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

We study revenue-optimal pricing and driver compensation in ridesharingplatforms when drivers have heterogeneous preferences over locations. If aplatform ignores drivers' location preferences, it may make inefficient tripdispatches; moreover, drivers may strategize so as to route towards theirpreferred locations. In a model with stationary and continuous demand andsupply, we present a mechanism that incentivizes drivers to both (i) reporttheir location preferences truthfully and (ii) always provide service. Insettings with unconstrained driver supply or symmetric demand patterns, ourmechanism achieves (full-information) first-best revenue. Under supplyconstraints and unbalanced demand, we show via simulation that our mechanismimproves over existing mechanisms and has performance close to the first-best.

Ridesharing with Driver Location Preferences