This research used approximately 1.9 million data of daily customer numbers of cars and motorcycles of CPC Self-Operated gasoline stations from 2010.01.01 to 2018.09.30. First of all, we applied data visualization such as tree maps and word clouds to help quickly understand the distribution of customer numbers of cars and motorcycles between weekdays and 9 business branches. Then we implemented time series intervention analysis on the change of customer numbers of cars and motorcycles during 10-day Lunar New Year holidays. Understanding the distribution of customer numbers of cars and motorcycles of 9 business branches can help the strategy of marketing, transporting and storing. Research about Lunar New Year Effects can be a reference of the policy of storage during the holidays.