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

Designing spatio-temporal forecasting models separately in a task-wise andcity-wise manner poses a burden for the expanding transportation networkcompanies. Therefore, a multi-task learning architecture is proposed in thisstudy by developing gated ensemble of spatio-temporal mixture of expertsnetwork (GESME-Net) with convolutional recurrent neural network (CRNN),convolutional neural network (CNN), and recurrent neural network (RNN) forsimultaneously forecasting spatio-temporal tasks in a city as well as acrossdifferent cities. Furthermore, a task adaptation layer is integrated with thearchitecture for learning joint representation in multi-task learning andrevealing the contribution of the input features utilized in prediction. Theproposed architecture is tested with data from Didi Chuxing for: (i)simultaneously forecasting demand and supply-demand gap in Beijing, and (ii)simultaneously forecasting demand across Chengdu and Xian. In both scenarios,models from our proposed architecture outperformed the single-task andmulti-task deep learning benchmarks and ensemble-based machine learningalgorithms.

Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System