How Covid-19 changes the way we commute by MRT system in Taiwan?
Organisation size: Small
Publication date: 8 May 2020
Credit: Pei-Yu Chen , Yu-Ju Lee, Hsin-Chan Chieh
In March last year, Taiwan appeared a wave of overseas immigration due to the large numbers of overseas students and foreigners returning to the country: within two weeks from March 17, the number of confirmed cases has exceeded 10 people for 12 days. The government especially The company requested people should start the plan to work in a different place, and advised the public to stay at home as much as possible without going out. We grab the MRT traffic data on march in recent years to compare the change of MRT traffic volume affected by the epidemic.
The report compares the MRT traffic data on march in recent years, and we find that compared with the past 5 years, the number of passengers in march this year has dropped significantly, and the crowd is only 81% of the 5-year average. However, if we observe the changes in the number of people entering and leaving the station during commuting, the reduction is limited. On the other hand, the number of people entering and leaving the station during non-commuting hours and holidays has been significantly reduced. It can be seen that most people who originally take the MRT don’t change their way of commuting, but during the non-commuting time, they can try not to take the MRT.
We fetched the MRT data by Python script. The Python script crawled the MRT station data, and exported the csv files. We analytics the csv files by R, to make the traffic data for each station by hourly. And we make the charts by R ggplot2 library to visualize the traffic to understand how people move during the covid.
What was the hardest part of this project?
The most difficult part is to find evidence to distinguish the correlation between changes in the number of people entering and leaving MRT stations and commuting.
What can others learn from this project?
COVID-19 has been raging for more than a year, and has completely changed human behavior patterns. The report is based on the data of the MRT system, trying to interpret how people’s lives are affected. For example, compared with the number of people entering and leaving the station last year, the MRT station at tourist spots and the airport have both fallen by more than 30%.