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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The estimation of real-time Origin-Destination (OD) matrix for large network under data scarcity environment is presented. It focuses on four significant issues associated with data scarcity: 1) reduction of unknown variables by replacing OD flows with origin and destination flows; 2) consideration of noise in unknowns 3) development of a simulation algorithm to map unknowns and measurements precisely; 4) incorporation of speed data during feedback process. The approach is structured under unscented Kalman filter framework, featured by (a) a joint estimation model for estimating OD flows and route choices; (b) more sophisticated simulation model for mapping. It is empirically validated through a case study.

A COMPREHENSIVE APPROACH FOR DATA SCARCITY PROBLEM IN REAL-TIME OD MATRIX ESTIMATION