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Top1. Introduction
Spatio-temporal data mining usually refers to mining or predicting the historical trajectory of moving objects (Radmanesh M,2021; Meng J J, 2016; Harwood A, 2018). Spatio-temporal trajectory data are many sample records from different sensing devices. The understanding and modeling of spatio-temporal trajectory data provides a new perspective for learning people's movement patterns, and it is also an auxiliary tool for urban planning and smart city management with great potential (Zhang M H, 2021; Pan X Y, 2019; Yang W L,2021). The semantic analysis model is widely used to analyze the various semantic relationships that may arise in the document. A new semantic analysis model is proposed to find possible time relationships between posts (Chen L C, 2019; Gao Q, et al., 2017) .
As more high occupancy toll (HOT) facilities are planned and under development, a comprehensive understanding of HOT operations is required for establishing appropriate HOT policies. To enhance the understanding, the factors affecting drivers’ choices on HOT lane use and carpooling are investigated in the Atlanta I-85 HOT corridors (Guensler R.,2020). A coevolutionary algorithm is proposed for two solution sets, population and archive, the objective-wise local search and set-based simulated binary operation are used in order to address the MOCSPTW (Huang S. C.,2020). An ant path-oriented carpooling allocation approach is proposed to solve this problem in the time domain (Huang S. C.,2019). Agreedy approach is propose based on the iterative matching and merging (Duan Y. B.,2020). An intelligent carpool system is first presented based on the service-oriented architecture, a fuzzy-controlled genetic-based carpool algorithm is proposed by using the combined approach of the genetic algorithm and the fuzzy control system (Huang S. C.,2015),
Trajectory semantic enhancement refers to the association and related application scene data in the original coordinate trajectory. The trajectory data is generated by the behavior characteristics of the moving object, it is called as semantic trajectory data. The additional data is also called as semantic tags. The tag information has three levels: the track layer, the sub-track layer, and the track point layer. The analysis of the semantic track mainly focuses on the sub-track layer. In conversion, the spatio-temporal information in the original trajectory is fused with the text data, and a trajectory with semantic labels is obtained, it is called as a semantic trajectory (Alvares L O, et al., 2007).