The existence of state dependence derived from panel data has played a very important role in studying employment and labor policies. This study is about state dependence of the transportation sector using retrospective panel survey data. The Ministry of Land, Infrastructure and Transport of Korea has conducted the survey to monitor changes in vehicle ownership and usage nationwide and to prepare measures when oil prices tend to rise sharply. From this data, we identify the existence of state dependence on passenger cars, public transportation, and nonmotorized modes. To do this, we estimate and analyze the dynamic random effects probit model that explains the selection of each transportation mode after controlling for the unobserved individual heterogeneity. Our results indicate that despite the rise of oil prices, behavior of habitual use (i.e., state dependence) of transportation modes is found in all three modes. The amount of state dependence of nonmotorized modes was the largest, followed by passenger cars and public transportation. From the estimated models, important policy implications can be drawn from the fact that the presence of state dependence and the importance of early habit formation are important not only in nonmotorized modes but also in public transportation. In other words, if policy makers want to encourage people to use public transportation in a new city, it suggests that a sufficient and convenient public transportation network should be built before people move to the city. Once cities are built without sufficient public transportation networks and people have become accustomed to using private cars, then it will be more difficult to change their transportation modes, requiring much more social efforts and costs.
Let us suppose that there is a city where 50% of passengers use cars and the other 50% use public transportation. In general, such percentages can be obtained mainly from cross-sectional household travel surveys. For many years, the proportions of travel modes, which are an important indicator for policy formulation and implementation, have been estimated and utilized from such cross-sectional survey data. In addition, by analyzing the various factors influencing the choice of travel modes, policies have been attempting to reduce the share of passenger car modes and to increase the share of environment-friendly modes such as public transportation or walking. However, we can derive a richer policy implication by analyzing panel data rather than cross-sectional data. In this paper we examine the decision-making and behavior of the transportation sector from a dynamic point of view using panel data.
A 50% share of public transportation has very different behavioral implications when it is interpreted from a dynamic point of view. Assume that there are two groups where one group consists of frequent mode changers and the other of less frequent changers. If all public transportation users in the first group switch to passenger cars this year from last year, and if all passenger car users switch to public transportation this year from last year, then the share of each mode of transportation will be 50%. On the other hand, for the second group where the mode changes are not frequent, each mode share is again 50% even when all passengers use the same transportation modes from last year to this year. If cross-sectional data are used, both the former and the latter provide the same information of a 50% share of passenger cars and a 50% share of public transportation. However, the interpretation obtained from using panel data can be significantly different. In the former case, the users of the transportation system have no state dependence and in the latter case they have complete state dependence. In the latter case, past experiences determine the present and future outcomes, and the use of the same modes of transportation tends to be fixed as a habit.
The findings of state dependence using panel data will play a very important role in establishing and implementing transportation policies. First of all, if there is a state dependence, a policy that increases or decreases the use of specific modes of transportation at the beginning can be stressed because the initial use of transportation modes determines the use of transportation modes in the future. In addition, policies related to regional, infrastructural, and socioeconomic impacts will need to be considered, taking into account the importance and magnitude of impact variables of individual characteristics. However, if the state dependence does not exist on the use of transportation modes, more flexible transportation policies would be possible in order to supply and operate transportation at the right time and right place, rather than initiating initial habits.
In this study, we use retrospective transportation panel data. Korea’s Ministry of Land, Infrastructure and Transport conducted a survey in the spring of 2012 to monitor changes in vehicle ownership and usage nationwide and to construct policy measures when oil prices had risen sharply since 2010. The collected data are used to identify the existence of state dependence that is customarily used for passenger cars, public transportation, and nonmotorized modes (mainly walking). And we want to analyze the factors affecting the selection of each mode after controlling for the unobserved heterogeneity of individuals. Using the data of the period when oil prices rose sharply, we are able to derive behavioral results and policy implications that are different from the existing studies based on the random effects dynamic probit model using single transportation modes. This paper is also different from previous studies in that we consider various transportation modes rather than a single mode and compare the magnitudes of state dependence among those modes in the years of rising oil prices.
Studies of state dependence of an individual or a firm have begun to resolve the question of whether persistence of state is due to the heterogeneous nature of individuals or firms or by past experience in the economic and social phenomena. Identifying a state dependence has been an important issue in social sciences. For example, studies in labor economics have examined and estimated the state dependence of employment barriers in the labor market, and using panel data they have analyzed the mechanisms of the persistence of nonemployment, low wages, and low labor force participation rates, especially for women [
In the transportation sector, certain studies examine the existence and influence of state dependence. Typical examples are state dependence of vehicle ownership, transportation use, and activities. In conclusion, these studies suggest that state dependence exists and should be considered in forecasting future travel demand. Studies on vehicle ownership include Bjørner et al. [
Research on transportation mode use tends to be done primarily for single modes. Chartterjee used panel data from a survey of bus users before and after the construction of the Bus Rapid Transit in the Crawley and Gatwick Airport area in Southern England in 2003 [
Travel modes are used every day for commuting, shopping, and business activities. State dependence related research was also conducted based on observations of daily mode of transportation and activities. That is, state dependence can be identified if individual activities or schedules are examined over time. Representative studies include Ramadurai et al. and Cirillo et al. [
Among the studies related to the effects of introducing new traffic systems or policies, studies also exist that have identified the existence of habits or inertia. Fujii et al. have studied whether there is a change in choice of modes when habitual drivers are given one-month free coupons for public transportation [
In this study, we use a three-year retrospective transportation panel data, data that was collected when oil prices rose sharply. The Korean Ministry of Land, Infrastructure and Transport conducted a survey in the spring of 2012 to monitor changes in vehicle ownership and usage nationwide and to prepare transportation measures when oil prices were rising sharply since 2010. In the Statistics Portal’s annual oil price information, OPEC’s oil prices per barrel had risen sharply, rising to 60.86 in 2009, 77.38 in 2010, 107.46 in 2011 and 109.45 in 2012 as shown in Figure
Average annual OPEC oil price (in the US dollars per barrel). Note: this figure is slightly modified from original data source [
Specifically, the Korean government surveyed 2,500 observations across the country. In order to prevent the sampling bias, a three-stage stratified sampling method was applied. First, regional allocations were investigated in line with the national census. Second, it was assigned to the sex ratio of the population. Finally, the samples were collected in proportion to the age group of the population. In other words, our sample is representative of the national census. Therefore, there is little concern that the sample will be biased. Although the Korean government collected 2,500 observations through the stratified sampling method, only 1,966 observations are used for this study, excluding observations with households (not persons) without vehicles. The reason for this is that it is more important to see the change of modes of households that have a car.
If the travel information for the same person and household is surveyed every year like the Puget Sound Transportation Panel data, it is possible to conduct state-dependent research considering various travel purposes and travel modes [
As regards economic factors, the income level was categorized into three levels of income, low, middle, and high, but each of them was adjusted for dummy variables for analysis. The number of cars owned, the employment status, and the employment type of the employed persons were classified. Dynamic information of home and work was surveyed. Along with the duration of home and work, changes were recorded and coded for our study year (t), a year preceding our study year (t-1), and two years preceding (t-2). An oil dummy variable is coded as 1 when the oil price is the highest and is regarded as zero when not. Table
Definition of used variables.
Factor | Variable name | values | Definition | Variables |
---|---|---|---|---|
Transport | Car | 0,1 | For (t-2),(t-1),t year, if (passenger car is used) 1, otherwise 0 | Dependent variable |
Lcar | 0,1 | Lagged variable of car variable | independent variable | |
Transit | 0,1 | For (t-2),(t-1),t year, if (transit is used) 1, otherwise 0 | Dependent variable | |
Ltransit | 0,1 | Lagged variable of transit variable | independent variable | |
Non-motor | 0,1 | For (t-2),(t-1),t year, if (non-motor vehicle is used) 1, otherwise 0 | Dependent variable | |
Lnon-motor | 0,1 | Lagged variable of non-motor vehicle variable | independent variable | |
Time | Minutes | Travel time | independent variable | |
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Individual | Female | 0,1 | If(gender = female) 1, otherwise 0 | independent variable |
Age | 1,2,3,4,5 | If(age < 30) 1, If(age >= 30 & age < 40) 2 | independent variable | |
If(age >= 40 & age < 50) 3, If(age >= 50 & age < 60) 4 | ||||
If(age > = 60) 5 | ||||
Age 20s | 0,1 | If(age < 30) 1, otherwise 0 | independent variable | |
Age 30s | 0,1 | If(age >= 30 & age < 40) 1, otherwise 0 | independent variable | |
Age 40s | 0,1 | If(age >= 40 & age < 50) 1, otherwise 0 | independent variable | |
Age 50s | 0,1 | If(age >= 50 & age < 60) 1, otherwise 0 | independent variable | |
Age 60+ | 0,1 | If(age > = 60) 1, otherwise 0 | independent variable | |
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Regional | Seoul_m | 0,1 | If(residence is Seoul metropolitan area) 1, otherwise 0 | independent variable |
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Economic | Self_emp | 0,1 | If(respondent is self-employed) 1, otherwise 0 | independent variable |
Vehicles | Vehicles | Number of owned vehicles | independent variable | |
Income | 1,2,3 | If(household monthly income < 2000$) 1 | independent variable | |
If(household monthly income >= 2000$ & household monthly income < 4000$) 2 | ||||
If(household monthly income >= 4000$) 3 | ||||
Low income | 0,1 | If(household monthly income < 2000$) 1, otherwise 0 | independent variable | |
Mid income | 0,1 | If(household monthly income >= 2000$ & household monthly income < 4000$) 1, otherwise 0 | independent variable | |
High income | 0,1 | If(household monthly income >= 4000$) 3, otherwise 0 | independent variable | |
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Location | Hchg | 0,1 | For (t-2),(t-1),t year, if (there was the change of housing location) 1, otherwise 0 | independent variable |
Jchg | 0,1 | For (t-2),(t-1),t year, if (there was the change of job location) 1, otherwise 0 | independent variable | |
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Oil dummy | Oil dummy | 0,1 | if (oil price is the highest) 1, otherwise 0 | independent variable |
Descriptive statistics of used variable.
Variable | Obs. | Mean | Std. Dev | Min | Max |
---|---|---|---|---|---|
Car | 5,898 | 0.5732 | 0.4946 | 0 | 1 |
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Transit | 5,898 | 0.3173 | 0.4655 | 0 | 1 |
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Walks | 5,898 | 0.1093 | 0.3121 | 0 | 1 |
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Lcar | 3,932 | 0.6119 | 0.4873 | 0 | 1 |
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Ltransit | 3,932 | 0.2868 | 0.4523 | 0 | 1 |
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Lwalks | 3,932 | 0.1012 | 0.3016 | 0 | 1 |
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Time | 5,898 | 40.257 | 31.3602 | 1 | 180 |
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Female | 5,898 | 0.4613 | 0.4985 | 0 | 1 |
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Age | 5,898 | 2.8626 | 1.2725 | 1 | 5 |
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Vehicles | 5,898 | 1.4120 | 0.6714 | 1 | 4 |
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Seoul_m | 5,898 | 0.4821 | 0.4997 | 0 | 1 |
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Income | 5,898 | 2.1931 | 0.7126 | 1 | 3 |
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Self_emp | 5,898 | 0.1642 | 0.3705 | 0 | 1 |
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Hchg | 5,898 | 0.0735 | 0.2611 | 0 | 1 |
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Jchg | 5,898 | 0.0885 | 0.2840 | 0 | 1 |
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Oil dummy | 5,898 | 0.3333 | 0.4714 | 0 | 1 |
In Table
Based on the previous literature reviews, we found that similar variables are commonly used in case studies in most transport sectors. However, some variables were used differently depending on the research purpose. First of all, in studies [
In Tables
Car use status over time from (t-2) to (t).
t-2 | t-1 | t | male | female | Age 20s | Age 30s | Age 40s | Age 50s | Age 60+ |
---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 504 | 337 | 62 | 209 | 280 | 203 | 87 |
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1 | 1 | 0 | 155 | 120 | 39 | 77 | 72 | 70 | 17 |
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1 | 0 | 1 | 7 | 4 | 4 | 0 | 5 | 2 | 0 |
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0 | 1 | 1 | 29 | 25 | 25 | 20 | 6 | 3 | 0 |
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0 | 0 | 1 | 35 | 34 | 26 | 20 | 16 | 5 | 2 |
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0 | 1 | 0 | 20 | 18 | 15 | 13 | 8 | 2 | 0 |
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1 | 0 | 0 | 39 | 32 | 12 | 16 | 22 | 16 | 5 |
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0 | 0 | 0 | 270 | 337 | 159 | 118 | 101 | 94 | 135 |
Transit use status over time from (t-2) to (t).
t-2 | t-1 | t | male | female | Age 20s | Age 30s | Age 40s | Age 50s | Age 60+ |
---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 169 | 232 | 111 | 87 | 70 | 63 | 70 |
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1 | 1 | 0 | 39 | 36 | 30 | 22 | 12 | 6 | 5 |
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1 | 0 | 1 | 17 | 19 | 15 | 12 | 7 | 2 | 0 |
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0 | 1 | 1 | 33 | 37 | 14 | 18 | 22 | 12 | 4 |
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0 | 0 | 1 | 129 | 108 | 38 | 61 | 65 | 58 | 15 |
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0 | 1 | 0 | 12 | 7 | 9 | 1 | 6 | 3 | 0 |
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1 | 0 | 0 | 26 | 25 | 25 | 16 | 5 | 3 | 2 |
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0 | 0 | 0 | 634 | 443 | 100 | 256 | 323 | 248 | 150 |
Nonmotor mode use status over time from (t-2) to (t).
t-2 | t-1 | t | male | female | Age 20s | Age 30s | Age 40s | Age 50s | Age 60+ |
---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 77 | 65 | 20 | 16 | 21 | 27 | 58 |
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1 | 1 | 0 | 11 | 12 | 6 | 4 | 10 | 1 | 2 |
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1 | 0 | 1 | 5 | 4 | 5 | 3 | 1 | 0 | 0 |
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0 | 1 | 1 | 8 | 11 | 7 | 3 | 2 | 4 | 3 |
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0 | 0 | 1 | 46 | 31 | 15 | 24 | 15 | 16 | 7 |
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0 | 1 | 0 | 2 | 7 | 4 | 3 | 1 | 1 | 0 |
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1 | 0 | 0 | 10 | 21 | 13 | 11 | 5 | 2 | 0 |
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0 | 0 | 0 | 900 | 756 | 272 | 409 | 455 | 344 | 176 |
Two characteristics can be found from the three-year mode transition tables. First, we can find that the proportion of people who use the same modes each year is quite large. Second, the shifted proportion from passenger cars to other modes in the period between (t-1) and (t) rather than between (t-2) and (t-1) year was quite large. In addition, the frequency of public transportation mode from (t-1) to (t) has increased significantly. On the other hand, changes from public transportation to other modes and from nonmotorized modes to other modes were not significant. The behavioral response to oil prices appeared at a time when oil prices rose considerably.
The frequency of transition is different by gender and age. For example, it is found that males use passenger cars more, females use public transportation more, and males use nonmotorized vehicles more. By age, 30s-40s year olds for passenger cars, 20s-30s year olds for public transportation, and 60s year olds upwards for nonmotorized vehicles were found to be highly frequent and persistent.
A state dependence is said to exist when one’s current choice depends on his or her past choices. The notion of state dependence has been widely used in economic analyses and in terms of regression analysis the state dependence occurs when a lagged dependent variable appears as one of the control variables we used. Without loss of generality, we can write a choice model as follows:
In (
There are many factors that cause individuals to continue to use private cars when choosing a mode of transportation. Observed or unobserved heterogeneities among individuals may have an impact on the use of private cars (or using public transport or nonmotorized modes). Therefore, in order to estimate (
The persistence of the transportation mode can be identified by the existence of state dependence between past and present behavior after controlling for observed and unobserved heterogeneities of individuals. In (
Tables
Probit results of passenger car users.
Random effects probit | Dynamic random effects probit | |||||||
---|---|---|---|---|---|---|---|---|
Variables | Coef. | S.E. | t-value | p-value | Coef. | S.E. | t-value | p-value |
Lcar | 2.187 | 0.058 | 37.700 | 0.000 | 1.711 | 0.126 | 13.590 | 0.000 |
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Female | -0.128 | 0.053 | -2.440 | 0.015 | -0.204 | 0.063 | -3.240 | 0.001 |
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Age 20s |
0.236 | 0.104 | 2.270 | 0.023 | 0.225 | 0.115 | 1.960 | 0.050 |
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Age 30s |
0.341 | 0.101 | 3.390 | 0.001 | 0.506 | 0.122 | 4.150 | 0.000 |
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Age 40s |
0.351 | 0.101 | 3.490 | 0.000 | 0.549 | 0.127 | 4.320 | 0.000 |
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Age 50s |
0.131 | 0.103 | 1.270 | 0.205 | 0.286 | 0.123 | 2.320 | 0.020 |
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Seoul_m | -0.252 | 0.054 | -4.690 | 0.000 | -0.362 | 0.069 | -5.260 | 0.000 |
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Time | -0.005 | 0.001 | -6.610 | 0.000 | -0.006 | 0.001 | -6.700 | 0.000 |
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Vehicles | 0.162 | 0.041 | 3.940 | 0.000 | 0.206 | 0.048 | 4.310 | 0.000 |
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Self_emp | 0.207 | 0.076 | 2.720 | 0.006 | 0.323 | 0.092 | 3.530 | 0.000 |
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Hchg | 0.059 | 0.100 | 0.590 | 0.554 | 0.059 | 0.105 | 0.560 | 0.573 |
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Jchg | -0.027 | 0.087 | -0.310 | 0.758 | -0.020 | 0.091 | -0.220 | 0.829 |
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Mid income |
0.089 | 0.072 | 1.240 | 0.217 | 0.161 | 0.083 | 1.950 | 0.051 |
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High income |
0.148 | 0.077 | 1.910 | 0.056 | 0.231 | 0.089 | 2.580 | 0.010 |
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Oil dummy | -0.559 | 0.053 | -10.470 | 0.000 | -0.580 | 0.057 | -10.240 | 0.000 |
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const | -1.100 | 0.116 | -9.460 | 0.000 | -0.915 | 0.135 | -6.800 | 0.000 |
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rho | 0.162 | 0.072 | 2.240 | 0.025 | ||||
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theta | 3.974 | 1.533 | 2.590 | 0.010 | ||||
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obs | 3932 | 3932 | ||||||
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Log likelihood (0) | -2701.45 | -2701.45 | ||||||
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Log likelihood (c) | -1483.60 | -2640.81 | ||||||
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Chi-sq(df=15) | 2435.71 | 923.95 |
Note:
Probit results of public transit users.
Random effects probit | Dynamic random effects probit | |||||||
---|---|---|---|---|---|---|---|---|
Variables | Coef. | S.E. | t-value | p-value | Coef. | S.E. | t-value | p-value |
Ltransit | 2.052 | 0.059 | 34.590 | 0.000 | 1.409 | 0.112 | 12.610 | 0.000 |
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Female | 0.195 | 0.054 | 3.650 | 0.000 | 0.295 | 0.068 | 4.370 | 0.000 |
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Age 20s |
-0.013 | 0.103 | -0.130 | 0.897 | 0.118 | 0.122 | 0.970 | 0.330 |
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Age 30s |
-0.144 | 0.101 | -1.430 | 0.152 | -0.252 | 0.119 | -2.120 | 0.034 |
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Age 40s |
-0.138 | 0.101 | -1.370 | 0.171 | -0.293 | 0.122 | -2.400 | 0.016 |
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Age 50s |
-0.004 | 0.104 | -0.040 | 0.969 | -0.130 | 0.124 | -1.050 | 0.294 |
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Seoul_m | 0.302 | 0.054 | 5.540 | 0.000 | 0.466 | 0.076 | 6.170 | 0.000 |
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Time | 0.009 | 0.001 | 11.330 | 0.000 | 0.012 | 0.001 | 10.820 | 0.000 |
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Vehicles | -0.220 | 0.044 | -4.980 | 0.000 | -0.281 | 0.054 | -5.240 | 0.000 |
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Self_emp | -0.162 | 0.081 | -2.010 | 0.044 | -0.305 | 0.099 | -3.080 | 0.002 |
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Hchg | -0.057 | 0.103 | -0.550 | 0.579 | -0.049 | 0.111 | -0.450 | 0.656 |
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Jchg | 0.035 | 0.088 | 0.400 | 0.690 | 0.020 | 0.095 | 0.210 | 0.836 |
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Mid income |
0.056 | 0.073 | 0.760 | 0.446 | 0.061 | 0.086 | 0.720 | 0.474 |
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High income |
0.063 | 0.079 | 0.790 | 0.428 | 0.049 | 0.092 | 0.530 | 0.599 |
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Oil dummy | 0.417 | 0.054 | 7.760 | 0.000 | 0.443 | 0.057 | 7.730 | 0.000 |
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Const | -1.627 | 0.119 | -13.670 | 0.000 | -1.575 | 0.140 | -11.270 | 0.000 |
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Rho | 0.248 | 0.066 | 3.730 | 0.000 | ||||
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Theta | 4.738 | 1.508 | 3.140 | 0.002 | ||||
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Obs | 3932 | 3932 | ||||||
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Log likelihood (0) | -2501.62 | -2819.68 | ||||||
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Log likelihood (c) | -1418.79 | -2374.45 | ||||||
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Chi-sq(df=15) | 2165.65 | 890.46 |
Note:
Probit results of nonmotorized vehicle users.
Random effects probit | Dynamic random effects probit | |||||||
---|---|---|---|---|---|---|---|---|
Variables | Coef. | S.E. | t-value | p-value | Coef. | S.E. | t-value | p-value |
Lnon-motor | 2.710 | 0.091 | 29.820 | 0.000 | 1.941 | 0.177 | 10.960 | 0.000 |
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Female | -0.073 | 0.077 | -0.950 | 0.343 | -0.068 | 0.098 | -0.690 | 0.490 |
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Age 20s |
-0.103 | 0.134 | -0.770 | 0.442 | -0.216 | 0.170 | -1.270 | 0.203 |
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Age 30s |
-0.157 | 0.131 | -1.200 | 0.230 | -0.388 | 0.178 | -2.180 | 0.029 |
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Age 40s |
-0.311 | 0.136 | -2.280 | 0.022 | -0.549 | 0.185 | -2.970 | 0.003 |
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Age 50s |
-0.032 | 0.135 | -0.230 | 0.815 | -0.206 | 0.180 | -1.140 | 0.252 |
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Seoul_m | -0.053 | 0.078 | -0.670 | 0.502 | -0.094 | 0.103 | -0.910 | 0.364 |
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Time | -0.010 | 0.002 | -6.460 | 0.000 | -0.016 | 0.003 | -5.580 | 0.000 |
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Vehicles | 0.049 | 0.057 | 0.850 | 0.393 | 0.029 | 0.075 | 0.380 | 0.702 |
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Self_emp | -0.176 | 0.115 | -1.520 | 0.128 | -0.283 | 0.155 | -1.830 | 0.067 |
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Hchg | -0.014 | 0.148 | -0.100 | 0.923 | -0.087 | 0.174 | -0.500 | 0.615 |
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Jchg | 0.049 | 0.123 | 0.400 | 0.690 | 0.026 | 0.143 | 0.180 | 0.855 |
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Mid income |
-0.188 | 0.096 | -1.970 | 0.049 | -0.369 | 0.133 | -2.780 | 0.005 |
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High income |
-0.339 | 0.109 | -3.120 | 0.002 | -0.602 | 0.163 | -3.700 | 0.000 |
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Oil dummy | 0.418 | 0.079 | 5.310 | 0.000 | 0.490 | 0.092 | 5.340 | 0.000 |
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const | -1.418 | 0.154 | -9.190 | 0.000 | -1.118 | 0.204 | -5.470 | 0.000 |
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rho | 0.391 | 0.119 | 3.280 | 0.001 | ||||
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theta | 2.245 | 0.699 | 3.210 | 0.001 | ||||
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obs | 3932 | 3932 | ||||||
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Log likelihood (0) | -1378.06 | -1384.24 | ||||||
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Log likelihood (c) | -635.93 | -1185.73 | ||||||
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Chi-sq(df=15) | 1484.27 | 397.01 |
Note:
In our case it is important to consider the initial condition because all of theta and rho are significantly different from 0. From the values shown in the bottom right of Tables
The random effects probit models and the dynamic random effects probit models with initial conditions show very different results. In the case of the passenger car model in Table
The results of the dynamic random effects probit models of Tables
As stated in the introduction, the effect of an increase in oil prices on the choice of transportation mode is analyzed. The coefficients on the oil dummy indicate that changes in oil prices affect the choice of modes of transportation. A negative value for passenger car models and a positive value for public transportation and nonmotorized models imply that people rely on cars less and rely on public transportation and nonmotorized modes more when oil prices are increasing.
We now discuss factors determining the modes of transportation in more detail.
Factors affecting the use of passenger cars are the lagged variable of passenger car use, gender, whether they live in the Seoul metropolitan area, the number of vehicles, travel time, self-employment status, income level, and oil price dummy variables. And these factors are statistically significant. On the other hand, the housing and job location change variables were not statistically significant.
Females are less likely to use passenger cars than males. The effect of age on the use of passenger cars is concave: the likelihood of using cars increases as people get older, but decreases after the 40s. It is found that residents of the Seoul metropolitan area rely less on the use of private cars. As the estimated value is -0.362, the probability of using a car decreases when people reside in the Seoul metropolitan area, which is a clear result considering the well-established public transportation infrastructure in this area. The longer the travel time, the lower the probability of using a passenger car. This is probably due to the influence of high fuel costs.
In addition, the estimated value of the number of vehicles has a positive value of 0.206, so that the more vehicles they have, the higher the probability of using a car. The self-employed variable is more likely to use a car than the employed. Also, the higher the income level, the more likely people use passenger cars. On the other hand, changes in work or home location cannot be said to affect the use of private cars because the estimated coefficients are not statistically significant.
Factors affecting the use of public transportation, which are statistically significant, are the lagged variable of transit modes, gender, residence in the Seoul metropolitan area, the number of vehicles, travel time, self-employed status, and oil dummy variables. However, the level of income, and the change in home or work location were not statistically significant.
Some of the results obtained for public transportation show a different pattern from those obtained for passenger cars. For example, women are more likely to use public transportation than men. Middle aged people use public transportation less and young and older people rely on public transportation more. These results are consistent with our expectation.
People living in the Seoul metropolitan area have a high probability of using public transportation. This is again a clear result because of the well-established public transportation infrastructure of the Seoul metropolitan area compared to the provincial regions. The longer the travel time, the higher the probability of using public transportation. This is the opposite pattern to the use of passenger cars. The number of vehicles and the self-employed variables tend to decrease the probability of using public transportation, which is also the opposite to the case of passenger cars. Finally, changes in income level or location of work or home cannot be said to affect the sustainable use of public transportation because the estimated coefficients are not statistically significant.
The number of statistically significant variables on the use of nonmotorized modes is less than those for passenger cars and public transportation. The lagged variable of nonmotorized modes, age, travel time, self-employed status, and income level and oil dummy variables are statistically significant. The other variables are found to be statistically insignificant.
Unlike the previous cases, gender does not affect the use of nonmotorized modes. People aged 30s and 40s have a lower probability of using nonmotorized modes than the rest of the age groups. Finally, the probability of using nonmotorized modes decreases as travel time and income increase.
The previous analysis has examined the existence of state dependence and the factors affecting the choice of transportation modes. However, equally important is the prediction of state dependence by modes. It will be also interesting to compare the magnitudes of state dependence with similar characteristics. Here, we present the probability of using a car in year (t) if everyone used a car in year (t-1), not the probability that a person using a car in year (t-1) will use a car in year (t). That is, we present an average probability of using a car conditional on that everyone used a car in the previous year. The same is true for public transportation and nonmotorized modes.
In the literature review, Bjørner et al. have identified the magnitude of state dependence of vehicle ownership [
Estimation of state dependence of passenger car users.
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Variables | The probability that a person using a car (t-1) year will use the car (t) year | The probability that a person who did not use a car (t-1) year will use the car (t) year | Difference | Estimates of State Dependence |
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Male | 0.913 | 0.378 | 0.535 | 1.674 |
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Female | 0.911 | 0.342 | 0.569 | 1.750 |
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Seoul metro | 0.898 | 0.360 | 0.538 | 1.626 |
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Non Seoul metro | 0.928 | 0.359 | 0.569 | 1.827 |
Estimation of state dependence of transit users.
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Variables | The probability that a person using a transit (t-1) year will use the transit (t) year | The probability that a person who did not use a transit (t-1) year will use the transit (t) year | Difference | Estimates of State Dependence |
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Male | 0.654 | 0.169 | 0.485 | 1.355 |
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Female | 0.759 | 0.225 | 0.534 | 1.460 |
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Seoul metro | 0.751 | 0.262 | 0.489 | 1.313 |
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Non Seoul metro | 0.683 | 0.138 | 0.545 | 1.563 |
Estimation of state dependence of nonmotorized users.
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Variables | The probability that a non-motor using a car (t-1) year will use the non-motor (t) year | The probability that a person who did not use a non-motor (t-1) year will use the non-motor (t) year | Difference |
Estimates of State Dependence |
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Male | 0.462 | 0.015 | 0.447 | 2.083 |
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Female | 0.367 | 0.015 | 0.352 | 1.836 |
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Seoul metro | 0.383 | 0.014 | 0.369 | 1.911 |
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Non Seoul metro | 0.414 | 0.015 | 0.399 | 1.956 |
Comparing the state dependence of males and females, females are more dependent on cars and public transport than males, but they are less dependent on nonmotorized modes. The marginal effects show the same pattern. On the other hand, the residents of non-Seoul metropolitan area have a greater state dependence than the residents of Seoul metropolitan area in all modes of transportation. This may be true because it is difficult for the non-Metropolitan area residents to switch to other modes of transportation due to the lack of various modes of transportation.
Finally, we note that people have the highest state dependence (coefficient on lagged variable) on nonmotorized modes, but the probability of using nonmotorized modes is as low as 10% of total transportation modes. Nevertheless, Table
In this section we extend our analysis to a model which includes two-period lag dependent variables. An example can be found in a recent paper by Xiong, Yang, and Zhang [
The two-step method can be described as follows. First, we estimate the probit equation for period 1; i.e., t=1.
The estimation results are presented in Table
Estimation results of probit equations with two-period lag dependent variables.
Probit Estimates | Probit Estimates | |||||
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Without Controlling Initial Conditions | With Controlling Initial Conditions | |||||
Car | Transit | Non-motor | Car | Transit | Non-motor | |
Lcar | 1.574 |
1.568 |
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(0.116) | (0.116) | |||||
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LLcar | 0.296 |
-0.460 | ||||
(0.116) | (0.674) | |||||
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Ltransit | 1.402 |
1.374 |
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(0.113) | (0.114) | |||||
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LLtransit | 0.421 |
-0.461 | ||||
(0.115) | (0.365) | |||||
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Lnon-motor | 2.004 |
1.996 |
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(0.190) | (0.190) | |||||
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LLnon-motor | 0.720 |
0.309 | ||||
(0.188) | (0.555) | |||||
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Female | -0.118 |
0.204 |
-0.155 | -0.196 |
0.291 |
-0.150 |
(0.069) | (0.070) | (0.099) | (0.097) | (0.078) | (0.099) | |
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Age 20s |
0.171 | -0.018 | -0.054 | 0.098 | 0.141 | -0.085 |
(0.140) | (0.137) | (0.181) | (0.153) | (0.150) | (0.184) | |
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Age 30s |
0.188 | -0.052 | 0.046 | 0.330 |
-0.113 | -0.010 |
(0.132) | (0.131) | (0.172) | (0.181) | (0.133) | (0.186) | |
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Age 40s |
0.316 |
-0.089 | -0.241 | 0.524 |
-0.234 | -0.299 |
(0.132) | (0.132) | (0.180) | (0.225) | (0.144) | (0.194) | |
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Age 50s |
0.015 | 0.099 | 0.087 | 0.191 | -0.014 | 0.034 |
(0.135) | (0.135) | (0.180) | (0.205) | (0.142) | (0.191) | |
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Seoul_m | -0.237 |
0.285 |
-0.073 | -0.350 |
0.445 |
-0.080 |
(0.070) | (0.071) | (0.101) | (0.122) | (0.095) | (0.101) | |
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Time | -0.005 |
0.009 |
-0.011 |
-0.006 |
0.011 |
-0.012 |
(0.001) | (0.001) | (0.002) | (0.001) | (0.001) | (0.002) | |
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Vehicles | 0.188 |
-0.257 |
0.071 | 0.225 |
-0.288 |
0.062 |
(0.053) | (0.058) | (0.073) | (0.062) | (0.059) | (0.074) | |
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Self_emp | 0.190 |
-0.110 | -0.176 | 0.315 |
-0.236 |
-0.193 |
(0.096) | (0.102) | (0.146) | (0.146) | (0.114) | (0.148) | |
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Hchg | 0.033 | -0.069 | 0.008 | 0.035 | -0.086 | 0.006 |
(0.133) | (0.137) | (0.180) | (0.133) | (0.137) | (0.181) | |
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Jchg | -0.249 |
0.122 | 0.318 |
-0.259 |
0.125 | 0.316 |
(0.111) | (0.112) | (0.144) | (0.111) | (0.112) | (0.144) | |
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Midincome | 0.031 | 0.125 | -0.154 | 0.098 | 0.118 | -0.190 |
(0.095) | (0.096) | (0.124) | (0.112) | (0.096) | (0.132) | |
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Highincome | 0.172 |
0.061 | -0.348 |
0.256 |
0.039 | -0.388 |
(0.102) | (0.104) | (0.141) | (0.126) | (0.104) | (0.149) | |
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Cons | -1.342 |
-1.212 |
-1.108 |
-1.007 |
-1.003 |
-0.943 |
(0.153) | (0.150) | (0.197) | (0.331) | (0.171) | (0.287) | |
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- | - | - | 0.461 | 0.551 |
0.230 |
(0.405) | (0.217) | (0.292) | ||||
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Log-likelihood | -896.04 | -853.18 | -389.85 | -895.39 | -849.92 | -389.54 |
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Obs | 1,966 | 1,966 | 1,966 | 1,966 | 1,966 | 1,966 |
Note: the numbers in the parentheses are standard errors.
In previous studies using cross-sectional data, the most important factors influencing the use of transportation were generally time and cost. However, in the present study using panel data, the state dependence on mode use is found to be a very important factor. In fact, all of the lagged dependent variables are statistically significant at the 99% level. The existence of state dependence found in this study represents familiarity habits because we have taken into account the unobserved individual heterogeneity in estimation.
The main purpose of this study was to identify the existence of state dependence by considering the problem of initial conditions in the choice of transportation modes. In other words, the analysis of the decision of the individual to select the transportation mode was divided into two parts: the random effects probit model which does not consider the initial condition problem and the dynamic model which takes into account the initial condition problem. It turns out that the initial condition problem is important so that the coefficient of the lagged dependent variable in a simply random effects model is overestimated. Therefore, the state dependence obtained without considering the initial condition exaggerates the true dependence of travel modes.
The magnitude of state dependence of the transportation mode appears in the order of nonmotorized, passenger cars, and public transportations, and these results are independent of gender or metropolitan area. Interestingly, however, an increase in the probability of using the same mode of transportation due to state dependence is the largest for cars and least for nonmotorized vehicles. The reason is that the marginal effect of state dependence is estimated to be small because the absolute sample proportion of using nonmotorized modes is low.
Some factors influencing the use of public transportation and passenger cars work in the opposition direction. At the time of rises in oil price, people used all three methods habitually in the same pattern. However, the effect of the highest oil prices was different depending on travel modes. Negative value for passenger cars, positive values for public transportation, and nonmotorized vehicles show a reduction in the persistence of passenger cars and an increase in the persistence of public transportation and nonmotorized modes.
Based on the results of this study, some policy implications can be derived. Without a state dependence, people’s habits will not have a significant impact on the choice of transportation modes, so the policy intending to change people’s transportation modes will be more likely to succeed. For example, when policies to increase the use of walking or bicycling are implemented, those policies will be more successful if the state dependence of a travel mode is weak. However, our study shows that past transportation mode has a great impact on the current transportation decision. This implies a considerable state dependence in the choice of transportation mode, so the policies relying on campaigning on walking or cycling are unlikely to succeed.
A high state dependence means that a person who chooses a mode of transportation before is more likely to choose the same mode of transportation thereafter. Thus, if one tries to change a person’s transportation mode, it will take more effort and costs when the person has a strong state dependence than when the person has a weak state dependence. In the former case, policy makers should pay more attention to determining the initial modes of transportation. For example, if policy makers want to encourage people to use public transportation in a new city, they need to build a sufficient and convenient public transportation network before people move to the city. Once cities are built without sufficient public transportation networks and with people having become accustomed to using private cars, then it will be more difficult to change their transportation modes and also much more social efforts and costs will be required.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this article.
This research was funded by The Ministry of Land, Infrastructure and Transport (MOLIT) of Korea. The authors thank MOLIT for providing the opportunity to conduct this research.