A Hybrid Data-Driven Framework for Spatiotemporal Traffic Flow Data Imputation
Peixiao Wang, Tao Hu, Fei Gao, Ruijie Wu, Wei Guo, Xinyan Zhu
Abstract
An accurate estimation of missing data in traffic flow is crucial in urban planning, intelligent transportation, economic geography, and other fields. Thus, improving the data quality of traffic flow is a necessary step in data modeling. Most existing studies use data-driven models to determine spatiotemporal patterns in traffic flow data and fill in the missing information automatically. However, simple data-driven models have unsatisfactory results for describing complex patterns in traffic flow and filling in missing data. This study treated the complex patterns in traffic flow as integrating multiple simple patterns and proposed a hybrid missing data imputation framework called ST-PTD. We used a specific time-series analysis to mine periodic patterns and proposed a novel matrix decomposition method to describe the trend of the traffic flow data. Furthermore, we fused the periodic and trend characteristics of the missing data using a novel dendritic neural network. We applied the framework in actual traffic flow data sets collected in Wuhan, China. The results showed that the ST-PTD framework outperformed the eight existing baselines in terms of imputation accuracy.