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 this research, we propose a series of methodologies to mine transit riderstravel pattern and behavioral preferences, and then we use these knowledges toadjust and optimize the transit systems. Contributions are: 1) To increase thedata validity: a) we propose a novel approach to rectify the time discrepancyof data between the AFC (Automated Fare Collection) systems and AVL (AutomatedVehicle Location) system, our approach transforms data events into signals andapplies time domain correlation the detect and rectify their relativediscrepancies. b) By combining historical data and passengers ticketing timestamps, we induct and compensate missing information in AVL datasets. 2) Toinfer passengers alighting point, we introduce a maximum probabilistic modelincorporating passengers home place to recover their complete transittrajectory from semi-complete boarding records.Then we propose an enhanceactivity identification algorithm which is capable of specifying passengersshort-term activity from ordinary transfers. Finally, we analyze thetemporal-spatial characteristic of transit ridership. 3) To discover passengerstravel demands. We integrate each passengers trajectory data in multiple daysand construct a Hybrid Trip Graph (HTG). We then use a depth search algorithmto derive the spatially closed transit trip chains; Finally, we use closedtransit trip chains of passengers to study their travel pattern from variousperspectives. Finally, we analyze urban transit corridors by aggregating thepassengers critical transit chains.4) We derive eight influential factors, andthen construct passengers choice models under various scenarios. Next, wevalidate our model using ridership re-distribute simulations. Finally, weconduct a comprehensive analysis on passengers temporal choice preference anduse this information to optimize urban transit systems.

Study on Key Technologies of Transit Passengers Travel Pattern Mining and Applications based on Multiple Sources of Data