Self-driving tour route mining based on sparse trajectory clustering
YANG Fengyi1,2,3, MA Yupeng1,3, BAO Hengbin1,2,3, HAN Yunfei1,3, MA Bo1,2,3
1. The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi Xinjiang 830011, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi Xinjiang 830011, China
Abstract：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.
杨奉毅, 马玉鹏, 包恒彬, 韩云飞, 马博. 基于稀疏轨迹聚类的自驾车旅游路线挖掘[J]. 计算机应用, 2020, 40(4): 1079-1084.
YANG Fengyi, MA Yupeng, BAO Hengbin, HAN Yunfei, MA Bo. Self-driving tour route mining based on sparse trajectory clustering. Journal of Computer Applications, 2020, 40(4): 1079-1084.
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2020, Vol. 40 
