Parking is mostly seen in a strategic point of view in terms of demand and supply. There is little knowledge about modeling the user's behavior and the time needed to park a car. The focus of this project is on modeling off-street parking at educational institutes based on user’s behavior. In this study two types of multiple linear regression models using SPSS software were developed. Disaggregate model and aggregate model. Disaggregate model study determines the most suitable independent variables affecting the parking demand based on user’s socio economic behavior. The demand for this model is expressed in terms of parking usage per person per day in hours at educational institutions in Srinagar city. The disaggregate model analysis revealed that the most influencing independent variables for this model are travel distance and income per month for employees and visitors with the coefficient of variation(R)=0.96 and coefficient of determination(R2) =0.92 and family income and travel distance for students with the coefficient of variation(R)=0.98 and coefficient of determination(R2) =0.96. Aggregate model study determines the most suitable independent variable for estimating the parking demand (vehicle hours) or parking supply (space hours) at educational institutions. The formula emulated can be used to establish the number of parking bays to be provided to accommodate the parking needs at educational institutions. The aggregate model analysis predicts that most fitting independent variable for determining the parking demand formula is the number of employees working at the institution with the coefficient of variation(R)=0.997 and coefficient of determination (R2) =0. 993.The most suitable model is that for which the coefficient of variation(R) and coefficient of determination (R2) is nearly equal to one. Goodness of fit test or significance test and validation test has been conducted on the developed equations and can be used with a high level of confidence.
Latif, M., Singla, S., & Vadav, V. (2019). Modeling off-street parking based on user‟s behavior using spss software. International Journal of Innovative Technology and Exploring Engineering, 8(8), 2360–2372.