Abstract: The road network with complete and accurate information, as one of the key foundations of Smart City, bears significance in fields like urban planning, traffic managing and public traveling, et al. However, long manufacturing period of road network data, based on traditional surveying methods, often leaves it in an inconsistent state with the latest situation. Recently, positioning techniques ubiquitously used in mobile devices has been gradually coming into focus for domestic and overseas scholars. Currently, most of approaches, generating or updating road networks from mobile location information, are to compute with GPS trajectory data directly by various algorithms, which lead to expensive consumption of computational resources in case of mass GPS data covering large-scale areas. For this reason, we propose a spiral update strategy of road network data based on map-matching technology, which follows a “identify→analyze→extract→update” process. The main idea is to detect condemned road segments of existing road network data with the help of HMM for each trajectory input, as well as repair them, on the local scale, by extracting new road information from trajectory data.The proposed approach avoids computing on the entire dataset of trajectory data for road segments. Instead, it updates information of existing road network data by means of focalizing on the minimum range of potential condemned segments. We evaluated the performance of our proposals using GPS traces collected on taxies and OpenStreetMap(OSM) road networks covering urban areas of Wuhan City.

Key words: map-matching, condemned road segments, partial update