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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Urban dispersal events are processes where an unusually large number ofpeople leave the same area in a short period. Early prediction of dispersalevents is important in mitigating congestion and safety risks and making betterdispatching decisions for taxi and ride-sharing fleets. Existing work mostlyfocuses on predicting taxi demand in the near future by learning patterns fromhistorical data. However, they fail in case of abnormality because dispersalevents with abnormally high demand are non-repetitive and violate commonassumptions such as smoothness in demand change over time. Instead, in thispaper we argue that dispersal events follow a complex pattern of trips andother related features in the past, which can be used to predict such events.Therefore, we formulate the dispersal event prediction problem as a survivalanalysis problem. We propose a two-stage framework (DILSA), where a deeplearning model combined with survival analysis is developed to predict theprobability of a dispersal event and its demand volume. We conduct extensivecase studies and experiments on the NYC Yellow taxi dataset from 2014-2016.Results show that DILSA can predict events in the next 5 hours with F1-score of0.7 and with average time error of 18 minutes. It is orders of magnitude betterthan the state-ofthe-art deep learning approaches for taxi demand prediction.

Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data