1 News

7 Dec:

  • The conclusions have been updated

8 June:

  • Some graphs have been extended to display the full range of data points from October 2019 onwards.
  • The sociodemographic graphs have been smoothed to improve interpretability. The un-smoothed data is still available at the download link.
  • New home office analysis. link
  • Unification of the color scheme and wider graphs.

31 May:

  • Due to regional differences in the regional composition between the Link and original MOBIS panels, the report has been adjusted to only include participants who meet the original MOBIS eligibility criteria. Forthcoming analysis will include all participants where a comparison with 2019/20 is not needed.

25 January:

  • There have been significant changes since the last report.
  • Since Nov. 12, a co-operation with the company LINK has allowed us to increase the size of our panel. The results now include these new participants, and the changes to the panel distribution are indicated here: link
  • This does mean changes to some of the graphs from November onwards - due to the new sample composition.
  • The results of online surveys on changes to participants’ employment and household statuses have been integrated into the results.

14 December:

  • Most charts are now interactive. The effects of the winter weather are now clearly visible.

Previous news (click to expand)

18 November:

  • Charts 2-6 are now interactive and zoomable.

2 November:

  • The method for the calculation of active days was improved, which affects some of the results.
  • New graph of average daily distance by trip purpose. link

6 October:

  • The chart showing the daily average distance by gender has been corrected to show total distance, whereas it previously included only car travel. Also the same graph now dispalys a 4 day rolling average for visibility. The original values are still available in the downloadable data.

28 September:

  • New chart with key transport-mode indicators added. link

24 August:

  • Updated conclusions.
  • The report will now be updated every second week.

11 August:

  • New analysis of trip purpose (mode shares and hourly counts). link
  • Weekly weights of the sample against the original MOBIS study have been applied to all results. link

6 August:

13 July:

  • As of July 6th, face masks are compulsory on public transport in Switzerland.
  • To reflect this development, the relevant graphs have vertical lines added indicating the start of the lockdown (March 16th), relaxation of the lockdown (May 11th) and the introduction of the mask requirement (July 6th).

29 June:

  • New analysis on the shifts in the transport mode share - link

15 June:

  • The data can now be downloaded directly for certain requested charts. Please make sure to cite both the IVT, ETHZ and WWZ, Uni Basel as the source.
  • Hourly counts now show the whole day - Midnight to 4am is no longer excluded.
  • Analysis by home office - link.
  • In response to multiple inquiries, we would like to clarify that the baseline-2019 period covers September and October 2019.
  • Results by gender corrected
  • New analysis of car travel speeds by distance class - link.

25 May:

  • Adjusted # Activities/Day so that the first home activity per day is not included.
  • Converted long tables to graphs.

18 May:

  • First report after the relaxation of lockdown measures on May 11th.
  • The 2019 Baseline period has been shortened to only include September and October - This mostly affects the cycling numbers.

11 May:

  • New graph of activity space and daily radius - link.

4 May:

  • New chart from online survey with participants on risk perception - link.
  • Key points summary and formatting adjustments.

27 April:

  • New chart on the change in activity type by land zoning.

20 April:

  • Mobile participants per day.
  • Non-mobile participants are now included in the activity-space numbers in addition to a new table on median weekly activity spaces.
  • New graphs, including average trip distance by mode.
  • Formatting improvements and other small corrections.

13 April:

  • Earlier weeks have been grouped and colored grey in certain graphs.

2 Introduction

On March 16, 2020, 3700 participants who completed the MOBIS study between September 2019 and January 2020 were invited to reinstall the GPS Logger and Travel Diary App ‘Catch-My-Day’, developed by MotionTag. This voluntary recording of their mobility behaviour allowed us to track the impact of the various special measures during the unfolding pandemic. The pandemic is still going on one year later and many participants are still tracking.

The results are shown in comparison to those of the first weeks of mobility data from the original MOBIS Study which were recorded between 1st September and 1st November 2019, and thus serve as a baseline well before the pandemic hit Switzerland. Only trips inside Switzerland are currently considered, although data on cross border travel is available.

Participation decreased from about 1’300 participants to around 500 by the start of the second COVID19 wave in autumn/fall 2020 for any number of good reasons, such as a new smartphone, operating system updates, etc.. About 250 rejoined the panel after a second invitation in October 2020. We are very grateful for their engagement. Still, we happily agreed, when LINK offered to recruit more participants to the panel. This further increase of our sample allows us to complement the existing core. By mid-January a total of 393 additional participants had joined via LINK.

For the MOBIS study, participants were only eligible if they used a car at least 3 days a week - which skews the sample away from the Swiss general population. We did not impose a similar condition on the LINK-recruited participants as we are now aiming towards a more representative sample of the population. However, this means that the sample as of 2021 is no longer comparable to 2019 and 2020, as the mobility behaviour is quite different between the two groups. As such, for any analyses which makes comparisons to the pre-pandemic era, only link participants who meet the MOBIS-criteria are included.

The number of tracking participants each day used to calculate the average daily values, includes all participants who recorded tracks before or after that date. This allows the consideration of those who stay at home while still allowing for survey dropouts.

The GPS Travel diary used, Catch-My-Day (for iOS and Android) can have a 2-3 day delay before the tracks are available for analysis. The scaling by active participants accommodates for this, but the results of previous reports may change when the report is updated. The scales are calculated against the representative sample we obtained as part of the MOBIS recruitment process.

The colors in the graphs below are selected to indicate transport mode groupings. The greens indicate active modes, and the blue/purples public transport. brown is Car, and black is the total. These colors are consistent through following transport mode related graphs.

3 Average daily distance

Here, the average daily distance travelled by participants is presented, differentiated by gender. To aid readability, a 7-day rolling average is used. The clear reduction in travel caused by the lockdown at the start of the pandemic is visible, as well as the gradual increase over the following months. The amount of travel is slowly recovering to pre-pandemic levels, as seen during the relatively normal period before the second wave in Autumn 2020.

[1] “Download chart data

4 Active days

llustrated below is the number of participants who are ‘mobile’ on a particular day. That means that they logged some travel in the Catch-my-day app, even a short walk. The downward spikes indicate the weekends. The 7-day rolling average is given by the black line, which shows relative stability since the end of the first lockdown. This is the case even during the second partial lockdown. Various small variations are driven primarily by public holidays where people remain at home.

[1] “Download chart data

5 Change in kilometers travelled by transport mode

Here we see the breakdown by travel mode. The values are given relative to the pre-pandemic behavior, calculated based on the average from September and October 2019. Particularly evident is the large increase in cycling observed during the lockdown, which was sustained throughout the summer of 2020. Public transport usage collapsed during the lockdown and recovered much more slowly than other modes. It is still at around only 50% of pre-pandemic levels.

Also presented in this section is a stacked version of this graph, with public transport modes grouped together. This graph makes it evident how the overall mode share has changed, with driving and cycling taking modal share away from public transport.

[1] “Download chart data

The following two figures present regression estimates based on a Poisson model. Such a model can be used to estimate the average proportional change in a variable of interest while controlling for confounding factors. We control for weather effects and person fixed effects to absorb unobserved heterogeneity that is constant across time. The vertical lines or the colored bands mark the 90%-confidence intervals.

[1] “Download chart data

6 Key indicators by mode

This section presents additional indicators by mode, in addition to the daily distance that matches the previous graphs in section 5. Again, these graphs present the percentage change when compared to the baseline period in 2019. Particularly interesting is the variation in trip distance. Walk trips during the lockdown were much longer, but this behavior wasn’t sustained afterwards. On the other hand, bus trips have become shorter since the start of the pandemic, potentially driven by home-office trend.

[1] “Download chart data

7 Sociodemographics

The graphs below present the change in average daily kilometers by various sociodemographic variables. The lines have been smoothed to improve readability. For some demographics, be aware that there are however only a small number of participants (see the distributions section). The difference by household size during and after the lockdown are particularly interesting, as is the behavior of the 25-35 age group in summer 2020. During the first lockdown higher income groups reduced their daily travel more.

7.1 Age

7.2 Education

7.3 Employment status

7.4 Gender

7.5 Household Size

7.6 Monthly Income

7.7 Correspondence Language

7.8 Access to car

8 Analysis of trip purpose

The purpose of each trip is taken from the activity performed at the destination of the trip. The purpose was imputed using a random forest model, using training data from those who voluntarily recorded the purpose of their activities. Some trip purposes show a larger shift in modal split than others. For shopping, during 2020, cycling became more popular for going to the shops. Grocery shopping and discretionary shopping are not differentiated. The train is almost never taken to go shopping, whereas local public transport is still used.

For commuting, public transport usage lost most of its share during the lockdown. Since the lockdown walking has a continually increasing share, which can mostly likely be attributed to the shift towards home office and the resulting fewer work trips.

[1] “Download chart data

8.1 Assistance