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

Problems of dynamic origin-destination (OD) estimation using smart card datacan be modelled using entropy maximisation and solved for large networks usingsolution techniques such as Lagrangian relaxation. In this paper, we give anoverview of the research literature about OD estimation. We show how entropymaximisation can be used to model this problem in case station-entry data fromsmart cards is the only available information, i.e. number of entries is knownbut not exits nor the flow between the stations. The large entropy maximisationprogram is solved using Lagrangian relaxation. The model is tested on a casestudy from the commuter train service in Stockholm with smart card entry-datafrom a working day in 2015. The results show that, given the entry-data, theentropy maximisation-based model and methods allows to find an OD-estimate thatis as accurate as the reported estimates from manual observations. Theestimates are further improved when using relevant assumptions and additionalavailable data.

Dynamic Origin-Destination Estimation Using Smart Card Data: An Entropy Maximisation Approach