LAUSR

The quality of traffic data is crucial for modern transportation planning and operations. However, data could be missing for various reasons. Hence, the data imputation approaches which aim at predicting/replacing the missing data or bad data have been considered very important. The traditional traffic data imputation approaches mainly focus on using different probability models or regression methods to impute data, and they only take limited temporal or spatial information as inputs. Thus, they are not very accurate especially for data with a high missing ratio. To overcome the weaknesses of previous approaches, this study proposes an innovative traffic data imputation method, which first transforms the raw data into spatial-temporal images and then implements a deep-learning method on the images. The key idea of this approach is developing a convolutional neural network (CNN)-based context encoder to reconstruct the complete image from the missing source. To the best of the authors' knowledge, this is the first time a CNN method has been incorporated for traffic data imputation. Experiments are conducted on three months of data from 256 loop detectors. Through comparison with two state-of-the-art approaches, the results indicate that this new approach increases the imputation accuracy greatly and has a stable error distribution.