Disaster management teams could learn much from the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. The virus outbreak provoked public fear, which resulted in a mass reduction in transit use in the Seoul Metropolitan Area (SMA). Its effect differed among socioeconomic groups and geographical areas. A typical way to analyze this is to associate individual reactions to the disaster with socioeconomic characteristics. However, a more structured approach, which considers behavioral characteristics resulting from the societal position of an individual, would identify the basic reason for such associations. The study hypothesized that the degree of fixity that individuals have in their daily life may elucidate these associations. When fear is prevalent, people having the flexibility to change their lifestyle will make more changes in daily activities and travels. The study examined the influence of public fear of a pandemic disease on travel behavior and the effect of life fixity on individual response to the fear. To this end, smart card data of transit use and changes in travel behavior during the MERS period were examined. The study found that fear was powerful and influenced travel behavior differently depending on life fixity levels and regional characteristics.

Recently, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns or policy decision support due to missing information on alighting stops of users. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. In order to overcome these limitations, various methods for estimating alighting stops have been developed. This study classifies trips with missing alighting stop information into trip four types and then applies appropriate alighting stop estimation methodology for each trip type in stages. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checking the accuracy for each standard of allowable error for sensitivity analysis. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can estimate users’ destinations more accurately than the methodologies of previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.