The accurate and timely information about parking occupancy and availability has played a crucial role to solve the smart city challenge related to mobility, by helping drivers to save their time and by avoiding waiting to find a space, to move smoothly, or be in traffic. In recent times, there has been growing interest in the use of Big Data and crowd-sourcing data for both research and commercial applications. However, several challenges remain to extract salient information for designing an accurate and timely parking recommendation system (PRS). Differently from the current state of the artwork our PRS extend the application of standard Machine Learning approaches by proposing the application of an additive regression model (Prophet model) fed by parking meters data (parking meters occurrences). The proposed PRS provides timely forecasting until the next month parking occupancy for each different area using different data sources and an additive-based model (Prophet model). The preliminary results related to the forecasting accuracy on a specific area confirmed how the proposed PRS framework is effective and accurate to provide the forecast of parking meters occurrences until the next month, with an R2 score up to 0.51. The obtained results suggest that the proposed approach is a viable solution for providing reliable forecasting of parking occupancy for different areas and different data sources by modeling non-linear, non-periodic, and weekly periodic changes of the parking meter data.
Analysis, Design and Implementation of a Forecasting System for Parking Lots Occupation
Romeo L.;Frontoni E.
2021-01-01
Abstract
The accurate and timely information about parking occupancy and availability has played a crucial role to solve the smart city challenge related to mobility, by helping drivers to save their time and by avoiding waiting to find a space, to move smoothly, or be in traffic. In recent times, there has been growing interest in the use of Big Data and crowd-sourcing data for both research and commercial applications. However, several challenges remain to extract salient information for designing an accurate and timely parking recommendation system (PRS). Differently from the current state of the artwork our PRS extend the application of standard Machine Learning approaches by proposing the application of an additive regression model (Prophet model) fed by parking meters data (parking meters occurrences). The proposed PRS provides timely forecasting until the next month parking occupancy for each different area using different data sources and an additive-based model (Prophet model). The preliminary results related to the forecasting accuracy on a specific area confirmed how the proposed PRS framework is effective and accurate to provide the forecast of parking meters occurrences until the next month, with an R2 score up to 0.51. The obtained results suggest that the proposed approach is a viable solution for providing reliable forecasting of parking occupancy for different areas and different data sources by modeling non-linear, non-periodic, and weekly periodic changes of the parking meter data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
