[Purposes] Urban road networks, as critical infrastructure supporting the operation and development of cities, undergo continuous changes with the constant expansion and evolution of urban areas. This study aims to update road network information based on trajectory data, enabling closed-loop control and adaptive updates for Intelligent Transportation Systems. [Methods] An incremental road network update method based on road network mesh segmentation using trajectory data is proposed. Firstly, the trajectories are segmented using road network meshes as the control units, and an index is constructed based on the road network meshes to associate various elements. Next, a map-matching method based on point-level features is used to extract mismatched trajectory points, providing an initial detection of changes in the road network. Then, an incremental road extraction method is applied to extract candidate new roads from the mismatched trajectory points. Subsequently, a matching method based on line-level features is used to further screen out the new roads. Finally, topological updates are performed based on rules to achieve the update of the road network. [Findings] A road network update experiment was conducted using trajectory data and historical navigation electronic map data from Changsha City. In terms of qualitative analysis, the extracted new roads not only effectively connected the existing road network but also showed a high degree of alignment with OpenStreetMap (OSM) where there was trajectory coverage. Additionally, they included roads that were covered by trajectories but not recorded in OSM. In terms of quantitative analysis, 50 roads were randomly removed from the existing road network, and the proposed method was used for reconstruction, achieving a reconstruction rate of 88.00%. [Conclusions] This indicates that proposed method can achieve efficient detection of road network changes and effectively extract newly added roads, thus realizing the update of the road network.