This paper investigates time-series correlations between macroscopic travel speed and parking garage occupancy in downtown area, using the real-time parking occupancy data via SFPark.org and travel speed data from HERE Maps for San Francisco downtown areas as a data-driven case study. This study significantly expands recent work on instantaneous correlations by incorporating variables as time series. The equivalency between the nonlinear regression with logistic curves and the single-node single hidden layer neural network is established. By testing time delay neural network models, this study investigates the time delay effects between macroscopic travel speed and parking garage occupancy. The performance of single-layer multi-nodes nonlinear autoregressive with exogenous inputs neural network is evaluated, which suggests such types of time series neural networks can effectively forecast macroscopic travel speed by using travel speed and parking occupancy information with various forecasting delay tabs.
Time series relations between parking garage occupancy and traffic speed in macroscopic downtown areas – a data driven study
Journal of Intelligent Transportation Systems ; 25 , 4 ; 423-438
2021-05-09
16 pages
Article (Journal)
Electronic Resource
Unknown
Relationships Between Parking Garage Occupancy and Traffic Speeds In Cities
Transportation Research Record | 2017
|Macroscopic Modeling of On-Street and Garage Parking: Impact on Traffic Performance
DOAJ | 2019
|Downtown Traffic and Parking Needs Related to Downtown Economic Trends
British Library Conference Proceedings | 1996
|Downtown Traffic and Parking Needs Related to Downtown Economic Trends
Online Contents | 1996
|Downtown Traffic and Parking Needs Related to Downtown Economic Trends
Transportation Research Record | 1996
|