PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

PURPOSES : The main purpose of this study is to identify vulnerable areas by evaluating public transit accessibility for the introduction of smart mobility. METHODS : This study proposes a methodology for analyzing public transportation accessibility. We identified the less accessible areas of public transit in Daegu Metropolitan City by dividing them into low- and high-facilitated areas considering travel demand and developed plans to introduce smart mobility based on the analysis results. RESULTS : Areas vulnerable to public transportation in Daegu Metropolitan City can be divided into those with low public transportation accessibility and low usage rates, those with good public transportation accessibility but low usage rates, and those with low public transportation accessibility but high usage rates. CONCLUSIONS : Based on the results of this study, it is possible to introduce customized services for each area with poor public transit accessibility, and some of the inconveniences experienced by citizens using public transit are expected to be resolved.

With the rapid development of the Internet and modern economy, people gradually come into contact with mobile terminals, and their demand for them is also growing. In particular, in recent years, the number of mobile payments in China has also shown a continuous growth trend, and there have been mobile payment means such as Alipay, WeChat, Apple pay and so on. At the same time, with the emergence of new technologies such as big data, cloud computing and financial payment, the technical means of mobile payment have become more and more diverse, which provides space for the continuous development of mobile payment. The construction and improvement of the bus card system has always been an important issue to promote the development of urban traffic. It is also a problem that the government should solve in the construction of a new city. A more perfect bus card system can help to improve the frequency of the residents to use the bus travel and help to solve the urban traffic problems. This paper mainly aims at the application of mobile payment in the field of public transportation, analyzes the current situation and trend of the payment in the field of public transportation, and analyzes and studies the existing problems and solutions of the application of mobile payment in the field of public transportation.