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/

Zendy Plus

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

Zendy Tools

Zendy Open

The parking issue is central in transport policies and drivers' concerns, butthe determinants of the parking search time remain relatively poorlyunderstood. The question is often handled in a fairly ad hoc way, or byresorting to crude approximations. Very recently, we proposed a more generalagent-based approach, which notably takes due account of the role of the streetnetwork and the unequal attractiveness of parking spaces, and showed that itcan be solved analytically by leveraging the machinery of Statistical Physicsand Graph Theory, in the steady-state mean-field regime. Although theanalytical formula is computationally more efficient than direct agent-basedsimulations, it involves cumbersome matrices, with linear size proportional tothe number of parking spaces. Here, we extend the theoretical approach anddemonstrate that it can be further simplified, by coarse-graining the parkingspot occupancy at the street level. This results in even more efficientanalytical formulae for the parking search time, which could be usedefficiently by transport engineers.

Parking search in urban street networks: Taming down the complexity of the search-time problem via a coarse-graining approach