Graduation Year

2006

Document Type

Thesis

Degree

M.S.I.E.

Degree Granting Department

Industrial Engineering

Major Professor

Grisselle Centeno, Ph. D.

Keywords

Parking modeling, Logistic regression, Yield management, Pricing, Neural network, Prediction

Abstract

The time spent searching for a parking space increases air pollution, driver frustration, and safety problems impacting among other issues, traffic congestion and as consequence the environment. In the United States, parking represents a $20 billion industry (National Parking Association, 2005), and research shows that a car is parked on average 90 percent of the time. To alleviate this problem, more parking facilities should be built or intelligent models to better utilize current facilities should be explored. In this thesis, a general methodology is proposed to provide solutions to the parking problem. First, stated preference data is used to study drivers' choice/behavior. Parking choices are modeled as functions of arrival time, parking price, age, income and gender. The estimated values show that choice is relatively inelastic with respect to distance and more elastic with respect to price.

The data is used to estimate the price elasticity that induces drivers to change their behavior. Second, neural networks are used to predict space availability using data provided by a parking facility. The model is compared with traditional forecasting models used in revenue management. Results show that neural networks are an effective tool to predict parking demand and perform better than traditional forecasting models. Third, the price elasticity that induces drivers to change their choice or behavior is determined. Finally, taking as an input the forecasting results obtained from the neural network and the price elasticity, parking spaces are optimally allocated at different price levels to optimize facility utilization and increase revenue. This research considers a parking facility network consisting of multiple parking lots with two, three and four fare classes and utilizes revenue management techniques as a mean to maximize revenue and to stimulate and diversify demand.

The output indicates the number of parking spaces that should be made available for early booking to ensure full utilization of the parking lot, while at the same time attempting to secure as many full price parking spaces to ensure maximization of revenue.

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