Papers by Keyword: Missing Values

Abstract: The collection and management of dynamic traffic information is one of the most important part of ITS. Its a main task for it to improve the accuracy of the acquisition of the traffic information when facing up with different kinds of traffic detectors. Data fusion method can deal with data from different detectors and improve the accuracy. This paper first analyzed the characters of different traffic detectors, and proposed a method to repair the missing values which is a common phenomenon in the detect data. Then some improvements are made to adjust the BP neural network so that it could be suitable for data fusion. At last, the data fusion of traffic speed from the south of Jianguomen Qiao to the north of Chaoyangmen Qiao on the second ring road of Beijing is given as an example with the comparison of different improve methods of BP neural networks, and it shows that the method given in this passage is efficient in improving the accuracy of the traffic data detection.

Abstract: The presence of missing values in statistical survey data is an important issue to deal with. These data usually contained missing values due to many factors such as machine failures, changes in the siting monitors, routine maintenance and human error. Incomplete data set usually cause bias due to differences between observed and unobserved data. Therefore, it is important to ensure that the data analyzed are of high quality. A straightforward approach to deal with this problem is to ignore the missing data and to discard those incomplete cases from the data set. This approach is generally not valid for time-series prediction, in which the value of a system typically depends on the historical time data of the system. One approach that commonly used for the treatment of this missing item is adoption of imputation technique. This paper discusses three interpolation methods that are linear, quadratic and cubic. A total of 8577 observations of PM₁₀ data for a year were used to compare between the three methods when fitting the Gamma distribution. The goodness-of-fit were obtained using three performance indicators that are mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R²). The results shows that the linear interpolation method provides a very good fit to the data.