The United States has the world’s largest road network with over 4.1 million miles of roads supporting more than 260 million of registered automobiles including around 11 million of heavy trucks. Such a large road network challenges the road and traffic management systems such as condition assessment and traffic monitoring. To assess the road conditions and track the traffic, currently, multiple facilities are required simultaneously. For instance, vehicle-based image techniques are available for pavements’ mechanical behavior detection such as cracks, high-speed vehicle-based profilers are used upon request for the road ride quality evaluation, and inductive loops or strain sensors are deployed inside pavements for traffic data collection. Having multiple facilities and systems for the road conditions and traffic information monitoring raises the cost for the assessment and complicates the process. In this study, an integrated system is developed to simultaneously monitor the road condition and traffic using in-pavement strain-based sensors, which will phenomenally simplify the road condition and traffic monitoring. To accomplish such a superior system, this dissertation designs an innovative integrated sensing system, installs the integrated system in Minnesota's Cold Weather Road Research Facility (MnROAD), monitors the early health conditions of the pavements and ride quality evaluation, investigates algorithms by using the developed system for traffic data collection especially weigh-in-motion measurements, and optimizes the system through optimal system design. The developed integrated system is promising to use one system for multiple purposes, which gains a considerable efficiency increase as well as a potential significant cost reduction for intelligent transportation system.