With the developing of smart sensors and mobile devices produces an increasing volume of data, and it captures the states of transportation infrastructures. Such data are collected and uploaded frequently, which forms the heavy data calculation and storage. Moreover, state of monitored object may be keeping the same or slight change according to a certain state during a period, such as moving vehicles on a certain path with a basic uniform speed. Therefore, if trajectory pattern of vehicles can be obtained through the state change mode, scale and update frequency of the data can be greatly reduced. Based on the above-mentioned ideas, we are aware that the trajectory data storage is divided into traceability storage and vector storage, where original sampled data from sensing device, and state vectors are extracted from the analysis of the original sample data. In this way, only a relatively small amount of vector data is stored. The system will not only effectively reduce the frequency of sampling data storage, but also reduce query and analysis operations involved with the amount of data. The vector function is used to represent road network with indexes, and the data query based on the road network is used to extract the semantic information. Our experimental results show that our proposed methodologies have significant improvements in intelligent transportation.