Reserch On Method Of Pattern Classification Based On Multi-information Fusion And Application In Bus Passenger Flow Recongnition System

The passenger flow data is the basis of public transportation management. Correct and real-time data of passenger flow can provide the most important information for reasonably dispatching buses and optimizing lines of public transportation, and it can also show the actual number of passengers for checking back the cash in the cashbox conveniently. This paper presents some general methods of bus passenger flow recognition, and generalizes the advantages and disadvantages. The multi-information fusion technology is introduced into passenger flow recognition in this paper. Multiple source information fusion has widespread application and important theory significance in the area of information science and technology. Common multi-information fusion methods include weighting method, Bayes method, evidence combination theory, fuzzy logic and neural network, etc. The recognition results are dissatisfied under the conditions of small sample and high dimension space, because most of these methods are based on prior knowledge. Support Vector Machine (SVM) is introduced into multi-information fusion pattern classification to solve this problem. According to the practical problems of passenger flow recognition, the methods to improve training efficiency and speed up classification are researched on, and some new methods are proposed in this paper. On these bases, the multi-information fusion model of passenger flow recognition is established and the passenger flow collection vehicle terminal is developed and implemented.Recent years, support vector machine has become increasingly popular tools for machine learning tasks which involving pattern recognition, regression analysis and feature extraction. Because of large-scale training samples and outlier data immixed in the other class, there are some disadvantages of support vector machine such as slow learning speed, large buffer memory requirement, and low generalization performance. Aiming at these problems, a new reduction strategy for large-scale training samples according to the point set theory is proposed in this paper. This new strategy reduces the outlier data immixed in the other class and get the support vector by using fuzzy clustering. It can effectively avoid over-learning which is caused by outlier data, improve the generalization performance of the SVM learning machine and greatly reduce the scale of training samples. So the learning rate can be speed up and the classification accuracy can be unaffected. Effectiveness and feasibility of this strategy are proved by experiments.There is a bottleneck of Support Vector Machine: the speed of classification depends on the number of support vectors. Aiming at this, a fast classification algorithm of support vector machine is proposed. Firstly, it constructs the minimum spanning by introducing the similarity measure and divides the support vectors into groups according to the maximum similarity in feature space. After then, the determinant factor and the adjusting factor will be found in each group by some rules. In order to simplify the support vectors, the linear combination of"determinant factor"and"adjusting factor"will be used to fit the weighted sums of support vectors in feature space. Finally, the speed of classification will be improved significantly. Experimental results show that this algorithm can obtain higher reduction rate of classification time with minor loss of classification accuracy. It can also satisfy the requirements of real-time classification.In this paper, large numbers of experiments are carried out to research the pressure data of getting on/off the bus. It is proposed that pressure characteristic vector can be used for pattern classification when only one person getting on/off the bus. A new passenger flow recognition method based on multi-information fusion combining pressure data with timing information is presents for two persons getting on/off the bus at the same time, and the multi-information fusion model of passenger flow recognition is established. The experimental results show that the proposed model is effective and its precision is preferable. Finally, according to requirements of practical problem, passenger flow collection vehicle terminal is developed and implemented.