UC Irvine

Parking cost is an important and sensitive factor in understanding travel behavior and is typically utilized in the mode choice model of regional demand forecasting models. There are various socio-economics variables that can affect the value of parking cost by employment type, time periods, and trip purposes. In this study, a set of parking cost forecasting models are developed using survey data and local socio-economic data with the objective of identifying parking cost patterns and forecasting future parking costs.

This study first summarizes methods applied in previous parking cost forecasting models. Two categories of models were estimated. The first category does not consider parking space supply as a factor in forecasting TAZ parking; the second category considers both parking space supply and parking demand as explanatory variables. For each category, using current off-street parking cost survey data, linear regression models are built for hourly, daily and monthly pricing for SCAG Tier 2 Transportation Analysis Zones (TAZ) using R and Matlab. Daily parking rates are set as the base rates to generate the hourly and monthly parking cost models. The consideration of parking demand is a major contribution of this study, with demand generated based on home-based-work trip attractions for commuters by income groups in all models. This study found that daily parking rates can be explained by total employment, the proportion of office to total jobs, and the proportion of multiple to total households. Hourly parking cost can be explained based on daily parking rates and travel behavior associated with education, hospital, finance, entertainment and other employment types. The monthly parking cost model is built base on both daily and hourly parking rates as independent variables. Future work includes, integration of on-street parking costs with the current models for off-street parking.