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Open AccessArticle

A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

1
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(6), 815; https://doi.org/10.3390/sym11060815
Received: 16 April 2019 / Revised: 6 June 2019 / Accepted: 18 June 2019 / Published: 20 June 2019
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Abstract

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. View Full-Text
Keywords: traffic flow data; abnormal data; data recovery; missing data; intelligent transportation system; traffic information traffic flow data; abnormal data; data recovery; missing data; intelligent transportation system; traffic information
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Ma, M.; Liang, S.; Qin, Y. A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach. Symmetry 2019, 11, 815.

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