This study empirically analyzes the factors influencing the commuting time of households with multiple commuters in Chungnam Province. In particular, we examined how the commuting time varies between commuters depending on their wage gaps. A regression equation, in which the dependent variable was the difference in commuting time, was used. The key independent variable was the wage gap for households with two commuters. Further estimations were performed on samples restricted to dual-income couples with additional variables such as the wife’s household work burden, number of preschool children, and number of caregivers for children. Based on the results of the empirical analyses using the Chungnam Social Survey, the larger the wage gap between two commuters in a household, the longer the commuting time for high-wage commuters than for low-wage commuters. This contradicts the argument that a higher opportunity cost of commuting for higher wages should reduce the commuting time. In the analysis of dual-income couples, the wife’s commuting time was relatively shorter than that of the husband’s because of the burden of housework; however, the influence of childcare was not observed. As households with multiple commuters or dual-income couples become increasingly common, and the structure of cities changes from monocentric to multicentric, deciding where to live has become more complicated. Long-time and long-distance commuting can lead to wasteful commuting, and this needs to be considered as a social cost owing to the possibility of traffic congestion beyond the loss for the individual concerned. Therefore, the government’s urban policies, including housing and transportation policies, must be improved.

PURPOSES : In this study, the factors affecting commuting time according to city, county, and ward were empirically analyzed. METHODS : We estimated the average commuting time according to city, county, and ward by controlling for the characteristics of individual commuters, using a 2% sample of the Population and Housing Census of the National Statistical Office, and performed a twostage regression analysis using the average commuting time as the dependent variable. RESULTS : Among the regional attributes in the second stage, the share of commuters with different work and living areas was analyzed as a representative factor causing longer commuting times. The proportion of each mode of transportation in the total regional traffic volume and the population and household characteristics were also analyzed as affecting the average commuting time in the region. Particularly, when analyzing regions by dividing them into cities and counties within a metropolitan city and cities and counties within a province, or by dividing them into urban and rural areas, it can be observed that the factors affecting the average commuting time in the region are different, indicating that differentiated transportation policies are required according to the characteristics of the region. CONCLUSIONS : Commuting time entails increasing opportunity costs as wages increase. However, the expansion of the inter-regional transportation infrastructure acts as a factor in increasing job-residence separation and causes contradictory results by increasing the commuting time. If the characteristics of each region are different, and a function hierarchy as a city appears, travel between regions will become more common. Today, the widening gap between urban and rural areas in terms of employment and residential conditions can cause social waste due to increased commuting times. Ultimately, the extinction crisis of rural areas can be alleviated through policy by encouraging proximity to direct employment through the balanced development of jobs and settlement conditions between regions.

PURPOSES : Sleeping hours are an critical factor that influences the lives of office workers. As for sleep characteristics, it is crucial to spend a lot of time; however, it is also important to ensure satisfactory sleep. This study attempted to analyze the factors affecting sleep in terms of time and satisfaction. METHODS : Sleep duration, which is a quantitative component of sleep, was composed of a continuous variable, and multiple regression analysis was employed. For the qualitative component of sleep, sleep time satisfaction was used, satisfaction was composed of binary type, and binary logistic regression analysis was used. Transportation characteristics, including personal and work characteristics, and other factors were used as independent variables. RESULTS : As a result of the analysis, various influencing variables were derived for both sleep duration and satisfaction. The higher the satisfaction with commuting time and commuting fee, age group, health level, and leisure time, the higher the sleeping hours and satisfaction. However, the higher the income or the choice of long-distance transportation, the lower both sleeping hours and satisfaction. Moreover, if they choose a car, have a high academic background, or have children, sleeping hours decrease, but satisfaction is high. Males, singles, and office workers with fewer than five employees had longer sleep hours but lower satisfaction. CONCLUSIONS : Commuting time and work characteristics have varying effects on sleep activity. Therefore, an alternative solution that can enhance both sleeping hours and sleeping hour satisfaction is proposed through the results of this study.

PURPOSES : Disabled people have a low employment rate and poor working conditions, making it difficult for them to get a job compared to non-disabled people. In this study, we examined the characteristics of the commuting environment for the disabled because they experience many difficulties in the commuting environment owing to the physical influence of the disabled. METHODS : Disabled people are expected to have a higher meaning in commuting satisfaction than commuting time. Therefore, in this study, commuter satisfaction was used as a dependent variable, and an ordered logit model was used because it was composed of a five-point scale. RESULTS : As a result of the analysis, both disabled and non-disabled people were identified as influencing factors in salary, satisfaction with the environment around the residence, work characteristics, and public transportation. The main difference was that disabled people were affected by the household income, length of residence, and commuting area, whereas non-disabled people were affected by the personal income, home ownership, and area of residence. Therefore, it appeared that the household income, stability of residence, and short-distance commuting had a strong influence on the satisfaction of the commuting environment of the disabled. CONCLUSIONS : This study showed that if we understand the determinants of commuting environment satisfaction and various environmental factors, it is expected that effective policies to improve the employment rate of the disabled can be determined.