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Traffic volume data collected by radar detector contain inherent types of errors that are hard to observe. Therefore, the identification of missing data and outliers based on reliable verification volume data is essential, and the development of a methodology for correcting detector traffic data while considering various specificities at the detector installation sites is required. This study proposed a deep-learning-based long short-term memory–multiple linear regression (LSTM–MLR) hybrid model. First, the corrected detector traffic volume was calculated via the MLR model after the preprocessing of the missing and outlier detector traffic volumes. Second, the corrected radar detector traffic volume was learned via the LSTM model to predict the detector traffic volume for the target time periods. The results confirmed a correction effect in terms of radar detector traffic volume data at most of the 30 target sites. The results of this study provide three contributions. First, the missing and outlier traffic volume correction algorithm is easy to apply and can be applied to traffic volume data collected from various detectors. Second, the MLR model developed in this study derived a causal relationship between the traffic volume of the detector and the complex factors that could not reveal the obvious cause, such as the undetected small car driving next to a large vehicle and the radio transmission/reception problem. Third, complex neural networks and dropout techniques to avoid overfitting do not necessarily improve prediction accuracy.

Development of LSTM-MLR hybrid model for radar detector missing and outlier traffic volume correction