Modeling Spatial Trajectories with Attribute Representation Learning


The widespread use of positioning devices has resulted in the generation of a large number of trajectories, each of which possesses four attributes: user ID, location ID, and time-stamp, as well as an implicit attribute known as activity type (which is analogous to the concept of "topic" in text mining). Existing works learn different attribute representations in order to model these trajectories. This can be done by either introducing latent activity types based on topic models or by transforming the location and time context into a low-dimensional space using embedding techniques. Both of these options are available. In this paper, we propose a comprehensive method that we will refer to as the Human Mobility Representation Model (HMRM). This method will simultaneously produce the vector representations of all four characteristics, both explicit and implicit. The benefits of using HMRM include the following: (1) it models the latent activity types and learns trajectory attribute embeddings in an integrated manner; and (2) it connects the activity-related distributions and these attributes embeddings by adding a newly designed collaborative learning component, and makes them mutually exchanged in order to take advantage of the best of both worlds. On two real check-in datasets obtained from Foursquare, we apply HMRM to both unsupervised and supervised tasks, including two activity evaluation tasks and two embedding evaluation tasks. These tasks are comprised of two activity evaluation tasks and two embedding evaluation tasks. According to the findings of the experiments, HMRM has the potential to not only improve performance in terms of capturing latent activity types but also learn better trajectory embeddings.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Multi-tier Workload Consolidations in the Cloud Profiling, Modeling and Optimization ABSTRACT: It is becoming increasingly important to cut down on tail latency in order to improve the experience that users have
PROJECT TITLE :Cloud-Based Fine-Grained Health Information Access Control Framework for LightweightIoT Devices with Dynamic Auditing and Attribute - 2018ABSTRACT:The eHealth trend has spread globally. Internet of Things (IoT)
PROJECT TITLE : TAFC: Time and Attribute Factors Combined Access Control on Time-Sensitive Data in Public Cloud - 2017 ABSTRACT: The new paradigm of outsourcing information to the cloud is a double-edged sword. On the one hand,
PROJECT TITLE : Full Verifiability for Outsourced Decryption in Attribute Based Encryption - 2017 ABSTRACT: Attribute based mostly encryption (ABE) may be a in style cryptographic technology to shield the safety of users’
PROJECT TITLE : Secure Authentication in Cloud Big Data with Hierarchical Attribute Authorization Structure - 2017 ABSTRACT: With the quick growing demands for the large knowledge, we tend to need to manage and store the massive

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry