基于稀疏轨迹聚类的自驾车旅游路线挖掘

针对自驾车游客加油轨迹稀疏，还原真实旅游路线困难的问题，提出一种基于语义表示的稀疏轨迹聚类算法，用以挖掘流行的自驾车旅游路线。与基于轨迹点匹配的传统轨迹聚类算法不同，该算法考虑不同轨迹点之间的语义关系，学习轨迹的低维向量表示。首先，利用神经网络语言模型学习加油站点的分布式向量表示；然后，取每条轨迹中所有站点向量的平均值作为该轨迹的向量表示；最后，采用经典的k均值算法对轨迹向量进行聚类。最终的可视化结果表明，所提算法有效地挖掘出了两条流行的自驾车旅游线路。

Self-driving tour route mining based on sparse trajectory clustering

Aiming at the difficulty of constructing real tour routes from sparse refueling trajectories of self-driving tourists,a sparse trajectory clustering algorithm based on semantic representation was proposed to mine popular self-driving tour routes. Different from traditional trajectory clustering algorithms based on trajectory point matching,in this algorithm, the semantic relationships between different trajectory points were considered and the low-dimensional vector representation of the trajectory was learned. Firstly,the neural network language model was used to learn the distributed vector representation of the gas stations. Then,the average value of all the station vectors in each trajectory was taken as the vector representation of this trajectory. Finally,the classical k-means algorithm was used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm mines two popular self-driving tour routes effectively.