关键词: 图卷积, 最优路径, A*算法, 射频识别技术（RFID）, 时空图卷积

Abstract: The optimal path search and planning in the road network has received widespread attention as an important part of location-based services (LBS). Positioning technologies such as radio frequency identification (RFID) have brought a lot of traffic data, which has become the foundation and challenge of research. Travel scenarios in cities are very sensitive to the dynamic changes of the road network. At the same time, the traffic situation in the city is complex and changeable, and the real road network and the trajectories of moving objects contain rich temporal and spatial semantic information. These are difficulties faced by the optimal route search in the road network. In response to these challenges, after analyzing the deficiencies of existing algorithms, a machine learning model GCN-Search based on graph convolutional networks for deep search is proposed, with reference to the heuristic idea of A* algorithm. Firstly, the model uses the spatio-temporal graph convolutional network to aggregate the spatio-temporal information of adjacent areas and past periods to model the recent dynamic changes of the road network on which urban travel depends. Secondly, the model expands the search depth, defines the depth evaluation value of the node, and uses a neural network to replace the artificially designed evaluation function to search for nodes that are conducive to the overall optimal path until the final path is generated. Comparative experiments on the RFID dataset provided by a traffic management bureau show that the GCN-Search algorithm can effectively use the temporal and spatial semantic information of RFID data, and can improve the accuracy of the optimal route search for short-term travel on the dynamic road network.

Key words: graph convolution, optimal path, A* algorithm, radio frequency identification (RFID), spatio-temporal graph convolution