With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features c...With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features can be abstracted as user roles.This role is closely related to the users’social background,occupation,and living habits.This study makes four main contributions to the literature.First,user feature models from different views for each user are constructed from the analysis of the check-in data.Second,the K-means algorithm is used to discover user roles from user features.Third,a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result.Finally,experiments are used to verify the validity of the method.The results show that the method can improve the effect of clustering by 1.5∼2 times,and improve the stability of the cluster results about 2∼3 times of the original.This method is the first time to apply reinforcement learning to the optimization of user roles in mobile applications,which enhances the clustering effect and improves the stability of the automatic method when discovering user roles.展开更多
Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic proc...Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.U1504602.
文摘With the widespread use of mobile phones,users can share their location and activity anytime,anywhere,as a form of check-in data.These data reflect user features.Long-term stability and a set of user-shared features can be abstracted as user roles.This role is closely related to the users’social background,occupation,and living habits.This study makes four main contributions to the literature.First,user feature models from different views for each user are constructed from the analysis of the check-in data.Second,the K-means algorithm is used to discover user roles from user features.Third,a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result.Finally,experiments are used to verify the validity of the method.The results show that the method can improve the effect of clustering by 1.5∼2 times,and improve the stability of the cluster results about 2∼3 times of the original.This method is the first time to apply reinforcement learning to the optimization of user roles in mobile applications,which enhances the clustering effect and improves the stability of the automatic method when discovering user roles.
文摘Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.
