Reductions in CO 2 emissions have a significant effect on the transportation sector, and there is increasing interest in developing green cars such as electric cars. To prepare for the advent of the electric car era, it will be necessary to predict the increase in electricity demand owing to the spread of electric cars and determine the policy approaches. Therefore, the analysis was performed to promote the use of electric car that helps reduce CO 2 emissions. This study establishes a mode choice model using the stated preference method. To improve the predictive power of the model, some revealed preference data were also examined to consider the characteristics of the commuters and the extent of current electric car technology to determine and verify the parameters of the mode choice models. This was used to estimate changes in CO 2 emissions owing to the introduction of electric cars and present effective policy approaches to reduce CO 2 emissions.
Significant efforts are being made around the world to deal with the gradually worsening problem of climate change, and policies are being promoted at a national level such as specifying target countries for obligated greenhouse gas reductions. However, the greenhouse gas emission trends measured by the Ministry of the Environment from 2010 to 2015.
increased every year with the exception of 2014, and even in 2014, the range of reduction was very small [
In this study, the stated preference survey (hereon referred to as SP) method was used to predict the usage ratio of a new means of transportation, the electric car. An individual behavioral model was created based on the studied data with the goal of understanding factors that effect transportation mode choice and changes in usage ratios for each form of transportation, and calculating the change in CO2 emissions owing to the introduction of electric cars. In addition, revealed preference (hereon referred to as RP) data were also collected and used to reflect individual characteristics in the construction of the model. Based on the collected data, the LIMDEP program, which is widely used for mode choice models, was employed to calculate the importance of each variable, i.e., the parameter values.
More studies have been conducted on potential factors affecting choice behavior since the 2000s [
Electric cars have not created a market scale sufficient for the study of consumer preferences, and hence, it is necessary to study their choices in virtual scenarios to recreate the process of an individual selecting a form of transportation, and to select important characteristics and set rational standards for each characteristic [
In estimating a behavioral model, RP data are data based on actual scenarios and behavior, whereas SP data are different in that they demonstrate preference decisions through virtual scenarios. As such, the two types of data can be observed to have mutually complementary qualities on a practical and statistical level. These are compared in
Hwang et al. [
Lee et al. [
Kim et al. [
Type | RP | SP |
---|---|---|
Information | The results of actual behavior and choices The same as actual behavior Data that only have the results of actual choices | Expression of intentions in virtual scenarios Possibly will not match selection behavior Selection/Ranking/Grading data |
Alternatives | Alternatives that actually exist | Includes alternatives that do not exist |
Properties | Limited range of property values Correlation between properties exists | Expandable range of property values Controllable correlation between properties |
Kim et al. [
As the electric car choice percentage of the respondents varies according to their circumstances, this study constructs a mode choice models that combines RP and SP data while considering the social and economic characteristics of the individuals and the commute times and costs of various forms of transportation. Accordingly, this study examines the factors affecting mode choice and the usage rates for each form of transportation, and it calculates changes in CO2 emissions owing to the introduction of electric cars.
The questionnaire presented in this study was created using an experiment design method for ensuring orthogonality between the properties of the virtual alternatives proposed to the survey respondents and avoiding multicollinearity, which is a problem in RP data.
When an experiment is set up with three properties at three possible levels and five properties at two possible levels, related to a total of 8 factors as in this study, there will be 576 experimental combinations. In this kind of multi-factorial survey, detailed information about interactions between factors can be obtained aside from the major effects. However, the experiment is performed more than once to combine the levels of all factors, and hence, the number of experiment rounds becomes greater in proportion to the number of factors. An increase in the number of experiment rounds leads to time and cost problems, and problems with making the combinations of factors and levels uniform when selecting alternatives. Therefore, this study assumed that there is no interaction between factors and compressed the 576 combinations into 58 types of transportation conditions. The questionnaires were created so that five of the 58 transportation conditions were presented to a respondent using a random function.
In the SP survey, the commute time, commute cost, and parking cost levels for each form of transportation were set as shown in
Survey Items | Content |
---|---|
Individual and household characteristics | Age, sex, student status, number of household members, possession of driver’s license |
Conditional mode choice intention survey | Choice of a form of transportation mode under 8 conditions, assuming that a passenger car, public transportation, and electric car can all be selected |
RP | The transportation mode selected for actual commute and time spent |
SP | The transportation mode selected in a survey of five questions given to a person based on an orthogonal matrix table, which classifies transportation as a passenger car, public transportation, or electric car and places its related properties on 2 levels or 3 levels |
Form | Factor for Each Form of Transportation | Unit | Level | ||
---|---|---|---|---|---|
1 | 2 | 3 | |||
Passenger Car | Commute Time | Minute | 15 | 30 | 60 |
Commute Cost | Won | 4000 | 8000 | - | |
Parking Cost | Won | 0 | 2000 | - | |
Public Transportation | Commute Time | Minute | 50 | 120 | - |
Commute Cost | Won | 1000 | 2500 | - | |
Electric Car | Commute Time | Minute | 30 | 60 | 90 |
Commute Cost | Won | 3000 | 6000 | - | |
Parking Cost | Won | 0 | 1000 | 2000 |
at 1/2 or 2/3 of the speed of a passenger car to reflect the limited driving speed of the former. The commute cost reflected the cost of purchasing the car and the cost of filling up on fuel. In the case of a passenger car, this corresponded to gas costs (gas cost and filling time cost), whereas in the case of an electric car, this corresponded to charging costs (electricity cost and charging time cost). The cost of filling up on fuel for an electric car was far lower than that of a passenger car, but the cost of purchasing an electric car was large compared with fuel costs in an absolute sense, even when considering subsidies, and hence, this was reflected in the settings.
In the case of parking cost, it was decided that an economic incentive must be provided for the environmental friendliness of the electric car and hence, the cost was set at 1/2 that of the passenger car. For public transportation, the levels were set to reflect the current fees of normal buses and wide-area buses.
In this survey, the personal characteristics such as age, sex, occupation (student or not), number of household members, and possession of a driver’s license were the basic items of the survey.
The survey analyzed the current transportation mode choice percentages according to the commute distance to understand the current situation. The analysis results are shown in
In this survey, eight transportation intention survey items were presented. As shown in
Age | Sex | Occupation | |||||
---|---|---|---|---|---|---|---|
20 - 30 years | 85% | Man | 57% | Student | 80% | ||
31 - 40 years | 6% | Woman | 43% | Worker | 17% | ||
40+ years | 9% | Other | 3% | ||||
Number of Household Members | Driver’s License Possession | ||||||
Alone | 21% | Has license | 74% | ||||
Two or more | 79% | Does not have license | 26% | ||||
Survey Item | Content |
---|---|
Case 1 | When the parking cost of an electric car is 50% that of a passenger car |
Case 2 | When the parking cost of an electric car is free |
Case 3 | When the maximum driving distance of an electric car is 50 km per charge |
Case 4 | When the maximum driving distance of an electric car is 100 km per charge |
Case 5 | When the maximum driving distance of an electric car is 200 km per charge |
Case 6 | When the charging cost of an electric car per km is 1/2 that of a passenger car |
Case 7 | When the charging cost of an electric car per km is 1/5 that of a passenger car |
Case 8 | When the charging cost of an electric car per km is 1/10 that of a passenger car |
with that of a passenger car, the selection percentage of an electric car increased from 36% to 76% to 84%, whereas those of a passenger car and public transportation decreased.
In this survey, the current commute characteristics of the respondent were divided into commute distance, commute time, and commute purpose. The results of analyzing the commute characteristics are shown in
Commute Time | Commute Distance | Commute Purpose | |||
---|---|---|---|---|---|
0 - 30 min | 23% | 0 - 20 km | 38% | To work | 22% |
30 - 60 min | 20% | 20 - 40 km | 31% | To school | 64% |
60 - 90 min | 15% | 40 - 60 km | 19% | Other | 14% |
90 - 120 min | 30% | >60 km | 12% | ||
>120 min | 12% |
respondents, a majority had a commute of 0 - 20 km or 20 - 40 km, accounting for 38% and 31% of respondents, respectively. The distribution of respondents according to commute time was relatively even, and 30% had a commute time of 90 - 120 min. With regard to the commute purpose, the highest percentage of respondents (64%) was commuting to school.
The transportation mode choice intention cases include parking cost cases, driving distance limitation cases, and charging cost cases. The cases were selected to best reflect the current status and technology of electric cars, and the selection percentages were analyzed according to commute characteristics. For the charging costs, the case with the greatest difference between a passenger car and an electric car and the case with the smallest difference between them were both analyzed.
In Case 1 (
With regard to commute distances, the largest percentages of respondents commuting 0 - 20 km and 20 - 40 km preferred public transportation, at 45% and 42%, respectively. As the commute distance became longer, fewer respondents selected public transportation, and the percentage of respondents selecting a passenger car or electric car increased. This seems to be related to an increased appreciation of commute convenience as the commute distance becomes longer.
With regard to commute times, the largest percentage (44%) of respondents commuting for less than one hour selected a passenger car, but as the commute times increased, the percentages selecting public transportation and an electric car gradually increased, whereas the percentage selecting a passenger car gradually decreased. It seems that this was because the commuters felt the burden of parking costs as the commute time increased.
In Case 4 (
when the purpose was going to school or another place, the selection percentages between the three forms of transportation were not significantly different. This seems to be because there is insufficient awareness of charging limitations and hence, responses were provided according to the commute purpose rather than the characteristics of the form of transportation or the given conditions.
Considering Case 4 by commute distance, there was no significant difference between the selection percentages of the three forms of transportation according to commute distance. This is believed to be because only 3% of respondents commuted 100 km or more.
Considering Case 4 by commute time, the largest percentage of respondents (44%) who commuted for one hour or less selected a passenger car. The largest percentage of respondents (54%) who commuted for one to two hours selected an electric car. The largest percentage of respondents (48%) who commuted for more than two hours selected public transportation.
In Case 6 (
for commuters who go to school is often limited to public transportation in reality.
Considering Case 6 by commute distance, there is almost no difference in the selected form of transportation of the respondents according to the commute distance. It is believed that this is because a difference in charging cost (fuel cost) has a small effect on inducing existing public transportation users to change their form of transportation, and passenger car users tend to emphasize convenience over sensitivity to charging cost.
Considering Case 6 by commute time, the respondents who selected an electric car showed no changes in their selected form of transportation according to commute time, but as the commute time increased, more respondents switched from a passenger car to public transportation. It is believed that this is because, as the commute time increased, the respondents an electric car.
In Case 8 (
Considering Case 8 by commute distance, a passenger car was not selected by any of the respondents except 3% of respondents commuting 20 - 40 km and 3% of respondents commuting 40 km or more. When respondents had a relatively short commute of 0 - 20 km, 24% selected public transportation, and 76% selected an electric car, but as the commute distance increased, more respondents preferred an electric car.
Considering Case 8 by commute time, a passenger car was not selected by any of the respondents except 3% of respondents commuting for one to two hours and 4% of respondents commuting for more than two hours. When the commute time was less than one hour, 11% of respondents selected public transportation, and 89% selected an electric car, and as the commute time increased, more respondents preferred public transportation.
It is believed that the overall analysis results of Case 8 were affected by the limitations in the size and characteristics of the survey group. However, it seems that the combination of commuting convenience and economic benefits provided by an electric car influenced the respondents, and it is believed that these conditions are the most appreciated by commuters.
This study used survey data related to transportation mode choice behavior regarding the current most frequently used transportation (RP) and the transportation mode choice behavior after by the introduction of an electric car (SP).
In the RP data, 85% of the 100 respondents were public transportation users, and this percentage was far greater than the percentage of passenger car users; however, this was because 80% of the respondents were students. In the SP data, each person was made to respond to five transportation mode choice questions, and hence, the total sample size was 500, and of these, the percentage of those selecting public transportation was the highest at 42.4%. The number of respondents selecting a passenger car and electric car were almost the same, but those selecting an electric car (29.60%) were slightly more than those selecting a passenger car (28.00%).
To create a model based on the survey data, the SP data, which simply add the new electric car form of transportation to a virtual situation, were not used by themselves. Instead, it was necessary to combine them with RP data, which reflect actual behavior slightly better.
Selection behaviors were described by an expected utility maximization theory using a probability utility function. In the probability utility function U, individual characteristics and service variables are related. Pij, Probability of an individual i selecting a mode j, is calculated by the following equation.
P i j = Prob ( U i j > U i k ) k = 1 , 2 , … , j ( k ≠ j ) (1)
Prob (Uij > Uik)―the probability of Uij > Uik; Uij―utility function of mode n for individual i; Pij―probability of individual i selecting mode j;
By setting several variables in the utility function, several kinds of models for the cumulative distribution function of Uij are determined. In this study, analysis was performed via the Log it model, and the equation of this model is as follows.
P i j = exp ( U i j ) ∑ i = 1 I exp ( U i j ) (2)
Transportation Form | RP Data | SP Data | ||
---|---|---|---|---|
Passenger Car | 15 | 15% | 140 | 28.00% |
Public Transportation | 85 | 85% | 212 | 42.40% |
Electric Car | - | - | 148 | 29.60% |
Total | 100 | 100% | 500 | 100% |
U i j = ∑ k a k X i j k (3)
Xijk―the value of the kth descriptive factor for mode j; I―total number of available transportation modes in the choice set for individual i.
Individual characteristic and mode choice characteristics variables were set based on the basic analysis results from the data examined in this study.
The model estimation results are shown in
Only significant variables were selected based on the model described in the previous section, and a newly estimated model, shown in
The basic units shown in
Variable Type | Variable Value and Unit | |
---|---|---|
Individual Characteristic Variables | Age | Years |
Sex | Man = 0, Woman = 1 | |
Occupation | Student = 0, Other = 1 | |
Number of Household Members | 1 or less = 0, 2 or more = 1 | |
Driver’s License Possession | Yes = 0, No = 1 | |
Mode Choice Characteristics Variables | Commute Time | Minutes |
Commute Cost | Won | |
Parking Cost | Won |
Variable | Estimated Value | t-value | P-value | |||
---|---|---|---|---|---|---|
Alternative Special Constant | Public Transportation | −0.1119 | −0.103 | 0.9179 | ||
Electric Car | −0.7598 | −0.704 | 0.4815 | |||
Individual Characteristic Variables | Age | Public Transportation | 0.0514 | 1.556 | ** | 0.1197 |
Electric Car | 0.0262 | 0.807 | 0.4195 | |||
Sex | Public Transportation | −0.7553 | −2.349 | *** | 0.0188 | |
Electric Car | −0.0521 | −0.161 | 0.8723 | |||
Occupation | Public Transportation | −0.6295 | −1.122 | * | 0.2621 | |
Electric Car | 0.1995 | 0.356 | 0.722 | |||
Driver’s License Possession | Public Transportation | 2.0014 | 5.159 | *** | 0 | |
Electric Car | 0.7966 | 2.035 | *** | 0.0418 | ||
Number of Household Members | Public Transportation | 0.1708 | 0.475 | 0.6351 | ||
Electric Car | 0.1894 | 0.486 | 0.6267 | |||
Alternative Characteristic Variables | Commute Time | Passenger Car | −0.0591 | −6.659 | *** | 0 |
Public Transportation | −0.0521 | −11.957 | *** | 0 | ||
Electric Car | −0.0596 | −8.585 | *** | 0 | ||
Commute Cost | Passenger Car | −0.0005 | −7.365 | *** | 0 | |
Public Transportation | −0.0010 | −5.427 | *** | 0 | ||
Electric Car | −0.0005 | −5.589 | *** | 0 | ||
Parking Cost | All Forms | −0.0003 | −3.084 | *** | 0.002 |
Note: ***: P-value ≤ 0.05, **: P-value ≤ 0.15, *: P-value ≤ 0.30.
was used for public transportation. No specific basic unit for an electric car has been proposed yet, and hence, it was decided that it would be appropriate to use the basic unit of the KTX high-speed rail, which uses only electricity, from among the basic units shown in
The standard values shown in
Variable | Estimated Value | t-value | P-value | ||
---|---|---|---|---|---|
Driver’s License Possession | Public Transportation | 1.5677 | 5.198 | *** | 0 |
Electric Car | 0.6125 | 4.524 | ** | 0.0862 | |
Commute Time | Passenger Car | −0.0615 | −7.638 | *** | 0 |
Public Transportation | −0.0468 | −12.813 | *** | 0 | |
Electric Car | −0.0607 | −9.843 | *** | 0 | |
Commute Cost | Passenger Car | −0.0005 | −9.897 | *** | 0 |
Public Transportation | −0.0008 | −5.367 | *** | 0 | |
Electric Car | −0.0005 | −7.518 | *** | 0 | |
Parking Cost | All Forms | −0.0004 | −3.415 | *** | 0.0006 |
Note: ***: P-value ≤ 0.05, **: P-value ≤ 0.15.
Form of Transportation | g·CO2/person·km |
---|---|
Passenger Car | 168.2 |
Bus | 55.7 |
KTX | 26.9 |
Saemaeul Train | 66.4 |
Mugunghwa Train | 42.3 |
Seoul Area Subway | 26.5 |
Subway | 25.9 |
Source: info.korail.com.
Type | Time (Minutes) | Cost (Won) | Parking Cost (Won) | Driver’s License Possession |
---|---|---|---|---|
Passenger Car | 30 | 8000 | 2000 | Yes |
Public Transportation | 90 | 2500 | 0 | Yes |
Electric Car | 60 | 6000 | 1000 | Yes |
transportation usage rates and CO2 emissions according to the changes in commute time, commute cost, and parking cost. In order to apply the basic units proposed by the Korea Railroad Corporation, analysis was performed based on the level of travel presented in the Seoul Area Household Travel Diary Survey, which stated that people in the Seoul area commuted 335,300,000 km in 2006. The results are shown below.
cost, when the commute cost of an electric car was reduced by 2000 Won, emissions were reduced by 2286 tons, and when the commute cost was reduced by 4000 Won, emissions were reduced by 4919 tons. In addition, when the parking cost was reduced by 1000 Won, emissions were reduced by 709 tons, and when the parking cost was increased by 1000 Won, emissions increased by 576 tons.
The present study performed an SP survey regarding the introduction of an electric car and analyzed it to construct a model and predict the usage ratios for each form of transportation.
To reduce CO2 emissions with the use of electric cars, it is necessary to develop technology that can reduce the commute times and introduce related facilities. In current electric cars, the maximum driving distance with a single charge is much shorter than that of a passenger car, and electric cars are not well regarded because of long charging times and inadequate charging facilities. In addition, while the actual fuel costs are much cheaper than those of a passenger car, the burden of purchase cost has a significant limiting effect on the use of electric cars. As such, in order to expand the supply of electric cars, the overall cost such as the cost of buying electric car and oil price should be considered. And based on economic analysis, appropriate government subsidies for electric cars must be arranged.
In this study, the survey was performed on a limited population where 80% of the respondents were students and 85% were in their twenties. Therefore, the population and its properties were biased, and the survey may have arrived at results that are not significant to users other than students. Therefore, in the future, it will be necessary to select a larger and more diverse population to perform research to calculate rates of change in transportation mode choice according to commute cost, time, distance, etc., and also calculate the extent of changes in CO2 emissions in a more appropriate way in terms of the environment and policy.
This study was supported by “2018 ITS Performance Evaluation Project” of the Korea Institute of Construction Technology (Now the name is the Korea Institute of Civil Engineering and Building Technology).
Ahn, S.Y. and Lee, S.H. (2018) Predicting Changes in Transportation Usage and Reductions in CO2 Emissions Due to Electric Cars. Engineering, 10, 432-447. https://doi.org/10.4236/eng.2018.107030