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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Transit ridership flow and origin-destination (O-D) information is essentialfor enhancing transit network design, optimizing transit route and improvingservice. The effectiveness and preciseness of the traditional survey-based andsmart card data-driven method for O-D information inference have multipledisadvantages due to the insufficient sample, the high time and energy cost,and the lack of inferring results validation. By considering the ubiquity ofsmart mobile devices in the world, several methods were developed forestimating the transit ridership flow from Wi-Fi and Bluetooth sensing data byfiltering out the non-passenger MAC addresses based on the predefinedthresholds. However, the accuracy of the filtering methods is stillquestionable for the indeterminate threshold values and the lack ofquantitative results validation. By combining the consideration of the assumedoverlapped feature space of passenger and non-passenger with the aboveconcerns, a three steps data-driven method for estimating transit ridershipflow and O-D information from Wi-Fi and Bluetooth sensing data is proposed inthis paper. The observed ridership flow is used as ground truth for calculatingthe performance measurements. According to the results, the proposed approachoutperformed all selected baseline models and existing filtering methods. Thefindings of this study can help to provide real-time and precise transitridership flow and O-D information for supporting transit vehicle managementand the quality of service enhancement.

Mining Public Transit Ridership Flow and Origin-Destination Information from Wi-Fi and Bluetooth Sensing Data