With the advent of fully grown intelligent transportation systems (ITS) and ongoing ubiquitous transportation systems (UTS), a data rich environment for transportation analysis has been matured. Using ITS or UTS archived data, in this thesis, two major applications were made with a hope to improve the practice of transportation planning, design and possibly operation. First, using archived volume and travel time data acquired from an urban transportation network, the estimation of origin-destination (OD) matrices for a relatively short time period (5 min) has been performed using a path-based dynamic traffic simulation model. The dynamic or temporal OD, as a result, has been successfully used for dynamic traffic assignment and traffic signal control, showing different measures of effectiveness in terms of vehicle hours traveled and total delay, respectively. The second application deals with the issue of the estimation of design hourly volume or the peak hour volume factor, so called K-factor, which influences the size of roadway facilities in the phase of planning and design. A new method overcoming the problems of the current K-factor has been devised using the statistical method with full one year archived data reserved in the Korean expressway archived data repository, called OASIS. Both results based on old and new K-factors have been compared, using the case of the expressway 50 of Korea, to observe the differences between the two approaches. The results were quite different in design hourly volumes and the required number of lanes. The method is rather quantitative compared with the traditional K30 approach and is supposed to be successfully used in other regions or countries. Examples in this thesis show only two applications for transportation planning and design. Numerous and better applications could be also possible in the area of traffic operations. The methods introduced in this research shed light on the prospective applications and can be integrated into the currently existing ADMS systems both in US and in Korea. For the better application, the issue of data sharing between agencies and other technical issues should also be addressed in the future.