Vehicle traveling time prediction is an important part of the research of intelligent transportation system. By now, there have been various kinds of methods for vehicle traveling time prediction. But few consider both aspects of time and space. In this paper, a vehicle traveling time prediction method based on grey theory （GT） and linear regression analysis （LRA） is presented. In aspects of time, we use the history data sequence of bus speed on a certain road to predict the future bus speed on that road by GT. And in aspects of space, we calculate the traffic affecting factors between various roads by LRA. Using these factors we can predict the vehicle＇s speed at the lower road if the vehicle＇s speed at the current road is known. Finally we use time factor and space factor as the weighting factors of the two results predicted by GT and LRA respectively to find the final result, thus calculating the vehicle＇s traveling time. The method also considers such factors as dwell time, thus making the prediction more accurate.
A novel intelligent drug delivery system potential for the more effective therapy of the diabetics was proposed, and the composition of system was analyzed. Based on the design of micro-electro-mechanical systems (MEMS), an iterative modeling process was introduced. Unified modeling language (UML) was em-ployed to describe the function requirement, and different diagrams were built up to explore the static model, the dynamic model and the employment model. The mapping analysis of different diagrams can simply verify the consistency and completeness of the system model.
