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

Public transportation is a fundamental infrastructure for the daily mobilityin cities. Although its capacity is prepared for the usual demand, congestionmay rise when huge crowds concentrate in special events such as massivedemonstrations, concerts or sport events. In this work, we study the resilienceand recovery of public transportation networks from massive gatherings by meansof a stylized model mimicking the mobility of individuals through themultilayer transportation network. We focus on the delays produced by thecongestion in the trips of both event participants and of other citizens doingtheir usual traveling in the background. Our model can be solved analyticallyfor regular lattices showing that the average delay scales with the number ofevent participants with an exponent equal to the inverse of the latticedimension. We then switch to real transportation networks of eight worldwidecities, and observe that there is a whole range of exponents depending on wherethe event is located. These exponents are distributed around 1/2, whichindicates that most of the local structure of the network is two dimensional.Yet, some of the exponents are below (above) that value, implying a localdimension higher (lower) than 2 as a consequence of the multimodality andmultifractality of transportation networks. In fact, these exponents can bealso obtained from the scaling of the capacity with the distance from theevent. Overall, our methodology allows to dynamically probe the localdimensionality of a transportation network and identify the most vulnerablespots in cities for the celebration of massive events.

Scaling in the recovery of urban transportation systems from special events