Imputing parking usage on sparsely monitored areas within Amsterdam through the application of machine learning

Jeroen Schmidt*, Elenna Dugundji, Bas Schotten

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

197 Downloads (Pure)

Abstract

Effective parking policy is essential for cities to reduce the demand their road networks experience and to combat their carbon footprints. Existing research in the application of machine learning to understand parking behavior assumes that cities have prohibitively expensive stationary parking sensors installed, while no research has yet attempted to use machine learning to impute for parking behavior using mobile probe data of sparsely monitored areas. To this end, this paper shows that it is indeed feasible to impute parking pressure (occupation as a percentage). Gradient boosted trees were found to perform the best with an R2 score of 0.20 and root mean squared error (RMSE) score of 0.087. This paper also found that three unique parking occupancy patterns exist in Amsterdam and that this information, in combination with neighborhood characteristics, has an impact on imputation under certain conditions.

Original languageEnglish
Pages (from-to)320-333
Number of pages14
JournalTransportation Research Record
Volume2675
Issue number11
Early online date22 Jul 2021
DOIs
Publication statusPublished - 1 Nov 2021

Bibliographical note

Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2021.

Fingerprint

Dive into the research topics of 'Imputing parking usage on sparsely monitored areas within Amsterdam through the application of machine learning'. Together they form a unique fingerprint.

Cite this