A state-space model was established for the short-term traffic flow prediction problem under complex road conditions, which is based on macroscopic traffic flow forecasting. In order to solve the problem of parameter optimization on the dynamic traffic forecast model, a method to improve the performance of Unscented Kalman Filter (UKF) with orthogonal adaptive Differential Evolution (DE) was proposed. The orthogonal method maximized the diversity of the initial population in DE algorithm. The crossover operator in DE was optimized by the orthogonal method and the technology of quantification to balance the exploitation and exploration, which was more beneficial to find the model parameters of UKF. The experimental results show that, with respect to use random distribution to initialize the parameters, or set model parameters based on the experience, the use of orthogonal design method for initialization strategy, mutation operator and adaptive control strategy of parameters in differential evolution algorithm can effectively save computing resources, improve forecasting performance and accuracy, and provide better robustness.

Focusing on the issue that the clustering accuracy rate and recall rate are difficult to balance in traditional text clustering algorithms, a clustering approach based on the R-Grams text similarity computing algorithm was proposed. Firstly, the clustered documents were sorted in descending order; secondly, the symbolic documents were identified and then initial clustering results were achieved by using an R-Grams-based similarity computing algorithm; finally, the final clustering results were completed by combining the initial clustering. The experimental results show that the proposed approach can flexibly regulate the clustering results by adjusting the clustering threshold parameter to satisfy different demands and the optimal parameter is about 15. With the increasing of the clustering threshold, the clustering accuracies increase, and the recalls increase at first, then decrease. In addition, the approach is free from time-consuming processing procedures such as word segmentation and feature extraction and can be easily implemented.

When the Synthetic Minority Over-sampling Technique (SMOTE) is used in imbalance dataset classification, it sets the same sampling rate for all the samples of minority class in the process of synthetising new samples, which has blindness. To overcome this problem, a Genetic Algorithm (GA) improved SMOTE algorithm, namely GASMOTE (Genetic Algorithm Improved Synthetic Minority Over-sampling Technique) was proposed. At the beginning, GASMOTE set different sampling rates for different minority class samples. One combination of the sampling rates corresponded to one individual in the population. And then, the selection, crossover and mutation operators of GA were iteratively applied on the population to get the best combination of sampling rates when the stopping criteria were met. At last, the best combination of sampling rates was used in SMOTE to synthetise new samples. The experimental results on ten typical imbalance datasets show that, compared with SMOTE algorithm, GASMOTE can increase 5.9 percentage on F-measure value and 1.6 percentage on G-mean value, and compared with Borderline-SMOTE algorithm, GASMOTE can increase 3.7 percentage on F-measure value and 2.3 percentage on G-mean value. GASMOTE can be used as a new over-sampling technique to deal with imbalance dataset classification problem.

A new autonomous vehicle navigation model was proposed based on multi-sensor system for vehicle navigation and Global Positioning System (GPS) under complex road conditions. And the Unscented Kalman Filter (UKF) was used to overcome some security issues due to the sudden error produced by the Kalman filters with extensions, which belonged to Sigma point based sensor fusion algorithm. It is more suitable than the Kalman filters with extensions that the UKF can calculate the evaluation satisfied the requirement in vehicle navigation. Comparison experiments with the Kalman filter based on polynomial expansion were given in terms of estimation accuracy and computational speed. The experimental results show that the Sigma-point Kalman filter is a reliable and computationally efficient approach to state estimation-based control. Moreover, it is faster to evaluate the motion state of the vehicle according to the current direction situations and the feedback information of vehicle sensor, and can calculate the control input of vehicle adaptively in real time.