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 : This study empirically examines the determinants of traffic accidents by focusing on the transport culture index. METHODS : Two-stage least-squares estimation using an instrumental variable is used as the identification strategy. As the instrumental variable of the transport culture index, its past values, particularly before the outbreak of COVID-19 in 2018 are used. RESULTS : The empirical results, considering the potential endogeneity of the transport culture index, show that areas with higher values of the index are likely to have fewer traffic accident casualties. Local governments of regions with relatively frequent traffic accidents can run campaigns for residents to fasten their seatbelts, causing reverse causation. Ignoring this type of endogeneity underestimates the importance of the index as a key determinant of traffic accidents. CONCLUSIONS : Several traffic accidents occur in Korea, e.g., 203,130 accidents with 291,608 injuries and 5,392 deaths. As traffic accidents cause social costs, such as delays in traffic flow and damage to traffic facilities, public interventions are required to reduce them. However, the first step in public intervention is to accurately understand the relationship between the degree of damage in traffic accidents and the transport-related attributes of the areas where the accidents occurred. Although the transport culture index appears to be an appropriate indicator for predicting local traffic accidents, its limitations as a comprehensive index need to be addressed in the future.

PURPOSES : This study empirically analyzes the determinants of fatal accidents based on raw data on traffic accidents occurring in Chungnam in 2020. METHODS : Regression models based on theoretical arguments for fatal traffic accidents are estimated using a binomial logit model. RESULTS : The prediction model for fatal accidents is affected by the degree of urbanization of the region, month and day of the accident, type of accident, and type of law violation. In addition, speeding or illegal U-turns among law violations appear more likely to result in fatal accidents. The road surface conditions at the time of the accident do not show a significant difference in the probability of fatality among traffic accidents. However, the probability of a fatal accident is rather lower in case of a snowy road; this is plausible, as drivers tend to drive more carefully in bad weather conditions. CONCLUSIONS : Among traffic accidents, fatal accidents appear to be affected by the time and place of the accident, type of accident, and weather conditions at the time of the accident. These analysis results suggest policy implications for reducing fatal accidents and can be used as a basis for establishing related policies.