Innovative Nonparametric Method for Data Outlier Filtering

Outlier filtering of empirical travel time data is essential for traffic analyses. Most of the widely applied outlier filtering algorithms are parametric in nature and based on assumed data distributions. The assumption, however, might not hold under unstable traffic conditions. This paper proposes a nonparametric outlier filtering method based on a robust locally weighted regression scatterplot smoothing model. The proposed method identifies outliers based on a data point’s standard residual in the robust local regression model. This approach fits a regression surface with no constraint on parametric distributions and limited influence from outliers. The proposed outlier filtering algorithm can be applied to various data collection technologies and for real-time applications. The performance of the new outlier filtering algorithm is compared with the moving standard deviation method and other traditional filtering algorithms. The test sites include GPS data of an Interstate highway in Indiana and Bluetooth data of an urban arterial roadway in Texas. It is shown that the proposed filtering algorithm has several advantages over the traditional filtering algorithms.

Robust Locally Weighted Regression for Profile Measurement of Magnesium Alloy Tube in Hot Bending Process

Section flattening often occurs in the hot bending process of magnesium alloy tube with large curvature. In order to control the forming quality of the tube, it is necessary to measure the section profile of the magnesium alloy pipe online. In this paper, the laser vision system is used to measure the profile of magnesium alloy tube. Due to the influence of the environment and the surface quality of the pipe, there are obviously isolated outliers in the profile data, which seriously affects the accuracy and precision of the tube measurement. An outlier identification algorithm based on robust locally weighted regression and PaйTa criterion is proposed. This algorithm is used to identify the typically isolated outliers in the measurement process and discuss its identification ability. Meanwhile, it is compared with the moving mean identifier and the Hampel identifier. Subsequently, the ellipse fitting of profile data was carried out, and the fitting ellipse parameters and fitting precision of the curved section were obtained. At the same time, the fitting results were compared before and after the outliers are eliminated. The experiment proves that the outlier identification method based on robust locally weighted regression and PaйTa criterion can effectively identify outliers in profile data, especially for spot outliers. This algorithm is a robust, accurate, and efficient outlier identification method, which can effectively improve the laser profile measurement accuracy of the pipe section and has great significance for the quality control of magnesium alloy tube.

Design of Traffic Volume Forecasting based on Genetic Algorithm

The traffic flow forecasting is very important aspect of traffic predication and congestion. It alleviates the increasing congestion problems that cause drivers to shorten the travelling duration required and prevent financial loss. Increasing congestion is one of the severe problems in big city areas. The aspect of traffic prediction is that it may give drivers to plan their traveling time and traveling path, based on the predictive data information they have. The aim is to design locally weighted regression model by proposing a method, which is a combination of Genetic algorithm and locally weighted regression method. This model helps to achieve optimal prediction performance under various traffic condition parameters. The time series model is used to predict the forecast value for the accurate assumption of the traffic volume generation according to the road capacity. The GA model results show these kind of predictions always be useful for highway road authorities.

Lane-based short-term urban traffic parameters forecasting using multivariate artificial neural network and locally weighted regression models: A genetic approach

Short-term prediction of traffic conditions on urban arterials has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision-making processes. Such information is useful for optimal infrastructure operation, routing, and trip scheduling. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g., 1 or 5 min) and, especially, lane-based are still rare and need special attention. Given the dynamic and stochastic nature of traffic, this study proposes a genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) multivariate models, for short-term traffic prediction using a combination of multiple traffic variables such as volume, occupancy, and speed, during peak and off-peak periods. The proposed 5-min GA-ANN and GA-LWR disaggregate multivariate models show lower average and 95th percentile (P95) errors, when compared to those reported in the literature. In particular, for peak and off-peak time prediction, the GA-ANN disaggregate multivariate models result in most of the average errors being from 2% to 5% and the 95th percentile errors being from 9% to 10%. On the other hand, for peak and off-peak time traffic prediction, the GA-LWR disaggregate multivariate models show that most of the average errors are lower than 5% and the 95th percentile errors are lower than 10%. Meanwhile, for peak and off-peak time prediction, both GA-ANN and GA-LWR disaggregates models show lower MSE of 0.11–1.84. Hence, such techniques are believed useful for developing a robust urban traffic forecasting system.