In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the featu...In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.展开更多
The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with se...The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices.However,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the servers.Due to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these servers.Unfortunately,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment.We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users.Firstly,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving points.Additionally,our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory.Furthermore,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge settings.We test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.展开更多
基金The National Natural Science Foundation of China(No.41471371)
文摘In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.
文摘The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices.However,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the servers.Due to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these servers.Unfortunately,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment.We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users.Firstly,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving points.Additionally,our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory.Furthermore,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge settings.We test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.
