A Study on Construction CALS Big-Data Service

This paper presents the direction for the big data service technology development using the 20 years' accumulated data from the construction CALS system. For the analysis of big data, this paper presents the method of gathering, processing, and utilizing not only the data stored in the construction CALS system, such as those on vehicle inspection, vehicle overload, slopes, road occupation, and land compensation, but also external data, such as those on traffic volume, cargo vehicle DTG, industrial complexes, road shape, buildings, land, land price, weather, and space. Based on the results of the data analysis, this paper presents the direction for the development of four service technologies: the optimal location and time prediction service technology for vehicle overload crackdown, the prediction service technology for slope collapse risks, the prediction service technology for road occupation (linkage) approval sections, and the compensation cost prediction service technology. In addition, in 2020, such developed services will be test-applied to the staffers of Regional Construction and Management Administrations (RCMAs) and Regional Construction and Management Offices (RCMOs) under the Ministry of Land, Infrastructure, and Transport (MOLIT), and to the general public, to prove the effects of big-data services. Afterwards, the services will be gradually applied to the road management work of MOLIT.