Traffic state estimation is one of the very basic and most important tasks in intelligent transportation systems. Data collected from multiple types of traffic sensors are valuable sources to estimate traffic state. However, those data are always incomplete for the cases such as sensors broken, data missing, etc. The incomplete data can also be used to accurately estimate traffic state by data prediction and data fusion. Firstly, the missing data are predicted based on the data correlations extracted on large amount of historical taxi GPS data. Then, data collected from multiple sensors are fused to get more accurate results. Finally, computing of data fusion is parallelized. The main research focus is placed on learning for extracting the inherent spatio-temporal correlations of traffic data and fusing multimedia traffic data. Accuracy and efficiency experiments are conducted and the experimental results demonstrate that our method can process incomplete data and achieve accurate and real-time traffic state estimation.
['Yiyang Yao', 'Yingjie Xia', 'Zhenyu Shan', 'Zhengguang Liu']
Learning for Traffic State Estimation on Large Scale of Incomplete Data