To the shortage of theoretical support in the policy-making process of traffic guidance management, the research method of choice behavior with confinement mechanism of traffic information was proposed. From the perspective of human perception, the deep analysis of Multi-Source Traffic Information (MSTI) constraint rule was presented based on fuzzy clustering algorithm, then the road network environment was simulated by VISSIM and the traffic state pattern recognition model was established to simulate the mental activity of traveler under restriction of information. Then by means of Biogeme software, the choice model was constructed based on the behavior survey data, which was obtained in the road network example by using Stated Preference (SP) investigate method. Results show that the sanction of traffic information on travel behavior is very limited and the travelers prefer the preference path when traffic of this preference path is not very heavy, while this sanction enhances gradually and the path change behavior, which is influenced by the information, becomes more frequent when the preference path is more congested. The conclusions provided a new idea and reference for incomplete rational behavior research under the information environment, and also provided decision support for traffic management department.

Concerning low classification accuracy and high false alarm rate of current intrusion detection models, an intrusion detection classification model based on Landmark ISOmetric MAPping (LISOMAP) and Deep First Search (DFS) Relevant Vector Machine (RVM) was proposed. The LISOMAP was adopted to reduce the dimension of the training data, and RVM based on the DFS was used for classification detection. Compared with the Principal Components Analysis (PCA)-Supported Vector Machine (SVM), the experimental results indicate that the LISOMAP-DFSRVM model has lower false alarm rate with almost the same detection rate.