Moving Average Method on Big Data in Intercity Transportation in the Post-COVID-19 Era

In 2020, the outbreak of COVID-19 pneumonia has had a great impact on China’s economic and social life. The construction and transportation industries have been greatly impacted and suffered from its high mobility. This paper studies the big data of migration between Xi’an and Chengdu from January 1, 2020 to March 15, 2020 and divides the epidemic situation into four stages according to the introduction of the elastic coefficient according to the development of the epidemic situation. In each stage, the elastic coefficient of index change is introduced in combination with the decreasing impact of epidemic prevention and control measures on transportation. Finally, a modified moving average method is formed, which is compared with the ordinary moving average method. The results show that the modified moving average method combined with Hadoop big data platform can improve the accuracy and efficiency of the intercity transportation flow prediction under the epidemic situation.