Leveraging Graph Structure and Simple Recurrence for Map Matching
Existing solutions for map matching mainly rely on sequence-to-sequence models to capture the correlations within a trajectory while neglecting the correlation between road segments and trajectories as well as trajectory-road correlations.Meanwhile,recurrent neural networks suffer from inherent limitations in conducting computations efficiently in parallel.To fully exploit all the aforementioned correlations and to improve the model parallelism,a Graph-enhanced Map Matching model with Simple Recurrence(GMMSR)is proposed.The model captures the correlations between road segments and trajectories through road network convolution and trajectory graph convolution respectively,and exploits the trajectory-road correlation by aligning road network and trajectory representations in latent space.Moreover,the model utilizes simple recurrent units to achieve more efficient parallel computations.Extensive experiments on a map matching dataset in a subarea of Beijing demonstrate significant improvements in accuracy compared with existing baselines while achieving comparable or better efficiency.
map matchingtrajectory and road correlationsgraph neural networkssimple recurrence unit
万方数据
维普
