Â鶹AV

Ms. Sena Önen Öz, a doctoral student at Â鶹AV in the area of Operations Management will be presenting her research proposal entitled:

Curb Drivers’ Enthusiasm: How Payment Methods, Pricing Strategies, and Driver Behavior Shape the Urban Parking Experience

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Wednesday, October 9, 2024 at 9:00 am – 11:00 am

Student Committee Chair: ProfessorÌýMehmet Gumus and Professor Wei Qi

Please note that the presentation will be conducted on Zoom. The proposal is open only to the student and the committee members.

ABSTRACT

With growing concerns about urban congestion and the environmental impact of city transportation, my dissertation focuses on improving urban parking systems from both a behavioral and operational perspective.

The first study examines how different payment methods and hourly parking prices affect drivers’ parking behavior, street parking occupancy, and search time to find an available parking spot. Utilizing data from an online survey and high-resolution transaction records provided by a municipal agency in a densely populated North American city, this study reveals that mobile payment methods reduce parking durations, which in turn improve turnover rates and decrease overall search times. Furthermore, we observe that a driver’s parking behavior is influenced by both price and payment method interaction, making it essential for policy analysis to consider this interplay. With a discrete event simulation, we further demonstrate that progressive pricing, along with mobile payment adoption, significantly impacts both search time and occupancy compared to constant pricing.

The second study investigates the factors influencing drivers’ parking preferences. Inspired by consumer behavior theory, this study approaches parking spaces as substitutable products and explores how drivers make choices based on the attributes of parking spaces. We utilize choice-based conjoint analysis to model street parking demand under different conditions, such as pricing, payment options, and availability. By studying substitution behavior, our goal is to gain insights into how drivers react to limited parking and to propose strategies for optimizing parking allocation in areas with high demand.

The final study develops a strategic pricing model to optimize urban parking efficiency. Recognizing the high cost of parking sensors required by existing models, we aimed to design a more accessible pricing strategy that cities could implement without expensive infrastructure. Building on insights from our earlier simulation study, we use mathematical modeling to balance municipal objectives, such as reducing congestion and emissions, with drivers’ preferences for pricing and convenience. The study proposes practical, implementable pricing policies that enhance space utilization while promoting greener driving habits.

These studies provide insights for managing urban parking demand, reducing search times, and promoting sustainable driving behavior. They also provide actionable recommendations for municipalities to optimize parking systems and reduce urban congestion.