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Trans. Planning Journal
| Title | Two-Stage Data Imputation for Missing Value of Vehicle Detectors and Its Applications Using Artificial Neural Networks |
|---|---|
| Author | Jiann-Sheng Wu, Tzu-Lin Liao and Yu-Shiang Lin |
| Summary | Using a two-stage data imputation method based on artificial neural networks, we carried out, in this study, an empirical analysis of the missing value of vehicle detectors in Hshehshan Tunnel to search for the optimal alternative, and developed its possible applications accordingly. By testing data imputation, we, at first, clustered all the data into groups using K-means, and then chose three typical artificial neural networks to impute the missing data. The result shows that two-group data clustering combined with a recurrent neural network can achieve the highest imputation performance. We, finally, developed two possible applications based on it, including data imputation and installation spacing of vehicle detectors. In respect to data imputation, speed performed the best with an accuracy of greater than 97.5%, and all pairs of vehicle detectors could be input for imputation. Flow performed the second best with an accuracy of over 90%, and the nearest two or ten pairs of detectors up- and downstream could be input for the imputation of data group 1 or 2, respectively. Occupancy performed the worst. Only by an accuracy threshold lowered to 80%, data points in group 1 could be imputed, and those in group 2 were not restricted, nevertheless. In respect to installation spacing, occupancy would dominate due to its relatively poor performance by considering all the three traffic attributes. Only when the overall accuracy decreased to fewer than 85% could we extend the current spacing of 350 m to 3,500 m. If only considering data group 2, we could extend it to 4,200 m with an accuracy of over 90% due to lower randomness. |
| Vol. | 40 |
| No. | 1 |
| Page | 1 |
| Year | 2011 |
| Month | 3 |
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