Airborne Laser Scanning (ALS) systems enable the acquisition of an accurately georeferenced set of 3D dense point cloud data that can be used to develop more efficient approaches for managing road infrastructures. These systems can contribute to the production of useful knowledge about road median, which is a narrow strip of land that separates traffic on opposite sides of the road. The road median is one of the fundamental feature, whose correct identification is a prerequisite to obtain precise information about road and other objects along it. The acquired ALS data can be used to locate, measure and classify the road median in a timely and cost-effective manner in order to facilitate their maintenance. In this paper, we present an automated algorithm for extracting road median from ALS data. We use the road vector polylines to reduce the search space in the LiDAR data, which enables a more accurate estimation of the road median. The frequency distribution of elevation values is used to remove the crossing highways above the road sections. We threshold the LiDAR elevation and intensity attributes to get an initial estimation of the road median. We make the morphological operations based knowledge analysis to complete the shape of road median and remove other road surface elements that are introduced through the use of thresholding. We tested our algorithm on two 1-km road sections consisting of distinct types of road medians based on concrete and grass-hedge barrier. The successful extraction of medians along these two road sections demonstrate the robustness of our automated algorithm. These research findings provide valuable insight and prototype road median extraction tool-set for both national road authorities and survey companies.