01764122

Component

https://doi.org/10.1177/03611981211029644

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.

Pablo Martín Calvo https://orcid.org/0000-0001-6170-2573 © National Academy of Sciences: Transportation Research Board 2021.

TRBAM-21-00656

English

0000-0001-6170-2573

0000-0001-9626-2088

0000-0001-9277-9485

pp 330-341

2021-12

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures; Maps; References (28)

Forecasting; On street parking; Predictive models; Traffic data

Amsterdam (Netherlands)

Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Dec 23 2020 11:20AM