symmetry |
| issn |
2073-8994 |
| publishDate |
2019-06-01 |
| description |
Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods. |
| topic |
traffic flow data abnormal data data recovery missing data intelligent transportation system traffic information |
| url |
https://www.mdpi.com/2073-8994/11/6/815 |
| work_keys_str_mv |
AT minghuima abidirectionalsearchingstrategytoimprovedataqualitybasedonknearestneighborapproach AT shidongliang abidirectionalsearchingstrategytoimprovedataqualitybasedonknearestneighborapproach AT yifeiqin abidirectionalsearchingstrategytoimprovedataqualitybasedonknearestneighborapproach AT minghuima bidirectionalsearchingstrategytoimprovedataqualitybasedonknearestneighborapproach AT shidongliang bidirectionalsearchingstrategytoimprovedataqualitybasedonknearestneighborapproach AT yifeiqin bidirectionalsearchingstrategytoimprovedataqualitybasedonknearestneighborapproach |
| _version_ |
1725973828128997376 |