Citation

Xia Y, Zhang F, Ou J. IEEE Trans. Intel. Transp. Syst. 2022; 23(3): 1746-1754.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3025948

PMID

unavailable

Abstract

With the rapid development of intelligent transportation systems (ITS), traffic data plays a more and more important role. Low quality traffic data has become a challenging issue in the implementation of ITS. Inspired by the fact that traffic data have strong spatio-temporal correlation, we propose a quality improving model for traffic data, which correlates spatial and temporal features to fix abnormal data. We call it STAP, a spatio-temporal correlative estimating model which firstly proposes an anomalies detection algorithm based on an improved Random Forest model, and then classifies traditional features and extracts spatial and temporal features respectively. Finally the model proposes an XGboost-based data estimation algorithm to fix abnormal data. We conduct experiments on real traffic data collected from a big China city, Changsha, and the results show that the STAP model is effective in improving data quality.

Language: en

Keywords

anomalies detection; Correlation; Data mining; Data models; estimating model; Estimation; Feature extraction; Predictive models; quality improvement; Roads; Spatio-temporal correlation