Forecasting the demand for electric vehicles: Accounting for attitudes and perceptions

Glerum, Stankovikj, Bierlaire, 2014, in Transportation Science

doi:10.1287/trsc.2013.0487
Location Switzerland
Population Other (specify)
Sample size 666 (phase 1), 593 (phase 2)
Factor analysis type exploratory factor analysis, unknown rotation
Stepwise regression no
Removal of insignificant variables no
Reviewed by LCM

Abstract

In the context of the arrival of electric vehicles on the car market, new mathematical models are needed to understand and predict the impact on the market shares. This research provides a comprehensive methodology to forecast the demand of a technology that is not widespread yet, such as electric cars. It aims at providing contributions regarding three issues related to the prediction of the demand for electric vehicles: survey design, model estimation, and forecasting. We develop a stated preferences (SP) survey with personalized choice situations involving standard gasoline/diesel cars and electric cars. We specify a hybrid choice model accounting for attitudes toward leasing contracts or practical aspects of a car in the decision-making process. A forecasting analysis based on the collected SP data and additional market information is performed to evaluate the future demand for electric cars. © 2014 INFORMS.

Factors

Variable Pattern loading
Design is a secondary element when purchasing a car, which is above all a practical transport mode (Other) 1
I give more importance to my vehicle's spaciousness or capacity to transport people and luggage than its look (Other) 0.67
I prefer having a car with a new propulsion technology to a car with a nice look (Other) 0.509
Variable Pattern loading
Leasing is an optimal contract that enables me to change my car frequently (Other) 1
With a leasing contract, I feel that the car does not belong to me completely (Other) -0.679
I prefer to pay the total price of my car at one time to avoid having to allow a leasing budget every month (Other) -1.04
A leasing contract is more adapted in the case of the purchase of an electric vehicle (Other) 0.809
As the technology of an electric car's battery will evolve rapidly, its lease is more adapted, implying its replacement by a more efficient battery when it does not work in an optimal way anymore (Other) 0.547

Models

Dependent variable Proleasing
Model type Latent variable model
Sample size 593.0
R2 0.47
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero -39348.0
Log-likelihood at constants nan
Log-likelihood at convergence -20982.0
Proleasing
Variable Coefficient p-value
nan 2.92 0.0
Presence of children 0.238 0.001
Age 30-50 -0.135 0.001
Household of couple with children -0.215 0.002
Living in shared housing -0.502 0.003
Retired -0.389 0.0
Smartphone ownership 0.264 0.0
French speaker -0.363 0.0
German speaker -0.56 0.0
Holds university degree -0.144 0.005
Monthly income 4000-6000 CHF 0.158 0.027
Monthly income 6000-8000 CHF 0.166 0.007
Monthly income above 8000 CHF -0.0107 0.834
Random variable -0.0974 0.0
Dependent variable Proconvenience
Model type Hybrid choice model
Sample size 593.0
R2 0.47
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero -23496.0
Log-likelihood at constants nan
Log-likelihood at convergence -12379.0
Proconvenience
Variable Coefficient p-value
nan 2.28 0.0
Male -0.171 0.001
Household size 0.131 0.0
Age 45+ 0.00604 0.0
Retired 0.418 0.0
Homeowner 0.161 0.001
Random variable 0.0378 0.115
Dependent variable Vehicle choice
Model type Logit model
Sample size 593.0
R2 0.37
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero -3556.0
Log-likelihood at constants nan
Log-likelihood at convergence -2215.0
Competitor's gasoline
Variable Coefficient p-value
Constant -2.71 0.0
Families with children -0.157 0.171
Monthly income above 8000 CHF -0.223 0.055
French speaker 0.373 0.003
Age 0.0172 0.0
Recent and prospective buyers 1.6 0.0
Renault customers 0.104 0.912
Price -3.6 0.0
Competitor's gasoline, Renault customers
Variable Coefficient p-value
PT commuter -2.64 0.0
Number of cars -0.664 0.061
Competitor's gasoline, recent buyers, prospective buyers, future Renault customers, newsletter members
Variable Coefficient p-value
PT commuter -0.259 0.05
Number of cars -0.207 0.006
Renault electric - Fluence
Variable Coefficient p-value
Use cost electric - high -0.264 0.028
Renault electric - Zoé
Variable Coefficient p-value
Use cost electric - high -0.802 0.0
Use cost electric - med -0.514 0.001
Renault's electric, Recent and prospective buyers
Variable Coefficient p-value
Price -0.365 0.01
Renault's electric, Renault customers
Variable Coefficient p-value
Price 0.342 0.036
Renault's electric, future Renault customers and newsletter members
Variable Coefficient p-value
Price -0.152 0.184
Renault's gasoline, Renault customers
Variable Coefficient p-value
PT commuter -1.17 0.0
Number of cars -0.945 0.0
Price -0.29 0.29
Renault's gasoline, recent buyers, prospective buyers, future Renault customers, newsletter members
Variable Coefficient p-value
PT commuter -0.577 0.0
Number of cars -0.193 0.021
Price -1.39 0.0
Renualt gasoline
Variable Coefficient p-value
Constant -2.17 0.0
Families with children 0.183 0.119
Monthly income above 8000 CHF -0.259 0.025
French speaker 0.0254 0.849
Age -0.0021 0.667
Recent and prospective buyers 0.664 0.059
Renault customers 2.63 0.0
Variable Coefficient p-value
Use cost gasoline -0.0469 0.159
High government incentive 0.799 0.0
Med government incentive 0.0538 0.689
Low government incentive 0.0164 0.905
Proconvenience -0.142 0.0
Monthly battery lease (CHF) 2.17 0.0
Proleasing -0.193 0.082

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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