Intro

<div class="footer-small">
<span>
<a href="#background">Background</a> | <a href="#choose">Conjoint Studies</a> | <a href="#packages">Software</a> | <a href="#cars">Vehicle Listings Project</a>
</span>
</div>

---

]

### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M304 128a80 80 0 1 0 -160 0 80 80 0 1 0 160 0zM96 128a128 128 0 1 1 256 0A128 128 0 1 1 96 128zM49.3 464H398.7c-8.9-63.3-63.3-112-129-112H178.3c-65.7 0-120.1 48.7-129 112zM0 482.3C0 383.8 79.8 304 178.3 304h91.4C368.2 304 448 383.8 448 482.3c0 16.4-13.3 29.7-29.7 29.7H29.7C13.3 512 0 498.7 0 482.3z"/></svg> John Paul Helveston, Ph.D.

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.6l-224 96c-14 6-21.8 21-18.7 35.8S16.8 160 32 160v8c0 13.3 10.7 24 24 24H456c13.3 0 24-10.7 24-24v-8c15.2 0 28.3-10.7 31.3-25.6s-4.8-29.9-18.7-35.8l-224-96c-8-3.4-17.2-3.4-25.2 0zM128 224H64V420.3c-.6 .3-1.2 .7-1.8 1.1l-48 32c-11.7 7.8-17 22.4-12.9 35.9S17.9 512 32 512H480c14.1 0 26.5-9.2 30.6-22.7s-1.1-28.1-12.9-35.9l-48-32c-.6-.4-1.2-.7-1.8-1.1V224H384V416H344V224H280V416H232V224H168V416H128V224zM256 64a32 32 0 1 1 0 64 32 32 0 1 1 0-64z"/></svg> Dept. of Engineering Management and Systems Engineering

]

---

# Hello World!

.leftcol30[.circle[
<img src="https://www.jhelvy.com/images/lab/john_helveston_circle.png" width="300">
]]
.rightcol70[

### John Paul Helveston, Ph.D.

.font80[
Assistant Professor, Engineering Management & Systems Engineering
Website: [www.jhelvy.com](http://www.jhelvy.com/)

- 2010 BS in Engineering Science & Mechanics at Virginia Tech
- 2015 MS in Engineering & Public Policy at Carnegie Mellon University
- 2016 PhD in Engineering & Public Policy at Carnegie Mellon University
- 2016-2018 Postdoc at [Institute for Sustainable Energy](https://www.bu.edu/ise/), Boston University
]]

---

## Technology Change Lab

> I study how consumers, firms, markets, and policies affect technology change to <br> facilitate transitions to sustainable and low-carbon technologies.

.cols3[
### .center[Electric & Sustainable Vehicle Technologies]
<center>
<img src="images/ev.png" width=280>
</center>
]

.cols3[
### .center[Market & Policy Analysis]
<center>
<img src="images/market.png" width=250>
</center>
]

.cols3[
### .center[U.S. - China Climate Relationship]
<center>
<img src="images/uschina.png" width=100%>
</center>
]

---

## I'm interested in questions like...

<br>

### - How can we get people to buy more efficient vehicles?

### - How will emerging technology like autonomous and electric vehicles compete against existing technologies?

### - Would people be willing to pay a premium to reduce pollution?

## **Answers depend on knowing what people want**

---

background-color: #000
class: center, middle, inverse

# So I try to figure out what people want

---

## Which feature do you care more about?

.cols3[
## .center[Battery Life?]
<center>
<img src="images/phone_battery.png" width=100%>
</center>
]

.cols3[
## .center[Signal quality?]
<center>
<img src="images/phone_signal.png" width=100%>
</center>
]

---

## **Conjoint Analysis**:
## Use choice data to model preferences

---

### .center[Use random utility framework to predict probability of choosing phone _j_]

<br>

### 1. `$u_j = \beta_1\mathrm{price}_j + \beta_2\mathrm{brand}_j + \beta_3\mathrm{battery}_j + \beta_4\mathrm{signal}_j + \varepsilon_j$`

### 2. Assume `$\varepsilon_j \sim$` iid Gumbel distribution

### 3. Probability of choosing phone _j_: `$P_j = \frac{e^{\beta'x_j}}{\sum_k^J e^{\beta'x_k}}$`

### 4. Estimate `$\beta_1$`, `$\beta_2$`, `$\beta_3$`, `$\beta_4$` via maximum likelihood estimation

---

<br>

### `$P_j = \frac{e^{\hat{\beta}'x_j}}{\sum_k^J e^{\hat{\beta}'x_k}}$`

<center>
<img src="images/phone_price_sens.png" width=500>
</center>
]

---

# Choose your own adventure

## [.red[Electric Vehicles]](#ev)
## [.orange[Low-carbon Fuels]](#fuel)
## [.yellow[Multi-modal Trips]](#modes)
## [.green[Autonomous Vehicles]](#av)
## [.blue[Electric Vehicle Incentives]](#incentive)
## [.purple[Electric Vehicle Smart Charging Programs]](#charging)

---

## Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China

Helveston, John P., George Washington University

Yimin Liu, Ford

Elea M. Feit, Drexel U.

Erica R.H. Fuchs, CMU

Erica Klampﬂ, Ford

Jeremy J. Michalek, CMU

]

_Transportation Research Part A: Policy and Practice_, 73, 96–112. (2015)
DOI: 10.1016/j.tra.2015.01.002

Funding:

- NSF
- Ford Foundation

]

---

---

.leftcol35[
## Chinese car buyers may be more willing to adopt full electric vehicles than Americans.
]

---

# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

---

## Choice at the Pump: Measuring Preferences for Lower-Carbon Combustion Fuels?

Stephanie M. Seki, CMU

Jihoon Min, CMU

Evelyn Fairman, CMU

Arthur A. Boni, CMU

Jeremy J. Michalek, CMU

Inês M. L. Azevedo, CMU
]

_Environmental Research Letters_, 14(8) (2019)
DOI: 10.1088/1748-9326/ab2bd2

]

---

background-color: #fff

---

## On average, respondents WTP $150/ton CO2 avoided

- 26 mpg car
- 12-gallon tank
- Gas: $3/gallon

**A WTP of $150/ton CO2 avoided means increasing fuel price by 45%!**
]

---

# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

---

## Measuring consumer preferences for multi-modal trips

<br>

Lujin Zhao, GWU

David Broniatowski, GWU

Zoe Szajnfarber, GWU

John P. Helveston, GWU

]

Zhao, L., Szajnfarber, Z., Broniatowski, D.A., & Helveston, J.P. (2023) “Using conjoint analysis to incorporate heterogeneous preferences into multimodal transit trip simulations” _Systems Engineering_. DOI: [10.1002/sys.21670](https://doi.org/10.1002/sys.21670)

Funding: Toyota Mobility Foundation

]

---

---

---

<br>

---

# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

---

# Undercutting Transit?

## Exploring potential competition between autonomous vehicles and public transportation in the U.S.

<br>

Leah Kaplan, GWU

John P. Helveston, GWU

]

Kaplan, Leah R. & Helveston, John P. (2023) “Undercutting Transit? Exploring potential competition between autonomous vehicles and public transportation in the U.S.” _Transportation Research Record_. DOI: [10.1177/03611981231208976](https://doi.org/10.1177/03611981231208976)

Funding: NSF

]

---

background-color: #fff
class: middle, center

## Imagine you are going out for an evening leisure activity - <br> Which transportation option would you choose/

---

background-color: #fff
class: center, middle

---

background-color: #fff
class: center, middle

---

background-color: #fff
class: center, middle

---

background-color: #fff
class: center, middle

---

# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

---

## Not All Subsidies are Equal:<br>Measuring Preferences for EV Financial Incentives

<br>

Laura Roberson, GWU

John P. Helveston, GWU

]

Roberson, Laura A. & Helveston, John P. (2022) “Not all subsidies are equal: Measuring preferences for electric vehicle financial incentives” _Environmental Research Letters_. 17(084003). DOI: [10.1088/1748-9326/ac7df3](https://doi.org/10.1088/1748-9326/ac7df3)

Funding: Alfred P. Sloan Foundation

]

---

background-color: #fff
class: middle, center

# Which incentive option would you prefer?

---

background-color: #fff
class: middle

]

## Sample

- Used [formr.org](https://formr.org/) Survey Platform
- Fielded September, 2021
- Nationwide sample of Dynata panel

]

---

background-color: #fff
class: center, middle

### Immediate rebate is **$1,400** more valuable than tax credit

---

background-color: #fff
class: center

### Immediate rebate even more preferred for **low-income households**

---

background-color: #fff

---

# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

---

## Grid-Integration of Electric Vehicles:<br>Consumer Preferences for Smart Charging Programs

Pingfan Hu, GWU

Brian Tarroja, UC Irvine

Matthew Dean, UC Irvine

Kate Forrest, UC Irvine

Eric Hittinger, RIT

Alan Jenn, UC Davis

John Paul Helveston, GWU

]

_Under Review_

Funding: Alfred P. Sloan Foundation

]

---

# Unmanaged EV charging causes problems

# "Smart charging" can help

---

background-color: #f8f7f1
class: center

## SMC - Supplier Managed Charging

### SMC smooths out overnight EV charging demand & lowers peak load

---

background-color: #f8f7f1
class: center

## SMC - Supplier Managed Charging

### SMC smooths out overnight EV charging demand & lowers peak load

---

background-color: #f8f7f1
class: center

## V2G - Vehicle to Grid

---

## Sample SMC Question

---

## Sample V2G Question

---

## Enrollment Sensitivity

---

## Enrollment Scenarios

---

# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

---

# .blue[.center[[`logitr`](https://jhelvy.github.io/logitr/)]]

Fast estimation of multinomial and mixed logit models in R with “Preference” space or “Willingness-to-pay” space utility parameterizations.

[https://jhelvy.github.io/logitr/](https://jhelvy.github.io/logitr/)

]

# .blue[.center[[`cbcTools`](https://jhelvy.github.io/cbcTools/)]]

Tools for designing choice based conjoint (cbc) survey experiments and conduction power analyses.

[https://jhelvy.github.io/cbcTools/](https://jhelvy.github.io/cbcTools/)

]

---

background-color: #fff

# `surveydown`

**surveydown** is an open-source, markdown-based platform for
interactive and reproducible Surveys using
[R](https://www.r-project.org/), [Quarto](https://quarto.org/),
[Shiny](https://shiny.posit.co/), and PostgreSQL databases like
[Supabase](https://supabase.com/).

[https://surveydown.org/](https://surveydown.org/)

]

]

---

# .center[Analyzing historical vehicle listings data]

]

- New and used cars from 2016-2021
- ~60,000 dealerships

Powertrain | Listings 
-----------|-----------
Gasoline   | 39,118,862
Hybrid     | 1,121,846
Battery Electric (BEV) | 349,506
Plug-In Hybrid (PHEV) | 181,461

]

---

## .center[EV owners drive less than gasoline car owners]

Zhao, L., Ottinger, E., Yip, A., & Helveston, J.P. (2023) “Quantifying electric vehicle mileage in the United States” _Joule_. 7, 1–15. DOI: [10.1016/j.joule.2023.09.015](https://doi.org/10.1016/j.joule.2023.09.015)

---

# .center[Estimating residual value of EVs]

Roberson, Laura A., Pantha, S., & Helveston, J.P. (2024) “Battery-Powered Bargains? Assessing Electric Vehicle Resale Value in the United States” _Environmental Research Letters_. DOI: [10.1088/1748-9326/ad3fce](https://doi.org/10.1088/1748-9326/ad3fce)

---

]

## .center[Where are the EVs?]

]

---

### .center[EVs are disproportionately supplied to ZEV states]

---

<br>

# .center[.font150[Thanks!]]

### .center[Slides: https://github.com/jhelvy/research]

@JohnHelveston <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg><br>
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