In this paper, we study the traffic information granular computing theory and build traffic information fusion model, framework and implementation program based on granular computing. We raise uncertainty reduction algorithms for traffic flow prediction and congestion recognition algorithms based on granular computing theory, which will provide new ideas and methods in the complex decision making under uncertainty problems of the transportation systems. In an attribute based encryption scheme, each user is identified by a set of attributes, which are used to determined ecryption ability for each ciphertext. On the base of attribute based encryption and symmetric encryption algorithm, it proposes a verifiable multi-authority attribute based encryption scheme. In our scheme, access control permission is divided into two kinds: Read and Write for the first time. An encryptor can easily deal with this problem by using two different secret keys. When a user wants to get Read permission, he just needs to go to some authorities, yet not all. We also provide a verification scheme that ensures the integrity of data and realness of data sources. The encryptor generates a signature while encryption, the user carries out verification with given signature and the decrypted message. The scheme not only enhances the confidentiality of data, but also supports more flexible and more fine-grained access control strategy.