- MOBIS-COVID19/34
- 1 News
- 2 Introduction
- 3 Average daily distance
- 4 Active days
- 5 Change in kilometers travelled by transport mode
- 6 Key indicators by mode
- 7 Change in kilometers travelled by…
- 8 Analysis of trip purpose
- 9 Road travel speeds
- 10 Shifts in the transport mode share
- 11 Reduction in kilometers travelled by working arrangement
- 12 Reduction in kilometers travelled by canton
- 13 Trip duration by transport mode and gender
- 14 Average stage length by transport mode (km)
- 15 Activity space and daily travel radius
- 16 Hourly counts
- 17 Participation
- 18 Risk perception
- 19 Differences in the distributions
- 20 Sample weighting
- 21 Updated conclusions
MOBIS-COVID19/34
Results as of 08/02/2021 (Second Wave)
A Project of IVT, ETH Zurich and WWZ, University of Basel
This work is licensed under Creative Commons. 
Contact: Joseph Molloy (joseph.molloy@ivt.baug.ethz.ch)
Previous and future reports can be found at: https://ivtmobis.ethz.ch/mobis/covid19/en
1 News
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:
- Added breakdown of the current sample to the section Differences in the distributions
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.
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.
This week’s report is the first based on the new sample.
The results are shown in comparison to those of the first 4 weeks of mobility data from the original MOBIS Study which were recorded between 1st September and 15th November 2019, and thus serve as a baseline well before the pandemic hit Switzerland. The four weeks are spread over a period of time as the sample was built up successively in waves. Only trips inside Switzerland are currently considered, although data on cross border travel is available. 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.
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.
3 Average daily distance
[1] “Download chart data”
4 Active days
[1] “Download chart data”
5 Change in kilometers travelled by transport mode
[1] “Download chart data”
[1] “Download chart data”
6 Key indicators by mode
[1] “Download chart data”
7 Change in kilometers travelled by…
9 Road travel speeds
The following graph shows the effect of the COVID-19 crisis on median car travel speeds during the week, i.e. excluding weekends and holidays. During the lockdown period from March 16th to May 11th, an increase in the peak-hour speeds was observed, indicating a decrease in overall congestion. Since the relaxation of the measures, peak-hour speeds have returned to pre-COVID-19 values, a sign that congestion is back to usual levels.
[1] “Download chart data”
10 Shifts in the transport mode share
A ternary plot is the graphical representation of triplets of numerical data. It is suitable for representing a constant sum, which is broken down into three summands. The following figure shows an example of such a plot with a single point. The triplet corresponding to this point can be read by following the green lines: A=0.5, B=0.3 and C=0.2. The sum of the three values is equal to 1.
The following ternary plots show the change in mode shares over the course of the COVID-19 crisis, for different types of public transport subscriptions (GA, Halbtax and other). The modes are grouped into the following categories:
- Motorized individual transport (car, motorbike, taxi, Uber)
- Public transport (bus, tram, ferry, metro, train)
- Unmotorized transport (walk, bike)
During the lockdown, a higher share of kilometers and trips were performed using motorized individual and unmotorized modes as compared to the reference period. After the lockdown, the share of public transport has increased and the share unmotorized modes has decreased, both slightly. The share of motorized individual modes it still greater than during the reference period.
11 Reduction in kilometers travelled by working arrangement
Participants in MOBIS-Covid19 were asked to report their working status on 24/4/2020. The following charts use these results, with the working status imputed using sociodemographic indicators for those who didn’t respond. Specifically, we asked for the number of days working both at home and out of home, and these were then grouped into the categories used below:
- Working only at home
- Only working out of home
- A mix of home office and normal conditions
12 Reduction in kilometers travelled by canton
[1] “Download chart data”
13 Trip duration by transport mode and gender
[1] “Download chart data”
14 Average stage length by transport mode (km)
[1] “Download chart data”
15 Activity space and daily travel radius
A commonly used definition of the activity space is the 95% confidence ellipse of the activity locations, in this case weighted by duration. In the following analysis, the activities at the home location are included, for those that had the app activated on that day. This is an important metric which gives an idea of the area in which travel is being performed. The daily travel radius is also presented.
[1] “Download chart data”
16 Hourly counts
The number of trips started per hour. The y axis is normalized by the maximum hourly value in the graph.
17 Participation
18 Risk perception
A first analysis from the survey on risk perception in the case of a COVID-19 infection shows that participants evaluate the risks of various outcomes differently for themselves and the Swiss population. The possibility that the participant experiences severe symptoms that require hospitalisation or fatal symptoms is considered somewhat lower by the participants for themselves than for the Swiss population. Both men and women appear to overestimate the probability of death by expecting the “asymptomatic” course to be less likely. While the median values for the various symptom categories do not differ greatly between men and women, the range of values for men is somewhat wider than for women
19 Differences in the distributions
The following charts show the characteristics of the MOBIS:COVID-19 sample compared to the original MOBIS Sample. There are some small differences, but generally the samples are consistent. This chart will be extended to compare to the relevant census data.
| N | % | N | % | N | % | |
|---|---|---|---|---|---|---|
| Aargau | 125 | 5.8 | 61 | 6.5 | 4,325 | 7.6 |
| Basel-Landschaft | 207 | 9.6 | 73 | 7.8 | 1,940 | 3.4 |
| Basel-Stadt | 51 | 2.4 | 23 | 2.5 | 1,555 | 2.7 |
| Bern | 248 | 11.4 | 122 | 13.1 | 7,244 | 12.7 |
| Fribourg | 28 | 1.3 | 16 | 1.7 | 1,942 | 3.4 |
| Geneva | 142 | 6.6 | 49 | 5.3 | 3,062 | 5.4 |
| Graubünden | 12 | 0.6 | 6 | 0.6 | 1,385 | 2.4 |
| Luzern | 35 | 1.6 | 30 | 3.2 | 2,658 | 4.7 |
| Neuchâtel | 15 | 0.7 | 12 | 1.3 |
|
|
| Other | 15 | 0.7 | 16 | 1.7 | 5,819 | 10.2 |
| Schaffhausen | 6 | 0.3 |
|
|
560 | 1.0 |
| Schwyz | 28 | 1.3 | 14 | 1.5 | 1,005 | 1.8 |
| Solothurn | 38 | 1.8 | 23 | 2.5 | 1,813 | 3.2 |
| St. Gallen | 40 | 1.8 | 22 | 2.4 | 3,286 | 5.8 |
| Thurgau | 15 | 0.7 | 13 | 1.4 | 1,799 | 3.2 |
| Valais | 22 | 1.0 | 13 | 1.4 | 2,172 | 3.8 |
| Vaud | 332 | 15.3 | 102 | 10.9 | 5,303 | 9.3 |
| Zug | 14 | 0.6 | 9 | 1.0 | 812 | 1.4 |
| Zurich | 780 | 36.0 | 321 | 34.4 | 10,410 | 18.2 |
20 Sample weighting
In order to account for the fluctuating size and composition of the sample, participant weights have been calculated and applied for each week of MOBIS-Covid Study (including the weeks in the baseline period). Hence, the results are corrected for weeks where more participants from a certain demographic group started or stopped tracking. The weighting was performed against the original 21,571 participants who completed the introductory questionnaire in the MOBIS Study using IPF (Iterative Proportional Fitting) using the following variables: age, gender, income, education, mobility tool ownership and accessibility. The weighting of the data did not lead to large changes in the results.
21 Updated conclusions
Since commencing last spring, the MOBIS:COVID-19 tracking study has recorded 964,339 person-trips as of 2021-02-08. It includes three small surveys about the work situation and the health of the participants. A maximum of 1,370 persons participated and an average of 808. We use the Catch-my-Day smartphone app, which is based on MotionTag technology. The sample is roughly comparable in socio-demographics to the latest federal 2015 Mikrozensus, but for a bias towards higher income season-ticket owning better educated males. The denser parts of the German and French speaking parts of the country are also overrepresented in our sample. The conclusions are based on weighted results which ensure that the sample matches the representative sample of around 20,000 invited persons in the original MOBIS study. The weights are calculated based on age, sex, education, income, mobility tool ownership and the accessibility level of a person’s home location.
Our study is not the only source for Switzerland which traces the impact of the pandemic and of the measures implemented to contain it. The Intervista panel has been funded by different federal offices during these months. The available Google data has the advantage of being available worldwide, but only for high-level geographies and without any socio-demographics. We can draw on a more detailed self-description of the participants using their socio-demographics and their attitudes.
The central observations focus on the ongoing acceptance of “Working from Home (WFH)” or in German “Home-office” and the modal shift in the recovery of the trip volumes and in the miles driven. Equally important is the comparatively much lower social selectivity of the impacts in Switzerland.
The share of active, i.e. mobile, days shows the anticipation of the lockdown and then the recovery and stabilization to about 80% in August. This is substantially below the 90+% share, which one can expect (Madre et al., 2007). This lower share must be a mixture of WFH, persons furloughed or newly unemployed. We don’t yet know how firms will handle WFH in the coming weeks after the end of the current lockdown. There have been prominent announcements of firms not calling staff back to the office, e.g. Facebook, twitter, PSA, but equally prominent ones calling their employees back, e.g, Stadler. If mobilized, this suppressed demand would meet a road system where the average speeds are again at pre-pandemic levels – conditions which could lead to increased congestion. The speed levels are explained by the low public transport shares and an unchanged distance for those who drive.
The population has shifted away from space-efficient large vehicles, as buses, trams and trains remain unpopular. Usage is down between 40% to 60% of the 2019 ridership after a near complete avoidance in the first weeks after the lockdown. On average, car travel has fully recovered. Equally, walking has fully recovered. It is worth noting that walking levels never dropped in the same way as for other modes. The surprise was, and still is the increased bicycle usage backed up by a boom in cycle acquisition during the good-weather months. While the increase seemed initially to be mostly a fitness, leisure boom, the recent imputation of the trip purposes has shown that the cycle gained for all purposes with leisure and shopping the most prominent ones. Commuting to work by bike also increased, but not as strongly (about 40% versus 60-80% for the other purposes). The new freedom in time allocation is visible in the cycling for leisure during the day. Regression-based analyses (soon to be part of this report) indicate that the modal shifts are not driven by weather. The results are qualitatively similar when controlling for temperature and precipitation.
Finally, the experience of the COVID-19 period confirms the results of earlier telecommuting experiments (Pendyala et al., 1991). They had shown that the total mileage is not reduced, as the persons used the liberated time for other travel. Here, the Kurzarbeiter (furloughed) track the trends and actually travel a bit more than the other workers. Those working from home do travel less than those who have to go to their workplaces, but the difference is not nearly as large as one would expect.
The inclusion of the new LINK recruited participants did not change the trends seen before. This highlights the overall quality of the study and of its sample.
References:
Madre, J.-L., K.W. Axhausen and W. Brög (2007) Immobility in travel diary surveys, Transportation, 34 (1) 107-128.
Pendyala, R.M., Goulias, K.G. and R. Kitamura (1991) Impact of telecommuting on spatial and temporal patterns of household travel, Transportation, 18 (4) 383-409.