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

In recent years, deep learning approaches have been proved good performancein traffic flow prediction, many complex models have been proposed to maketraffic flow prediction more accurate. However, lacking transparency limits thedomain experts on understanding when and where the input data mainly impact theresults. Most urban experts and planners can only adjust traffic based on theirown experience and can not react effectively toward the potential traffic jam.To tackle this problem, we adapt Shapley value and present a visualizationanalysis system , which can provide experts with the interpretation of trafficflow prediction. TrafPS consists of three layers, from data process to resultscomputation and visualization. We design three visualization views in TrafPS tosupport the prediction analysis process. One demonstration shows that theTrafPS supports an effective analytical pipeline on interpreting the predictionflow to users and provides an intuitive visualization for decision making.

TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley