Vehicle Trajectory Clustering in Urban Road Network Environment Based on Doc2Vec Model

Trajectory clustering is an important task in trajectory data mining. Grouping a trajectory dataset into clusters based on the similarity between vehicle trajectories is conducive to revealing the movement pattern of the vehicles. For trajectory clustering in a real urban road network environment, the existing methods have some deficiencies, including high time complexity in measuring the distance between trajectory sequences and poor clustering performance. This paper proposes a method for clustering vehicle traj ectories in the urban road network environment based on a word vector model. First, the original Global Positioning System (GPS) trajectory data of vehicles are converted into road segment sequences by using the urban road segment information contained in the GPS trajectory data (after map-matching). Then, the spatial properties, such as the geographical location, time and moving direction of vehicles in the original trajectory sequence, are expressed by road segments and their order. Next, the road segment sequences are converted into traj ectory segment eigenvectors with fixed dimensions using the doc2vec model, in this way, the calculation efficiency of similarity between traj ectory segments of different lengths is improved. Finally, the vehicle tracks are clustered according to the distances between the traj ectory segment eigenvectors using the hierarchical clustering method. The result of a simulation based on taxi trajectory data gathered in a real urban road network shows that the proposed method was superior to the traditional clustering methods based on trajectory space-time distance, improving the silhouette index by 10%-25%, and reducing the clustering time by two orders of magnitude.