TY - JOUR
T1 - Assessing the predictive value of traffic count data in the imputation of on-street parking occupancy in Amsterdam
AU - Calvo, Pablo Martín
AU - Schotten, Bas
AU - Dugundji, Elenna R.
N1 - Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2021.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - On-street parking policies have a huge impact on the social welfare of citizens. Accurate parking occupancy data across time and space is required to properly set such policies. Different imputation and forecasting models are required to obtain this data in cities that use probe vehicle measurements, such as Amsterdam. In this paper, the usage of traffic data as an explanatory variable is assessed as a potential improvement to existing parking occupancy prediction models. Traffic counts were obtained from 164 traffic cameras throughout the city. Existing models for predicting parking occupancy were reproduced in experiments with and without traffic data, and their performance was compared. Results indicated that (i) traffic data are indeed a useful predictor and improves performance of existing models; (ii) performance does not improve linearly with an increase in the number of counting points; and (iii) placement of the cameras does not have a significant impact on performance.
AB - On-street parking policies have a huge impact on the social welfare of citizens. Accurate parking occupancy data across time and space is required to properly set such policies. Different imputation and forecasting models are required to obtain this data in cities that use probe vehicle measurements, such as Amsterdam. In this paper, the usage of traffic data as an explanatory variable is assessed as a potential improvement to existing parking occupancy prediction models. Traffic counts were obtained from 164 traffic cameras throughout the city. Existing models for predicting parking occupancy were reproduced in experiments with and without traffic data, and their performance was compared. Results indicated that (i) traffic data are indeed a useful predictor and improves performance of existing models; (ii) performance does not improve linearly with an increase in the number of counting points; and (iii) placement of the cameras does not have a significant impact on performance.
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U2 - 10.1177/03611981211029644
DO - 10.1177/03611981211029644
M3 - Article
AN - SCOPUS:85120032641
SN - 0361-1981
VL - 2675
SP - 330
EP - 341
JO - Transportation Research Record (TRR)
JF - Transportation Research Record (TRR)
IS - 12
ER -