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

Shared mobility services require accurate demand models for effective serviceplanning. On the one hand, modeling the full probability distribution of demandis advantageous because the entire uncertainty structure preserves valuableinformation for decision-making. On the other hand, demand is often observedthrough the usage of the service itself, so that the observations are censored,as they are inherently limited by available supply. Since the 1980s, variousworks on Censored Quantile Regression models have performed well under suchconditions. Further, in the last two decades, several papers have proposed toimplement these models flexibly through Neural Networks. However, the models incurrent works estimate the quantiles individually, thus incurring acomputational overhead and ignoring valuable relationships between thequantiles. We address this gap by extending current Censored QuantileRegression models to learn multiple quantiles at once and apply these tosynthetic baseline datasets and datasets from two shared mobility providers inthe Copenhagen metropolitan area in Denmark. The results show that our extendedmodels yield fewer quantile crossings and less computational overhead withoutcompromising model performance.

Modeling Censored Mobility Demand through Quantile Regression Neural Networks