TY - JOUR
T1 - Imputing parking usage on sparsely monitored areas within Amsterdam through the application of machine learning
AU - Schmidt, Jeroen
AU - Dugundji, Elenna
AU - Schotten, Bas
N1 - Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2021.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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.
AB - 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.
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U2 - 10.1177/03611981211017141
DO - 10.1177/03611981211017141
M3 - Article
AN - SCOPUS:85120034436
SN - 0361-1981
VL - 2675
SP - 320
EP - 333
JO - Transportation Research Record
JF - Transportation Research Record
IS - 11
ER -