An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness

A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis

Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness

Solid Sampling Pyrolysis Adsorption-Desorption Thermal Conductivity Method for Rapid and Simultaneous Detection of N and S in Seafood

In this work, a rapid method for the simultaneous determination of N and S in seafood was established based on a solid sampling absorption-desorption system coupled with a thermal conductivity detector. This setup mainly includes a solid sampling system, a gas line unit for controlling high-purity oxygen and helium, a combustion and reduction furnace, a purification column system for moisture, halogen, SO2, and CO2, and a thermal conductivity detector. After two stages of purging with 20 s of He sweeping (250 mL/min), N2 residue in the sample-containing chamber can be reduced to 2, H2O, and CO2 were absorbed by the adsorption column in turn, the purification process executed the vaporization of the N-containing analyte, and then N2 was detected by the thermal conductivity cell for the quantification of N. Subsequently, the adsorbed SO2 was released after heating the SO2 adsorption column and then transported to the thermal conductivity cell for the detection and quantification of S. After the instrumental optimization, the linear range was 2.0–100 mg and the correlation coefficient (R) was more than 0.999. The limit of detection (LOD) for N was 0.66 μg and the R was less than 4.0%, while the recovery rate ranged from 95.33 to 102.8%. At the same time, the LOD for S was 2.29 μg and the R was less than 6.0%, while the recovery rate ranged from 92.26 to 105.5%. The method was validated using certified reference materials (CRMs) and the measured N and S concentrations agreed with the certified values. The method indicated good accuracy and precision for the simultaneous detection of N and S in seafood samples. The total time of analysis was less than 6 min without the sample preparation process, fulfilling the fast detection of N and S in seafood. The establishment of this method filled the blank space in the area of the simultaneous and rapid determination of N and S in aquatic product solids. Thus, it provided technical support effectively to the requirements of risk assessment and detection in cases where supervision inspection was time-dependent

Clinical Significance of M1/M2 Macrophages and Related Cytokines in Patients with Spinal Tuberculosis

Background. Macrophages are important immune cells involved in Mycobacterium tuberculosis (M.tb) infection. To further investigate the degree of disease development in patients with spinal tuberculosis (TB), we conducted research on macrophage polarization. Methods. Thirty-six patients with spinal TB and twenty-five healthy controls were enrolled in this study. The specific morphology of tuberculous granuloma in spinal tissue was observed by hematoxylin-eosin (H&E) staining. The presence and distribution of bacilli were observed by Ziehl-Neelsen (ZN) staining. Macrophage-specific molecule CD68 was detected by immunohistochemistry (IHC). M1 macrophages play a proinflammatory role, including the specific molecule nitric oxide synthase (iNOS) and the related cytokine tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ). M2 macrophages exert anti-inflammatory effects, including the specific molecule CD163 and related cytokine interleukin-10 (IL-10). The above markers were all detected by quantitative real-time PCR (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and IHC. Results. Typical tuberculous granuloma was observed in the HE staining of patients with spinal TB. ZN staining showed positive expression of Ag85B around the caseous necrosis tissue and Langerhans multinucleated giant cells. At the same time, IHC results indicated that CD68, iNOS, CD163, IL-10, TNF-α, and IFN-γ were expressed around the tuberculous granuloma, and their levels were obviously higher in close tissue than in the distant tissue. RT-PCR and ELISA results indicated that IL-10, TNF-α, and IFN-γ levels of TB patients were also higher than those of the healthy controls. Conclusion. The report here highlights that two types of macrophage polarization (M1 and M2) are present in the tissues and peripheral blood of patients with spinal TB. Macrophages also play proinflammatory and anti-inflammatory roles. Macrophage polarization is involved in spinal TB infection