The impact of COVID-19 related regulations and restrictions on mobility and potential for sustained climate mitigation across the Netherlands, Sweden and the UK: A data-based commentary.

Comment

Changes made to text

Overall comments from editor

1

The sections of the reviewers' comments questioning aspects of the way links between Covid-19 regulations, mobility and climate change mitigation are being made. There are some particular comments about EV that seems well formulated and need answering.

It may be worth pointing up that transport emissions are a difficult area to address but one that maybe particularly susceptible to a combination of new technology/market products and behaviour. Perhaps the supporting literature is limited in this respect (especially in terms of the Covid elements!). Do point up the peculiar situation that Covid created, and that this still needs more work to understand. The unique context for the paper needs to be very clear.

The following changes have been made in relation to the reviewers’ comments around Electric Vehicles and the context of emissions during COVID:

A footnote has been added to explain the divergence in definitions of Electric Vehicles between the Netherlands, Sweden and the UK (page 14):

‘Electric vehicles’ were defined differently between the Netherlands, Sweden and the UK. Where it was available, we have included information regarding how Electric Vehicles were defined.

Ongoing increases in the sales of Electric Vehicles has been noted in section 3, page 8:

“Additionally there has been an increase in the trend of Electric Vehicle sales in globally recorded in recent years, with the sales of plug-in hybrid electric vehicles and battery electric vehicles increasing by over 40% in 2019 compared to 2018 (International Energy Agency, 2020)”

Section 5.3 highlights that there is some evidence of intentions to increase private transport use after COVID-19 restrictions end. In light of the (parallel rather than causational) increase in EV purchases, we felt this represents an opportunity for policy-makers to consider how to integrate EVs into local transport systems.

To clarify, section 5.3, page 12 has been edited:

“The increase in sales of Electric Vehicles was happening prior to, and in parallel with COVID-19 related restrictions, rather than because of them. However, policy-makers could use the moment of change represented by the pandemic to consider ways to support the use of private Electric Vehicles (including electric-bikes and electric-scooters) over internal combustion engine vehicles, to uphold motivation and increase opportunities to maintain the increase in Electric Vehicle sales

References to global carbon emissions in 2020 have been added to section 2, page 4 to provide context for the impact of reduced transport emission:

Total energy consumption encompasses more than individual transport behaviours. A global drop in energy demand was reported in 2020 and global energy-related carbon emissions were reduced by 5.8% compared to 2019, representing the largest annual decline since the Second World War, and suggesting that the reduction in energy use for transportation did not result in a shift in demand for energy use in domestic settings (International Energy Agency, 2021). These global trends were also reported in the UK and Sweden (Department for Business Energy & Industrial Strategy, 2021; Statistics Sweden, 2021).

Acknowledging potential long-term changes has been added to section 4, page 11:

Additionally, there is a possibility of long-term changes in urban structure and land uses attributable to COVID-19 related restrictions.  These changes may result in people and companies moving away from city centres, decreasing motivation for investment in and use of public transport.

References to the need for further data collection have been made throughout and specifically relating to the sale of electric vehicles in section 5.3, page 12:

Data collection regarding the impact of increasing sales of Electric Vehicles on transport-related carbon emissions will increase understanding of this behaviour.

2

The Conclusion section could be strengthened - for example: does the information from each country suggest some common policy platform can be created or would there need to be separate policies in each country given the different dependency on the various modes of transport available or that are perceived to be needed in each country? All these countries have different transport mixes to meet the population mobility needs - can that factor be given slightly more prominence?

The following has been added to the conclusion, section 7, page 13:

The intended outcomes of interventions to maintain pro-environmental behaviour changes show similarities across the three countries we examined. However, the different transport systems and work-related cultures mean that different strategies for implementing policies may be necessary. Inter-country networks who bring together expertise across different relevant disciplines, such as the European Behaviour Change Network, can enable understanding of how different interventions could be implemented across different geographical locations.

3

The validity and applicability of the behavioural model chosen needs some further justification. Has it been used in transport or mobility work previously? If so, what was its utility? Were any policy changes made if it was used?

Further justification of the behavioural model used to illustrate the potential impact, along with clarity around the uncertainty of future trends, has been added to section 4, page 9 and 10:

These tools have been used to inform the development and evaluation of interventions and policies concerning behaviour change in local government in England (West et al., 2019). The Behaviour Change Wheel has also been used to illustrate the potential impact of behaviour change interventions to increase adherence to health related policies during the pandemic (Michie et al., 2020) and to review pro-environmental behaviour change interventions (Addo et al., 2018; Hedin et al., 2019; Staddon et al., 2016).

We cannot predict with certainty how citizens will behave once government recommendations and restrictions end as there is always uncertainty associated with predictions of future behavioural trends.

Reviewer 1

1

First part of introduction (Covid-19 related changes and impacts) could benefit from references to publications on the impacts of Covid-19 on air quality or the environment (reducing carbon emissions etc.). Give some examples and references.

The following has been added to section 1, page 3:

Examples of measured impacts due to these changes are improved air quality (Fuller, 2020; NASA Earth Observatory, 2021; SLB Analys, 2020), noise reduction in inner cities (Rumpler et al., 2020) and carbon emissions (Le Quéré et al., 2020)

2

As referring to “global warming” as “climate change” will cause confusion while reading, it would be better to use only the word “climate change”.

‘Global warming has been changed to ‘climate change’ throughout the commentary.

3

There is a clear change in transportation behaviours, however it would be good to look at total energy and fuel consumptions in these countries to make a good comparison and to clarify whether total consumption shifted from transportation to home usage or not.

The following has been added to section 2, page 4:

Total energy consumption encompasses more than individual transport behaviours. A global drop in energy demand was reported in 2020 and global energy-related carbon emissions were reduced by 5.8% compared to 2019, representing the largest annual decline since the Second World War, and suggesting that the reduction in energy use for transportation did not result in a shift in demand for energy use in domestic settings (International Energy Agency, 2021). These global trends were also reported in the UK and Sweden (Department for Business Energy & Industrial Strategy, 2021; Statistics Sweden, 2021).

4

In the paragraph starting “In April 2020, COVID-19 associated restrictions…”, rephrase the sentence of “By the end of March 2020, global road transport activity was almost 50% below the 2019 average (International Energy Agency, 2020).” Because it does not accurately reflect the IEA report.

The sentence referred to has been changed in section 2, page 4:

Globally, a 50-75% reduction in road traffic was seen during lockdown restrictions (International Energy Agency, 2020).

5

As mentioned in the paper, there is an increase in electric car sales and a decrease in internal combustion engine car sales. As there is no concrete evidence to show Covid-19 related behavioural changes caused these increases and decreases, it would be good to mention other factors such as shutdowns of the auto industry and suppliers around the world during several weeks due to Covid-19 restrictions. There is a decrease in total car sales due to decline in car production and a lack of availability due to showroom closures. It is not just behaviours of people working from home or changes in transportation. In addition, there is a clear increasing trend in electric vehicle sales over the years. In Europe, sales (plug-in hybrid electric vehicles and battery electric vehicles) increased by over 40% in 2019 compared to 2018 (see the figure (IEA, Global electric car stock, 2010-2019, IEA, Paris https://www.iea.org/data-and-statistics/charts/global-electric-car-stock-2010-2019)).

Context around the increase of electric Vehicle sales and decrease in internal combustion engine car sales has been added to section 3, page 8:

Reasons for the decrease in internal combustion engine vehicle sales could be increased working from home, global shutdowns of the auto industry, disruptions to suppliers and showroom closures during Covid-19 restrictions. However, disruptions caused to sales of internal combustion engine vehicles caused by Covid-19 restrictions would also apply to the sale of Electric Vehicles. Additionally, there has been an increase in the trend of Electric Vehicle sales in globally recorded in recent years, with the sales of plug-in hybrid electric vehicles and battery electric vehicles increasing by over 40% in 2019 compared to 2018 (International Energy Agency, 2020).

6

Even if there is limited data available, it would be good to define “electric vehicles” in one way. Is it just referring to electric cars, electric bikes, or all of them? There is an inconsistency in the statements:

1. In the Netherlands data: The sales of Electric Vehicles (Plug-In Hybrid Vehicles and Fully Electric Vehicles) increased in 2020 compared to 2019, with sales of Electric Bikes (speed pedelecs) and mopeds (electric and manual) doubling during summer 2020 compared to summer 2019.
2. In Swedish data: Of car sales made, Electric Vehicles accounted for 32.2% compared to 11.3% in 2019.
3. In the UK data: Sales of Electric Vehicles (Fuel Cell Electric Vehicles and Plug-in Hybrid Electric Vehicles) increased by 185.9% and 91.2% respectively.

Unfortunately, as each country employed different definitions of an ‘electric vehicle’, we have been unable to find one definition that would accurately represent all the descriptions. A footnote has been added on page 14 explain the divergence between the countries:

‘Electric vehicles’ were defined differently between the Netherlands, Sweden and the UK. Where it was available, we have included information regarding how Electric Vehicles were defined.

7

In the paragraph starting “Data from Transport for London showed tube journeys decreased by 94% or 100 million…”’ it is enough to give the changes in percentages only. Give details (changes in million) in another sentence.  Also, define the period of the changes in bus use (Transport of London and Citymapper). Compared to what?

To report data as consistently as possible between the three countries, we have removed the references to the reduction of number of journeys for the UK data presented in section 2.3.

The comparable time points for the data presented related to bus use is 2019. This has been added to section 2.3, page 6:

Bus use in London was down by 80% in April-May 2020, and 42% in October 2020 (Transport for London, 2020) compared to data for the same time points in 2019. These data are consistent with data from Citymapper showing a reduction of transport use in London by 80-90% in April and May 2020 and 40-50% in November 2020 compared to 2019 (Citymapper, 2020).

8

After giving all details in the section on countries’ data, it would be good to have a table showing differences and similarities in terms of changes (percentage of people working from home, the use of public transport, etc.) This will make it easy to understand for readers.

A table illustrating the main points of data across the three countries has been added to section 2, page 7.

9

In Figure 1, it is a bit hard to read at first glance, so increase fonts and image quality.

The font size of figure 1, on page 9, has been increased, along with making the figure a little larger to increase image quality.  

10

In the paragraph starting “We cannot predict with certainty how citizens will behave…”, it lacks balance, and it would be good to think about some of the what-ifs. For example, some people want to go back to their normal routines (maybe even worse in terms of consuming and travelling etc. as they were stuck at home for a long time) pre-Covid-19.

To account for different behavioural scenarios, the following has been added to section 4, page 10:

It is possible that desires to return to pre-pandemic levels of consumption and travel, or beliefs that using public transport increases the risk of exposure to Covid-19, may increase reflective motivation to use private transport. Collecting and analysing data relating to transport behaviours will inform our understanding of if and how motivations, capabilities or opportunities change once government restrictions have ended.

11

As many people stay at home and work from home during the pandemic, online orders, and shopping (food, electronic, cosmetic, fashion etc.) increased and gave extra pressure on delivery and shipment (many motorbikes and delivery vehicles), It would be good to have a look at data and to think about how this affected the transportation (maybe increasing carbon emission).

The following has been added to section 2, page 4:

Total energy consumption encompasses more than individual transport behaviours. A global drop in energy demand was reported in 2020 and global energy-related carbon emissions were reduced by 5.8% compared to 2019, representing the largest annual decline since the Second World War, and suggesting that the reduction in energy use for transportation did not result in a shift in demand for energy use in domestic settings (International Energy Agency, 2021). These global trends were also reported in the UK and Sweden (Department for Business Energy & Industrial Strategy, 2021; Statistics Sweden, 2021).

12

Finally, even if the conclusion section gives the main arguments, it would be good to make it stronger.

The following has been added to the conclusion, section 7, page 13:

The intended outcomes of interventions to maintain pro-environmental behaviour changes show similarities across the three countries we examined. However, the different transport systems and work-related cultures mean that different strategies for implementing policies may be necessary. Inter-country networks who bring together expertise across different relevant disciplines, such as the European Behaviour Change Network, can enable understanding of how different interventions could be implemented across different geographical locations.

Reviewer 2

1

The first point concerns the potential of long-term changes in urban structure and land uses, that have not been considered in the paper. While I agree that work-from-home can considerably reduce commuting carbon emission under the present urban structures, it is not clear what the long-term effect of this behavioral change will be in residential location patterns. Commuting time is a key factor for households when deciding where to live and given that in most cases employment is located in central areas, this is a key factor determining how far to live from the city center. In the cases of cities well served by transit, this incentivizes moving to locations with good transit access.  Released from this constraint, households are now free to choose locations even farther away from the city center and might not prioritize transit access as much, fueling urban sprawl, which would have the negative effect of increasing car dependency and reducing the viability of transit services.  Combined with the trends reported by the authors of reductions in transit use and increases in private vehicle use, there is a non-negligible possibility that current behavioral trends result in increased emission in the long term, and this should be considered in your analysis.

There is also the issue of the city center decline as a result of less people visiting, which also fuels urban sprawl as central locations might not be as profitable for firms and retailers as before, and these might opt for non-central locations with lower land prices. In such a context, it would seem difficult to “increase opportunities to use public transport through improved infrastructure,” since its viability might actually be reduced. The same can be said for any other type of transit-oriented development and/or compact city strategies which rely on certain levels of density and land use mixes to yield benefits.

Of course, the levels of uncertainty regarding any future predictions are very high, but in the same spirit of this paper that seeks to map potential outcomes, long-term changes in urban structure and land uses should be considered, including potential negative effects.

To acknowledge this potential factor, we have added the following, section 4, page 11:

Additionally, there is a possibility of long-term changes in urban structure and land uses attributable to COVID-19 related restrictions.  These changes may result in people and companies moving away from city centres, decreasing motivation for investment in and use of public transport.

2

The second point concerns the issue of the durability of behavioral interventions. In the context of transportation planning, travel demand management strategies have been used to nudge individuals into more socially or environmentally desirable behaviors, but my concern with this kind of approach is that the evidence on the durability of behavioral changes is scarce. That is, how long do these nudges really last for is not clear, to the best of my knowledge, in the literature. As such, if the authors could show some evidence regarding the durability of these strategies, it would certainly strengthen the conclusions presented.

In the particular context of the COVID-19 pandemic, there is already some evidence of mobility patterns slowly returning to some extent to pre-pandemic levels. This is the case for Japan, which is the context I am most familiar with, but I would expect similar trends elsewhere as well. As such, it would be useful to add a temporal dimension to the changes in transport use you report in Chapter 2 in order to evaluate which effects are lasting, and which are not, at least up to the most recent data point available. Ideally you would add some plots summarizing key findings graphically. Data from the Google Mobility Panel for example would be useful in this regard (https://www.google.com/covid19/mobility/)

Some discussion relating the durability of the theories presented has been added to section 4, page 11:

In relation to moments of change:

Increases in positive environmental behaviours were recorded in participants who had recently relocated compared to those who had not, suggesting that these were experienced as moments of change (Verplanken & Roy, 2016). Evidence suggests that interventions increasing  social and physical opportunities may be most effective, with some evidence for behaviour changes being maintained over time (Staddon et al., 2016).

In relation to the temporal dimensions of the data presented, we feel it is important to present the data that represents the period up to December 2020, to demonstrate the changes that there recorded in this period compared to the same time points in 2019 and have therefore refrained from collating data from after this point. However, we acknowledge that the transport -related behaviours reported in section 2 are likely to have changed across the three countries and have added the following to section 4, page 10:

Desires to return to pre-pandemic levels of consumption and travel, or beliefs that using public transport increases the risk of exposure to Covid-19, may increase reflective motivation to use private transport. Collecting and analysing data relating to transport behaviours will inform our understanding of if and how motivations, capabilities or opportunities change once government restrictions have ended.  

3

The third point concerns the validity of the model presented. This is important since you are making policy recommendations. Although I understand this is a commentary paper, I still think it necessary to add some discussion on the validity of the model, and the uncertainty associated with predictions of future behavioral trends.

This is of course an important point. To clarify the uncertainty of behavioural trends and the benefits of using theory to inform interventions, the following sections have been edited, and a reference related to using behaviour change in local government contexts has been added to section 4, pages 9 and 10.

These tools have been used to inform the development and evaluation of interventions and policies concerning behaviour change in local government in England (West et al., 2019). The Behaviour Change Wheel has also been used to illustrate the potential impact of behaviour change interventions to increase adherence to health related policies during the pandemic (Michie et al., 2020).

We cannot predict with certainty how citizens will behave once government recommendations and restrictions end as there is always uncertainty associated with predictions of future behavioural trends. However, based on COM-B we can predict that the behaviour changes most likely to be maintained are those where there are ongoing opportunities, capabilities and motivation to continue.

Reviewer 3

1

Sections 2 and 5: EV trends and COVID-19. You acknowledged the limitations of inferences from trends alone in section 6.  A specific instance of this limitation is in section 2. Namely, trends in EV purchasing are difficult to link to the COVID 19 pandemic, a priori.  This requires you to present either empirical evidence or a plausible argument to support such a link.  If either (or both) of these are available, please present them to the reader alongside the information about EV trends in section 2. If neither are available, then please acknowledge a reason for including this information in the article other than a cause-effect or correlational link.  For instance, could EV trends be relevant in terms of a moment of change afforded for EV adoption (which would help to justify inclusion of section 5.3., which seems to have little to do with COVID-19)?

EV trends have been included in Section 2 as they are an increasingly popular mode of private transport. Although the use of EVs are not directly related to COVID-19 restrictions, we felt it was important to acknowledge their use, and indeed, the change in behaviour that is represented by the increase in purchasing of EV’s that has been seen in recent years.

This ongoing change has been noted in section 3 on page 8:

Additionally, there has been an increase in the trend of Electric Vehicle sales in globally recorded in recent years, with the sales of plug-in hybrid electric vehicles and battery electric vehicles increasing by over 40% in 2019 compared to 2018 (International Energy Agency, 2020).

Section 5.3 highlights that there is some evidence of intentions to increase private transport use after COVID-19 restrictions end. In light of the (parallel rather than causational) increase in EV purchases, we felt this represents an opportunity for policy-makers to consider how to integrate EVs into local transport systems.

To clarify, section 5.3, page 12 has been edited:

The increase in sales of Electric Vehicles was happening prior to, and in parallel with COVID-19 related restrictions, rather than because of them. However, policy-makers could use the moment of change represented by the pandemic to consider ways to support the use of private Electric Vehicles (including electric-bikes and electric-scooters) over internal combustion engine vehicles, to uphold motivation and increase opportunities to maintain the increase in Electric Vehicle sales.

2

Page 4: paragraph beginning "In April 2020, COVID-19 associated restrictions [...]" is a bit awkward, because the reader is probably expecting treatments at the national level (given the previous text), so to return to the global level at this stage is confusing. I suggest indicating this discursion using language (e.g., beginning with a clause like "Globally, ..." or "In a global context..." or "Concerning global COVID 19 associated restrictions...")

Globally, has been added to the second paragraph in section 2, page 4.

3

Section 2.1.: the information presented in 2.1. is lacking the close numerical/detailed support that is available in sections 2.2. and 2.3.  For a 'data-based commentary', descriptions such as 'almost doubled', 'some', 'has decreased', 'an increased use of', etc., are not informative unless supported with clear and accurate evidence from the sources, because an 'increase' may be by a single unit and 'almost doubled' can be negligible if the original number was a single unit.  Similarly, for subjective descriptors such as 'low' (p.4.) it is especially important to present information demonstrating why (in your opinion, which you want the reader to share) such descriptors are being used, rather than more neutral ones.  I acknowledge that this may be due to the limitations of the evidence you have available (which seems to often be grey literature), in which case these limitations should be reported alongside this summary of 'data'.

Numerical support from the data has been added throughout section 2.1, pages 4 and 5.

The number of Dutch citizens working from home almost doubled, from 37% to 66%, between March 2020 and May/June 2020, with some citizens intending to work from home more often after COVID-19 related restrictions end (ranging from 26%-45%) (de Haas et al., 2020; Koninklijke RAI Vereniging, 2020e; van Hagen & Ton, 2020; VMS Insight, 2020).

Since March 2020, public transport use has decreased, with Translink reporting 41-87% less public transport check-ins in 2020 compared to the same week in the previous year (Taale et al., 2020a; 2020b; Translink, 2020; Bakker et al., 2020; de Haas et al., 2020; Statistics Netherlands, 2020). Dutch citizens have opted for use of private cars (between 41%-60%), bikes (between 14%-57%), and walking (between 17%-38%) instead, with numbers varying based on mode of public transport previously used (de Haas et al., 2020; Kamphuis, 2020; Taale et al., 2020b, 2020a; Translink, 2020). Additionally, a 30% increase in occupancy rate and a 15% increase in number of rides from car-sharing services since May 2020 has been reported (ANP, 2020). Data from June/July, September and November 2020 indicate that Dutch citizens biked and walked longer distances than before the COVID-19 restrictions (de Haas et al., 2020; Taale et al., 2020a; 2020b). Fifty-two percent of citizens reported intentions to continue biking instead of taking public transport once COVID-19 restrictions end (de Haas et al., 2020; Taale et al., 2020b).

4

Page 6: final paragraph of section 2.3.  There should be a clear statement of the time period for this statistic; as this is a single-sentence paragraph, it may also be an opportunity for comment on issues to do with linking EV trends and COVID 19, mentioned above?

Details of the time period to which this statistic refers have been added to section 2.3, page 6:

Sales of Electric Vehicles¹ (Fuel Cell Electric Vehicles and Plug-in Hybrid Electric Vehicles) in 2020 increased by 185.9% and 91.2% respectively compared to 2019  (The Society of Motor Manufacturers & Traders, 2021).

5

Page 8: "Government restrictions on [...] transport."  It would be useful to have factual information on the effectiveness and or extent of these measures. I realise that Appendix 3 gives a useful overview on measures in general and their timing, but adding information on these measures specifically would support this argument about opportunities for public transport.  (If there is not strong evidence on efficacy or information on extent of measures and their consequences, then perhaps qualify these as 'good ideas' rather than as events that were known to provide such opportunities.)

We have added a line into section 4, page 10 to direct refers back to the evidence presented regarding the effectiveness of government restrictions regarding transport use:

Increases in various metrics of active transport during periods of government restrictions on travel, outlined in sections 2.1, 2.2 and 2.3 provide some evidence of the effectiveness of these measures

6

Page 9: "for example with the use of low-traffic neighbourhoods, [...] motivation for using private transport." It would be useful to support this by citing evidence of the efficacy of such measures.

A reference has been added to section 4, page 11 to illustrate evidence for the effectiveness of low-traffic neighbourhoods:

For instance a reduction in car ownership was reported two years after introducing low-traffic neighbourhoods in Outer London (Goodman et al., 2020).

7

Page 10: "Employees working from home reduce their transport-related behaviours."  This may be so in practice, but it does not follow invariably, as commuting is not the only purpose of travel.  A slight re-phrasing (e.g., reduced commuting) would be prudent.

Wording has been changed in section 5.1, page 11 to reflect employees working from home reduce their commuting -related behaviours.

8

Section 5.1.: it would be worth acknowledging that not all work can be done from home, this text applying mostly to 'white collar' work.  This may be increasingly more relevant in the countries focused upon in this article, with ongoing shifts towards service and technology sector employment, but there remains a necessary and substantially subset of people for whom work is location-based or travel-based by necessity.  This may be worth a sentence to acknowledge, perhaps providing statistics about capabilities of reductions (i.e., how many people can work from home), if they are available.

Information related to the proportion of people who have the potential to work from home, outlined in section 3, is reiterated in section 5.1, page 12 and it is acknowledged that working from home is not an option for a large proportion of the workforce:

Within Europe it is estimated that between 24% of 31% of the workforce has the potential to work from home, with pre-COVID levels of working from home estimated to be 5%.  (International Energy Agency, 2020; International Labour Organization, 2020). Therefore, although there remains a large proportion of the workforce across Europe whose work depends on travel, a significant proportion of the workforce may have the potential to work from home.

9

Page 10: "Intentions to use private vehicles more, after restrictions end, were identified, and sales of Electric Vehicles increased in all three countries."  Given that this is a review of evidence, it would do no harm to remind the reader of the previously cited evidence for these points by citing it again.

References added and text changed to reduced ambiguity in section 5.3, page 12:

Intentions to use private vehicles more, after restrictions end, were identified in the Netherlands (de Haas et al., 2020), and sales of Electric Vehicles increased in all three countries (Bil Sweden, 2021; Koninklijke RAI Vereniging, 2019d, 2019b, 2019c, 2019a, 2020a, 2020b, 2020d, 2020c; The Society of Motor Manufacturers & Traders, 2021).

10

Please provide a complete reference for van Hagen, M., & Ton, D. (2020). Corona’s impact on the behavior of train passengers.  This information is insufficient for identifying the source in question.

The reference has been amended:  

van Hagen, M., & Ton, D. (2020). Corona’s impact on the behavior of train passengers [Powerpoint slides]. Dutch Railways. https://english.kimnet.nl/binaries/kimnet-english/documents/presentations/2020/09/28/looking-back-at-the-4th-mpn-symposium/Effecten+corona+NS+TU+MPN+symposium.pdf