English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The wide spread use of positioning and photographing devices gives rise to adeluge of traffic trajectory data (e.g., vehicle passage records and taxitrajectory data), with each record having at least three attributes: object ID,location ID, and time-stamp. In this paper, we propose a novel mobility patternembedding model called MPE to shed the light on people's mobility patterns intraffic trajectory data from multiple aspects, including sequential, personal,and temporal factors. MPE has two salient features: (1) it is capable ofcasting various types of information (object, location and time) to anintegrated low-dimensional latent space; (2) it considers the effect of``phantom transitions'' arising from road networks in traffic trajectory data.This embedding model opens the door to a wide range of applications such asnext location prediction and visualization. Experimental results on tworeal-world datasets show that MPE is effective and outperforms thestate-of-the-art methods significantly in a variety of tasks.

MPE: A Mobility Pattern Embedding Model for Predicting Next Locations