Concerning poor matching effect of existing map matching algorithms (such as classical Hidden Markov and its variants, advanced algorithms) for low-frequency trajectory data, a trajectory data mining method based on massive bus historical trajectory data was proposed. Taking bus stations as the sequence skeleton firstly, by mining, extracting, regrouping and sorting trajectory data from a large number of low frequency trajectory points to form high frequency trajectory data, then the high-frequency trajectory data sequence was processed by the map matching algorithm based on classical hidden Markov model to get the bus route map matching results. Compared with the matching method on the low-frequency data not processed by the mining algorithm, the proposed method reduces the matching error by an average of 6.3%, requires smaller data size and costs less time. In addition, this method is robust to low-frequency, unstable noise trajectory data, and it is suitable for map matching of all bus routes.

Concerning the problem that data often has irrelevant or redundant features which affect the classification accuracy in pattern classification problems, a hybrid feature selection method based on Shapley value and Particle Swarm Optimization (PSO) was proposed to obtain the best classification results with the fewest features. Firstly, the Shapley value of game theory was introduced into the local search of PSO algorithm. Then,by calculating the Shapley value of each feature in the particle (feature subset), the feature with the lowest Shapley value was gradually deleted to optimize the feature subset and update the particle, and enhance the global search ability of the algorithm at the same time. Finally, the improved particle swarm algorithm was applied to feature selection. The classification performance and the number of selected features of the support vector machine classifier were used as feature subset evaluation criteria. The classification experiments were performed on 17 medical data sets with different characteristic quantities of UCI machine learning data sets and gene expression data sets. The experimental results show that the proposed algorithm can remove more than 55% irrelevant or redundant features in the datasets effectively, especially more than 80% in the medium and large datasets, and the selected feature subset also has better classification ability,the classification accuracy can be increased by 2 to 23 percentage points.