With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Curren...With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.展开更多
With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other...With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other external descriptive attributes(i.e.,travelling mode,activities,etc.).Trajectory recommendation is especially important to users for finding the routes meeting the user’s travel needs quickly.Most existing trajectory recommendation works return the same route to different users given an origin and a destination.However,the users’behavior preferences can be learned from users’historical multi-attributes trajectories.In this paper,we propose two novel personalized trajectory recommendation methods,i.e.,user behavior probability learning based on matrix decomposition and user behavior probability learning based on Kernel density estimation.We transform the route recommendation problem to a shortest path problem employing Bayesian probability model.Combining the user input(i.e.,an origin and a destination),the trajectory query is performed on a behavior graph based on the learned behavior probability automatically.Finally,a series of experiments on two real datasets validate the effectiveness of our proposed methods.展开更多
In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolut...In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61772249).
文摘With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.
基金This work was partially supported by the grant from the Natural Science Foundation of Hebei Province(F2021210005)the Hebei Province Innovation Capability Improvement Plan(21550803D)+2 种基金the Outstanding Youth Foundation of Hebei Education Department(BJ2021085)the Fourth Outstanding Youth Foundation of Shijiazhuang Tiedao University,and Training Project for Improving Students of Scientific and Technological Innovation Ability for College and Middle School(DXS202106)Scientific Research Project from China Railway Corporation(2020F026).
文摘With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other external descriptive attributes(i.e.,travelling mode,activities,etc.).Trajectory recommendation is especially important to users for finding the routes meeting the user’s travel needs quickly.Most existing trajectory recommendation works return the same route to different users given an origin and a destination.However,the users’behavior preferences can be learned from users’historical multi-attributes trajectories.In this paper,we propose two novel personalized trajectory recommendation methods,i.e.,user behavior probability learning based on matrix decomposition and user behavior probability learning based on Kernel density estimation.We transform the route recommendation problem to a shortest path problem employing Bayesian probability model.Combining the user input(i.e.,an origin and a destination),the trajectory query is performed on a behavior graph based on the learned behavior probability automatically.Finally,a series of experiments on two real datasets validate the effectiveness of our proposed methods.
基金supported by the National Natural Science Foundation of China(No.61273039)
文摘In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.
