Objectives
Work package 6300 will analyze all available data sources (except questionnaire surveys, which are addressed in WP 6100) to provide insights into the impact of the pandemic on the use of different modes of transportation. The goal here is also to provide an overview of the available data sources along with their usefulness for transportation research. This will not only look at what insights can be gained from a data source with regard to the use of a specific mode of transport, but also how the different data sources can be combined and what their respective advantages and disadvantages are.
Procedures and methods
First, all potentially relevant data sources were reviewed:
- Mobile Data
- Floating Car Data
- Counting station data (cars, bicycles, pedestrians)
- Webscraping data (e-trekkers, rental bikes, car sharing)
- Air traffic statistics
- Data on the use of public transport
All freely available data were obtained and analyzed directly. In addition, a floating car data set from the company INRIX was purchased.
For continuously collected and available data, the time since the beginning of the first lockdown in Germany (March 22, 2020) was compared to the time before the pandemic. For bicycle counting stations in particular, this allowed the long-term average of the number of bicycles counted to be related to the number of bicycles counted during the Corona pandemic. Hourly count station data for cars, bicycles, and pedestrians allowed analyses adjusted for daytime, weekday, and season. Since bicycle use in particular is strongly influenced by weather conditions, the bicycle count station data were also enriched with weather data from the German Weather Service to also control for the influence of temperature, precipitation, and wind.
On the other hand, for data that had to be purchased or requested, specific comparison periods for the time during and before the pandemic were defined and compared. These comparison periods include a June week in the summer of 2020 and a June week in the summer of 2019, both weeks chosen to exclude school vacations and public holidays in each case to best represent usual mobility. June was chosen because by that time in 2020, mobility had returned to some degree of normalcy after the initial lockdown and society as a whole had reached a state that many referred to as the "new normal."
Results
The results of the work package were presented, among others, in a lecture at the seminar series "Railway Engineering" of the Institute of Land and Sea Transport of the Technical University of Berlin: https://www.ews.tu-berlin.de/sommersemester_2021/31_mai_2021/
Further publications in scientific journals are in progress.