Quantifying the Economic and ... - Webber Energy Group · 9/13/2017 · 1 Analytical framework...

Quantifying the Economic and Energetic Impacts of Mobility Services

Prepared for: Oak Ridge National Laboratory

United States Department of Energy

Attn: Dr. Zhenhong Lin

Transportation Energy Evolution

Modeling Program

Subcontract Number: 4000150102

Investigators: Dr. F. Todd Davidson, Gordon T. Tsai, and Dr. Michael E. Webber

The University of Texas at Austin

Final Report

September 13, 2017

Quantifying the Economic and Energetic Impacts of Mobility Services 1

Executive Summary This document provides a summary of transportation research that was completed at the University of

Texas at Austin from September 2016 through September 2017 under subcontract 4000150102, funded

by Oak Ridge National Laboratory.

The purpose of this project was to develop a framework for quantifying the economic and energetic

impacts of mobility services versus personal car ownership. Our hypothesis was that if you include the

hidden cost of car ownership then mobility services might be a compelling economic choice for more

people than conventional wisdom suggests. These hidden factors include the costs for garaging, parking,

and time lost while driving. Subsequently, if mobility services are an affordable option for many

consumers, then the use of the services should continue to rise. That growth would cause an unclear

outcome associated with fuel consumption and associated emissions.

The primary outcomes of this

project were an analytical

framework and interactive

calculator1 for quantifying

economic tradeoffs of mobility

services versus personal car

ownership. This work can be used

to inform consumers, drivers,

mobility service companies, and

policy makers about the economic

and energetic impacts of future

transportation options, including

the impact of automated vehicles.

The analytical framework found

that a large portion of the

American population, potentially in excess of 10-20% of the working population, would be economically

rational if they chose to use mobility services for a vast preponderance of their daily transportation needs.

The economic impact of using mobility services is very sensitive to how an individual values their time,

and whether they consider use of mobility services to be a productive means of recovering time. The work

also found that increased adoption of carpooling and automated vehicles have the potential to

dramatically lower the cost per person, per trip for transportation network companies (TNC). The

decrease in trip cost has the potential to make mobility services the most compelling economic option for

a significant portion of the population.

The impact of mobility services on energy consumption and environmental impacts is currently unclear.

Depending on the choice of vehicle (e.g. electric vs internal combustion), the adoption of carpooling, and

the development of automated vehicles, mobility services have the potential to increase or decrease

energy consumption and environmental impacts depending on subsequent impacts to total vehicle miles

1 http://www.rideordrive.org/calculator

This work developed an analytical framework to study the impacts of

mobility services and an online calculator to inform stakeholders.

http://www.rideordrive.org/calculator


traveled (VMT). Additional work is required to develop a greater understanding of the economic,

energetic, and environmental tradeoffs of mobility services.

The following sections are intended to provide a high-level summary of the methods that were

developed and the results that were produced as part of this work. Additional detail regarding project

methods and results are contained in the slide deck that was provided as part of the final briefing of

this work, titled “Quantifying the Value of Mobility Services_UT Austin_Supplemental Info_2017-09-

13.pdf”.

Introduction The expansion of transportation network companies (TNCs) such as Uber and Lyft have disrupted the traditional view of personal mobility. The promise of expanded on-demand transportation and the coming wave of automated vehicles will be a paradigm shift in how people move from one location to another, with significant energy implications. This trend raises the need to better understand the consumer acceptance of new mobility services. However, it is unclear whether this shift in behavior to mobility services makes economic sense for the rider rather than conventional car ownership. Because the economic considerations are non-obvious from an individual’s perspective, it is difficult to project total usage and whether the transition to mobility services will increase or decrease energy consumption. Consequently, understanding the economic considerations for whether an individual will shift to mobility services should enable a better understanding of broader, macroscopic energetic trends, leading to a better capability to predict the cumulative (society-wide) economic and energetic impacts.

The goal of this work was to fill that first knowledge gap by developing a framework for quantifying the economic and energetic impact of mobility services versus personal car ownership. Our hypothesis was that if you include the hidden cost of car ownership then mobility services might be a compelling economic choice for more people than conventional wisdom suggests. These hidden factors include the costs for garaging, parking, and time lost while driving. Subsequently, if mobility services are an affordable option for many consumers, then the use of the services should continue to rise resulting in what is currently an unclear outcome associated with fuel consumption and associated emissions.

Importantly, the framework was developed so that personal factors (e.g. local price of gasoline, the value of an individual’s time, and prevailing property tax rates) were accounted for to provide customized guidance on the economics of owning a car versus using mobility services. Using the framework developed by this study, an online calculator and interactive tool were built that can be disseminated to the public to inform them on whether or not mobile services is economically preferable for their unique living situation.

Project Tasks Task # Description Due Date Status

1 Analytical framework January 9, 2017 Complete

2 Develop online calculator March 9, 2017 Complete

3 Economic impact of carpooling May 9, 2017 Complete

4 Economic impact of automated vehicles July 9, 2017 Complete

5 Final report & briefing September 13, 2017 Complete


Methods The first step to develop a framework for comparing the economic value of mobility services versus

personal car ownership was to understand all of the relevant costs for both transportation modes. A list

of relevant inputs to the model are provided in Table 1.

Table 1: Model inputs for comparing the economic choice between mobility services and personal car ownership.

At its most basic level, the framework developed under this study uses the model inputs to compute the total cost for owning a car compared to the total cost of hiring mobility services. If the cost of hiring mobility services is less than the cost to own a car, then the model suggests that the economically rational thing to do would be to use mobility services for all transportation needs rather than purchasing a car. This is described as 𝐶𝐻𝑖𝑟𝑒 < 𝐶𝑜𝑤𝑛 (1) where 𝐶𝐻𝑖𝑟𝑒 and 𝐶𝑜𝑤𝑛 are the total cost to hire mobility services and the total cost to purchase and operate a vehicle over the lifetime of ownership, respectively. The cost of these two options is shown in greater detail as

𝐶𝐻𝑖𝑟𝑒 = ∑ [𝐶𝑇𝑁𝐶(𝑦) + 𝐶𝐼𝑛𝑣𝑒𝑠𝑡(𝑦) + 𝐶𝑡𝐻𝑖𝑟𝑒(𝑦)]𝑌

𝑦=1 (2)

and

Model Inputs for Determining the Economic Value Personal Car Ownership

of Using Mobility Services as Compared to Personal Car Purchase

Car Ownership Sticker Price $

Sales Tax % %

Global Inputs Purchase Fees $

Annual Miles Driven miles/year Down Payment $

Trips per Day trips/day Loan

Average Trip Duration minutes/trip Loan Term years

Fuel Efficiency miles/gallon Annual Interest Rate %

Fuel Price $/gallon Vehicle Ownership Length

Value of Time $/hour Planned Time to Own years

Rate of Return on Investment %/year Depreciated Value when Sold $

Direct Vehicle Expenses

Mobility Service Interest $

Rates to Hire Maintenance & Repair $

Fixed Fee $ Insurance $

Distance Fare $/mile Fuel $

Time Fare $/min Registration & Taxes $

Minimum Fare $ Indirect Vehicle Expenses

Average Surge Rate multiplier of fare Garage Property Taxes $/year

Rides During Surge % Garage Maintenance $/year

Riding Behavior Parking $/year

Average Wait Time for Ride minutes Average Walk to Parked Car minutes

Ride Recovered Productively % of Trip Duration Time for Vehicle Maintenance hour/year

Time for Vehicle Fueling minutes/week


𝐶𝑜𝑤𝑛 = ∑ [𝐶𝑐𝑎𝑟(𝑦) + 𝐶𝐷𝐸(𝑦) + 𝐶𝐼𝐸(𝑦) + 𝐶𝑡𝑜𝑤𝑛(𝑦)] − 𝑉𝑐𝑎𝑟(𝑌)𝑌

𝑦=1 (3)

Table 2 provides a description of each variable used in the

model. Many of the variables presented in Table 2 are

dependent on multiple inputs that are shown in Table 1.

The analytical framework was used to create a first

generation model that was built in Excel and a second

generation model that was built in Python. The second

generation model allowed for more complex scenario

analysis to better understand the conditions that result in

mobility services being economically preferable to personal

car ownership.

Future work should further develop the second generation

model to consider additional real-world aspects, such as

using probability distributions for inputs and accounting for

regional variations in transportation behavior.

Online Calculator One of the primary deliverables from this project was to

develop an interactive, online calculator that allows

individuals, policymakers, and corporations to consider the opportunity for mobility services vs. personal

car ownership based on the framework described in the Methods section. The online calculator is

available at www.rideordrive.org and provides interactive ability to modify the inputs presented in Table

1 so that the calculator provides customized feedback for the user’s unique transportation behavior.

Figure 1: An online calculator available at www.rideordrive.org was created as part of this work to allow people to compare the economic value of mobility services vs. personal car ownership as described by the analytical framework.

Model Variables

Hire Mobility Service

Total Cost to use Mobility Services

Total Fare to Hire a TNCLost Opportunity to Invest Cash that

is used to Pay for Hiring a TNC

Cost of Unproductive Time Spent

Resulting from Hiring a TNC

Personal Car Ownership

Total Cost to Own a Car

Total Cost to Purchase Car

Direct Expenses for Owning Car

Indirect Expenses for Owning Car

Cost of Unproductive Time Spent

Resulting from Owning a Car

Depreciated Value of Car at End

of Ownership

Nomenclature

Total Length of Ownership (years)

A Year Between Zero and Total

Length of Ownership

𝐶𝐻𝑖𝑟𝑒

𝐶𝑇𝑁𝐶

𝐶𝐼𝑛𝑣𝑒𝑠𝑡

𝐶𝑡𝐻𝑖𝑟𝑒

𝐶𝑜𝑤𝑛

𝐶𝑐𝑎𝑟

𝐶𝐷𝐸

𝐶𝐼𝐸

𝐶𝑡𝑜𝑤𝑛

𝑉𝑐𝑎𝑟

𝑌

𝑦

Table 2: Description of the variables used in the analytical framework.

http://www.rideordrive.org/

http://www.rideordrive.org/


Analytical Results The analytical framework developed for this work was used to study the economic opportunity for

mobility services to replace traditional car ownership. To assess the potential for mobility services, over

90,000 different scenarios were studied by varying the inputs to the analytical model (e.g. changing the

purchase price of a vehicle and/or changing the number of miles an individual drives every year).

Figure 2 provides an introduction to how results from the second generation model were analyzed. Figure

2 displays a heat map of the economically rational choice between personal car ownership (“Own a Car”)

and mobility service (“Hire TNC”). The heat map provides results for how the economically rational choice

might change as one considers different prices of purchasing a car and different values that one would

assign to their time. The results presented in a heat map are also dependent on the additional inputs that

are presented in Table 1.

The heat map on the right side of Figure 2 presents additional information regarding median car prices

and median wages. As reported by the United States Social Security Administration, the median reported

wage was approximately $14 per hour in 20152. The average purchase price of a car in the United States

was approximately $33,000 in 2016, according to Kelley Blue Book3 (it’s worth noting that incentives often

lower the car price by a few thousand dollars). Understanding the distribution of car prices and wages

provides important information for interpreting the heat maps and approximating the percentage of the

population that would be better off using mobility services.

The value of an individual’s time (e.g. what is the $ per hour that someone would assign to the time they

spend driving?) is one of the most important factors in determining the costs of driving in a car we own

vs. hiring a TNC to provide all transportation needs. The subjective nature of the value of time leads to a

variety of questions to help frame how someone might quantify the value of their time: How much would

someone pay to not be stressed driving around town? How much would someone pay to catch up on

email, read a book, or take a nap? Alternatively, how much could someone earn if they took care of those

tasks while riding? Some professions are more suited to using time riding in a productive way: it’s probably

easier for a lawyer to clock billable hours while riding to work than a plumber, for example. Due to the

subjective nature of valuing an individual’s time, the heat maps provide a wide range of $/hr to help

consider a variety of conditions. The online calculator4 that was developed as part of this work provides

the ability to tailor the value of time to match the unique perspective of the user.

2 U.S. Social Security Administration, “Measure of Central Tendency for Wage Data”, 2015, https://www.ssa.gov/oact/cola/central.html 3 Kelley Blue Book, “New-Car Transaction Prices Up 2 Percent in March 2016, Along with Increases in Incentive Spend”, March 2016, https://mediaroom.kbb.com/new-car-transaction-prices-up-2-percent-march-2016 4 http://www.rideordrive.org/calculator

https://www.ssa.gov/oact/cola/central.html

https://mediaroom.kbb.com/new-car-transaction-prices-up-2-percent-march-2016



http://www.rideordrive.org/calculator


Figure 2: The analytical results of this work helps determine whether mobility services or personal car ownership is the more appealing economic choice. The diagonal line shown on the left figure represents the conditions for which owning a car and hiring a TNC have equivalent economic appeal. The figure on the right shows how the majority of potential mobility service customers in the United States exist within the bottom left corner of the heat map and, for the condition tested, the majority of individuals are better off owning a car rather than using mobility services. The price for an average car was obtained from Kelly Blue Book and the median wage was obtained from the U.S. Social Security Administration (SSA). The “Minimum Value” of $20/hr was included to recognize that some individuals might consider the value of their time as being separate from the amount of money that they make and report to the SSA.

Figure 3 provides a snapshot of 90,000 different mobility scenarios by varying the annual miles driven, the

cost per mile that the TNC charges, the price to purchase a car, and the monetary value that the rider

assigns to the time that they can recover by using a service for transportation needs rather than driving

themselves. The results make a clear case that as the cost per mile drops and the annual miles driven

reduces, the case for mobility services becomes increasingly compelling. On the other hand, the average

annual miles driven in the United States is approximately 13,000 miles5, meaning that many people will

be better off owning their own vehicle if TNC companies are unable to lower the fares they charge.

The Supplemental Information provides additional, more detailed analysis of the heat map results.

Additional heat maps are also presented that provide insight into the ability for carpooling with mobility

services to lower the cost per person, per trip.

5 https://www.fhwa.dot.gov/ohim/onh00/bar8.htm

https://www.fhwa.dot.gov/ohim/onh00/bar8.htm


Figure 3: The economic appeal of mobility services varies widely depending on the unique conditions of the rider. Provided here is a snapshot of 90,000 different mobility scenarios by varying the annual miles driven, the cost per mile that the TNC charges, the price to purchase a car, and the monetary value that the rider assigns to the time that is recovered by using a service for transportation needs rather than driving themselves. The numbers on the x and y axes of each heat map are identical to the range of numbers shown on the x and y axes in Figure 2.

The analytical framework developed under this project was also used to assess the economic impact of

automated vehicles (AV). An important aspect was to consider how operation and maintenance (O&M)

costs might change as a result of deploying automated vehicles. For instance, if Level-5 autonomous

vehicles are fully realized, it seems likely that a TNC would purchase a fleet of electrified AV’s. O&M costs

for electrified AV’s might be significantly less than current vehicles. An anecdotal example leads one to

consider whether electrified AV’s might lower O&M costs to below $0.10 per mile6.

To analyze one aspect of the potential impact of AV’s on mobility services, the framework was used to

determine the potential drop in trip cost as a result of falling O&M costs. The results are presented in

Figure 4 and show a potential reduction in trip cost of approximately 40-70%. These results were

determined by assuming that a TNC would maintain pre-automation revenue per trip and pass on all

savings to the rider. It is also possible that a TNC would offer some savings to the rider, while also

increasing their own revenue.

An important consideration is that AV’s might reduce the appeal of mobility services. A personally owned

AV offers the ability for the rider to recover lost time and thus one of the primary benefits of mobility

services is no longer as compelling compared to personal AV ownership.

6 http://www.tesloop.com/blog/2017/8/30/tesla-model-s-hits-300k-miles-with-less-than-11k-maintenance-costs

6,000

10,000

14,000

$1.00/mile $1.50/mile $2.00/mile

Annual Miles Driven

TNC Cost per Mile

http://www.tesloop.com/blog/2017/8/30/tesla-model-s-hits-300k-miles-with-less-than-11k-maintenance-costs


Figure 4: Automated vehicles have the potential to significantly lower the cost of mobility services. The results presented assume that the TNC maintains pre-automation revenue while returning all cost-savings to the rider. The pre-automation trip cost was based on a distance fare of $1.50 per mile, a time fare of $0.20, and a base fare of $2.00.

The final aspect of this project was to assess the energetic impacts that mobility services might have on

personal transportation. As shown in the Sankey diagram in Figure 5, transportation consumed 28

quadrillion BTU (Quads) of energy in 2015 (approximately 28% of all energy consumption in the United

States that year). This energy consumption includes trains, planes, and ships, in addition to automotive

vehicles. In recent years, domestic light-duty vehicles consumed approximately 16 Quads7 and accounted

for nearly 2.8 trillion vehicle miles traveled (VMT)8 per year.

To answer the question of the

energetic impact of mobility

services, we compared four

simplified case studies of how

the vehicle fleet might

change in the future as more

mobility services are

adopted. A summary of the

different cases is provided in

Table 3. The variations

between the different cases

are described by changes in

the fuel efficiency of the LDV

fleet and the number of LDV

mile traveled every year. It

was assumed that vehicles

7 U.S. Energy Information Administration, “Transportation Energy Use: Highway: Light-Duty Vehicles, High incentive, AEO2016”, https://www.eia.gov/opendata/qb.php?category=2119625&sdid=AEO.2016.HIGHINNOVATE.CNSM_NA_TRN_HWY_LDV_NA_NA_TRLBTU.A 8 Federal Highway Administration, “FHWA Forecasts of Vehicle Miles Traveled (VMT): Spring 2017 https://www.fhwa.dot.gov/policyinformation/tables/vmt/vmt_forecast_sum.pdf

Figure 5: Energy flow through the United States in 2015 by source and sector. Approximately 28% of all energy consumption is for transportation. Source: Lawrence Livermore National Laboratory, modified to highlight transportation energy consumption

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$- $0.10 $0.20 $0.30 $0.40 $0.50 $0.60

Cost per Mile to Operate Vehicle ($/mile)

Percent Reduction in Trip Cost by using Automated Vehicles

https://www.eia.gov/opendata/qb.php?category=2119625&sdid=AEO.2016.HIGHINNOVATE.CNSM_NA_TRN_HWY_LDV_NA_NA_TRLBTU.A



https://www.fhwa.dot.gov/policyinformation/tables/vmt/vmt_forecast_sum.pdf


operating for the purpose of

mobility services would drive an

additional 10% in terms of

mileage9. Based on the results of

this study, it was assumed that

25% of existing LDV needs could

be replaced by mobility services.

As an example, for Case 3, it was

assumed that 25% of the existing

fleet would be replaced by new

internal combustion engine (ICE) vehicles with a fuel efficiency of 30 miles per gallon of gasoline. These

new vehicles would offer mobility services. Case 4 assumed that 25% of the existing fleet would be

replaced by new EV’s with a fuel efficiency of 3 miles per kilowatt-hour of electricity.

Figure 6 describes how domestic automotive energy consumption might change based on the cases

presented in Table 3. An important takeaway is that despite increased fuel efficiency, the additional miles

driven by TNC vehicles might result in automotive energy consumption remaining relatively flat. Energy

consumption begins to drop more significantly if electric vehicles are adopted. However, for the results

presented, the upstream conversion efficiency of electricity generation is not included. The conversion

efficiency of primary fuels to electricity was not accounted for because it can vary widely depending on

the technology chosen. Future work should consider additional details regarding the implications of how

carpooling and electrified AV’s might increase or decrease automotive energy consumption.

9 Loeb, B., Kockelman, K., “Fleet Performance and Cost Evaluation of a Shared Autonomous Electric Vehicle Fleet: A Case Study for Austin, Texas” Autonomous Vehicles Symposium 2017

Case 1 Case 2 Case 3 Case 4

Total 22.5 22.9 21.6 19.6

Light-Duty 15.9 16.3 15.0 13.0

Other Vehicles 6.6 6.6 6.6 6.6

0

5

10

15

20

25

30

Qu

adri

llio

n B

TU (

Qu

ads)

Domestic Automotive Energy ConsumptionFigure 6: Domestic automotive energy consumption might rise or fall as a result of increased mobility services. For the cases considered, only LDV’s were considered eligible to be replaced by mobility services. “Other Vehicles” includes heavy duty trucks and buses.

Case LDV Vehicles LDV VMT

1 Status quo Status quo

2 Status quo Increase 25% of fleet by 10%

3 Replace 25% of existing

fleet with new ICE vehicles Increase 25% of fleet by 10%

4 Replace 25% of existing

fleet with new EV's Increase 25% of fleet by 10%

Table 3: Case studies for considering energy impacts of mobility services. Abbreviations - Light-duty vehicle (LDV), Vehicle Miles Traveled (VMT)


Conclusions & Future Work The costs of traditional car ownership consist of far more than just the price of the car. There is interest,

insurance, taxes, fuel, and maintenance, among other expenses. Some expenses are non-obvious such as

parking, property taxes, construction costs for home garages, and, importantly, the value of our time. The

average American spends 335 hours10 annually behind the wheel while driving over 13,000 miles. Add in

the amount of time that we spend maintaining, cleaning, and managing our cars and it becomes clear that

America’s focus on personal car ownership is costing us a significant amount of time.

The purpose of this project was to take a holistic approach and account for all of the obvious and non-

obvious costs associated with personal car ownership to determine the conditions where mobility services

might be a more economically rational choice to replace all transportation needs.

This work found that a large portion of the American population, potentially in excess of 10-20% of the

working population, would be economically rational if they chose to use mobility services for a vast

preponderance of their daily transportation needs. The economic benefit of using mobility services is very

sensitive to how an individual values their time and whether they consider use of mobility services to be

a productive means of recovering time. The work also found that increased adoption of carpooling and

automated vehicles have the potential to dramatically lower the cost per person, per trip for TNCs. The

decrease in trip cost has the potential to make mobility services the most compelling economic option for

a significant portion of the population.

The impact of mobility services on energy consumption and environmental impacts is currently unclear.

Depending on the choice of vehicle (e.g. electric vs internal combustion), the adoption of carpooling, and

the development of automated vehicles, mobility services have the potential to increase or decrease

energy consumption and environmental impacts.

Future work should further develop the framework to accommodate more complex, interrelated inputs

to the models. Additional work is also required to describe spatial and temporal variations in how an

individual rider might value mobility services compared to personal car ownership. Finally, the improved

models should be used to develop a greater understanding of the energetic and environmental impacts

of mobility services.

Acknowledgements This work was funded by Oak Ridge National Laboratory and the U.S. Department of Energy with

intellectual contributions from Zhenhong Lin, Ph.D.

10 Bureau of Transportation Statistics, National Household Travel Survey Daily Travel Quick Facts, https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/national_household_travel_survey/daily_travel.html

https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/national_household_travel_survey/daily_travel.html

https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/national_household_travel_survey/daily_travel.html


Supplemental Info A high resolution version of the Supplemental Info is provided in the slide deck that was provided as part of the final briefing of this work, titled “Quantifying the Value of Mobility Services_UT Austin_Supplemental Info_2017-09-13.pdf”.

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