Title: Revealing the Hidden Costs of Driving to Better Inform Personal Transportation Mode Choices

Caleb Southern

Ph.D. student, Human-Centered Computing

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Date:Wednesday, April 19th, 2017

Time: 12:00 - 2:00 pm

Location: Centergy Suite 670, 75 Fifth Street NW (IPaT Yellow Jacket Room)

Committee:

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Dr. Gregory D. Abowd (Advisor, School of Interactive Computing, Georgia Tech)

Dr. Thad Starner (School of Interactive Computing, Georgia Tech)

Dr. Lauren Wilcox (School of Interactive Computing, Georgia Tech)

Dr. Kari Watkins (School of Civil and Environmental Engineering, Georgia Tech)

Dr. Jon Froehlich (Department of Computer Science, University of Maryland)

Abstract:

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Driving is the second highest expense for the average American household. Yet few people know the total cost of owning and operating their vehicles, and most cannot accurately estimate how much a common driving trip (like a daily commute) costs. Beyond the cost of fuel, the total cost of ownership for each vehicle trip includes hidden factors such as ownership (depreciation), maintenance, insurance, taxes, fees, and parking. There are a number of viable alternatives to driving alone for personal transportation, including ridesharing (carpools, vanpools), car services (e.g., Uber, Lyft), transit, biking, and walking. Cost is one factor people may consider in transportation mode choice. Increasing the awareness of the cost of driving is useful in making better informed personal transportation decisions.

The goal of my work is to explore the use of information technology to increase the awareness of the total cost of driving trips, and to understand the impact of revealing this cost on people's attitudes toward personal transportation mode choices. I first discuss my previous work in developing and evaluating a Trip Cost Meter, a system that reveals the total cost of a driving trip immediately after the completion of each trip. The Trip Cost Meter includes a personalized cost model that allocates the visible and hidden costs of owning and operating a vehicle to each driving trip, and instrumentation of the vehicle employing an On-Board Diagnostic (OBD) adapter and a smartphone. After using this Trip Cost Meter for one week, study participants were able to more accurately and confidently estimate the cost of their daily driving commute and other trips, and to retain this cost information after four months.

I will then discuss my proposed work. In the first study, I will explore the impact on awareness of: 1) instrumenting the vehicle; and 2) providing immediate in-vehicle cost feedback for each driving trip. In the previous work, I found that it is possible to reasonably estimate the cost of a driving trip using my cost model without instrumentation, and instead using the trip distance from a map and the vehicle's average fuel economy. Instrumenting a vehicle and providing in-vehicle feedback are expensive. It may be sufficient to reveal the driving trip costs to the user offline at a later time. If the impact on a user's awareness of trip cost does not depend on instrumenting the vehicle or on immediate in-vehicle feedback, the Trip Cost Meter can be deployed more easily and widely to a larger population in the final study. I will conduct a between-subjects study with three groups using three variations of the Trip Cost Meter: 1) instrumented vehicle with in-vehicle feedback; 2) instrumented vehicle with offline cost feedback; and 3) non-instrumented vehicle with offline cost feedback. For each group, I will asses the impact on awareness, confidence, retention of trip cost information, trust in the system, and changes in attitudes toward various modes of transportation.

In the final study, I will deploy the Trip Cost Meter with a population of employees who currently commute to work in Midtown Atlanta by driving alone. I will partner with a transportation organization that offers incentives to employees who choose alternative modes of transportation, as part of a best-practices Transportation Demand Management (TDM) program. In a between-subjects study, I will compare an experimental group (using the Trip Cost Meter intervention) to a control group to assess the impact of the Trip Cost Meter on attitudes toward alternative modes of transportation and on decisions to participate in the TDM program.