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

Most existing bike usage prediction studies aim at building models to fit continuous bike usage data rather than categorical data, which may result in an over-fitting problem and therefore reduce the potential of the model to capture more generalized trends in bike usage predictions. This study explores a multi-categorical probabilistic approach for sharing bike demand prediction. In order to overcome the weakness of using single point measurements to describe bike usage conditions, we prepare three alternatives to capture the range, local variation, and trend of bike usage over a short-time period. The suitable indicator variables are determined based on the Principal Component Analysis (PCA) results. The Gaussian Mixture Models (GMM) is adopted to cluster homogeneous bike usage states. Then, a Markov chain model is developed based on the identified states to forecast the categorical changes of bike usage. Finally, to examine the effectiveness of the proposed approach, the persistence model is employed as a benchmark and two measures-Percent Correct (PC) and Heidke Skill Score (HSS), are introduced to quantify categorical data prediction performance. The results show that the proposed approach is able to offer high accuracy, skill, reliability, and discrimination at suitable prediction intervals.

A Multi-Categorical Probabilistic Approach for Short-Term Bike Sharing Usage Prediction