路网约束下基于动态速度模拟的个体轨迹重构OA

[Background]The rapid advancement in the Internet of Things(IoT)and smart-device technologies has enabled the monitoring of individual mobility trajectories.However,inherent posi-tioning errors in GPS and other location-tracking devices often result in recorded points that deviate from their true locations,particularly in dense urban road networks,making it challenging to accu-rately reconstruct actual travel paths.[Objective]This study proposes an individual-level trajectory-reconstruction method based on dynamic velocity simulation and map matching to continuously map discrete global positioning system(GPS)trajectories in urban road networks.The goal is to generate long-term high-precision continuous-movement trajectories of vehicles or individuals that adhere to road-network constraints.[Method]The proposed method integrates shortest-path search,dynamic velocity modeling,and time-driven interpolation,thereby establishing a four-stage reconstruction mechanism comprising spatial mapping,path planning,stochastic speed sampling,and temporal in-terpolation.[Data]Using mobile signaling data from 50 000 users in Shanghai,coupled with road-network topology data,the algorithm successfully generated highly refined trajectories that adhered to road-network constraints.[Conclusion]Compared with the three mainstream methods,(HMM-Re-construct,Map-Matching Plus and GAT-Traj),the proposed approach reduced the average trajectory error by 33.3％,21.1％,and 12.5％,respectively,demonstrating superior precision in trajectory recon-struction.The reconstructed trajectories accurately captured spatiotemporal traffic patterns,including rush-hour flow dynamics and polycentric mobility radiation.A cross-city validation(Beijing and Xia-men)confirmed the transferability of this approach,demonstrating its potential for traffic-flow pre-diction,OD analysis,and smart-city applications.