@article{ref1,
title="STAP: a spatio-temporal correlative estimating model for improving quality of traffic data",
journal="IEEE transactions on intelligent transportation systems",
year="2022",
author="Xia, Yingjie and Zhang, Fan and Ou, Jing",
volume="23",
number="3",
pages="1746-1754",
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.<p /> <p>Language: en</p>",
language="en",
issn="1524-9050",
doi="10.1109/TITS.2020.3025948",
url="http://dx.doi.org/10.1109/TITS.2020.3025948"
}