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

"An advanced city is not a place where the poor move about in cars, ratherit's where even the rich use public transportation". This is what EnriquePenalosa, the celebrated ex-mayor of Bogota once said. However, in order toachieve this objective, one of the crucial properties that the publictransportation systems need to satisfy is reliability. While reliability isoften referenced with respect to on-schedule arrivals and departures, in thisstudy we are interested in the ability of the system to satisfy the totalpassenger demand. This is crucial, since if the capacity of the system is notenough to satisfy all the passengers, then ridership will inevitably drop.However, quantifying this excess demand is not straightforward since publictransit data, and in particular data from bus systems that we focus on in thisstudy, only include information for people that got on the bus, and not thosethat were left behind at a stop due to a full bus. In this work, we design aframework for estimating this excess demand. Our framework includes a mechanismfor identifying instances of potential excess demand, and a Poisson regressionmodel for the demand for a given bus route and stop. These instances ofpotential excess demand are filtered out from the training phase of the Poissonregression. We show through simulated data that this filtering is able toremove the bias introduced by the censored data logged by the system. Failureto remove these data points leads to an underestimation of the excess demand.We then apply our approach on real data collected from the Pittsburgh PortAuthority and estimate the excess demand over an one-year period.

Excess demand in public transportation systems: The case of Pittsburgh's Port Authority