Main content


Date created: | Last Updated:


Creating DOI. Please wait...

Create DOI

Category: Project

Description: Rivalry in consumption generates variation in the choice sets decision-makers face. Neglecting such variation generates biased demand estimates. Whereas previous research used information from periodic inventory systems to correct this bias, this study instead uses novel data from a real-time inventory system that records all public charging sessions by electric vehicles in a residential area of Amsterdam. For each transaction I can reconstruct a user's exact set of available alternatives at the time of arrival which allows me to impose deterministic choice set constraints. I show that this significantly improves demand forecasts for local charging facilities, with estimated differences between observed and latent demand reaching up to 40 percentage points at some locations. For the current demand and infrastructure, consumption rivalry however remains a local phenomenon limited to peak hours. A policy analysis suggests that it is not cost effective to install more capacity just to mitigate consumption rivalry. Instead, it may be better to implement policies aimed at increasing the utilization of existing capacity.

License: CC-By Attribution 4.0 International


Add important information, links, or images here to describe your project.


Loading files...


Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.