Can traffic data help us find a way out of the lockdown?

Daniel Mescheder
Fri May 08 2020
Moving world

Can traffic data help us find a way out of the lockdown?

Daniel Mescheder
Expert Software Engineer Maps
Fri May 08 20208 min read
For how long can you keep doing something before it gets old? This is the exercise humanity has been practicing in the past few weeks and months, as many of us continue to stay home in order to flatten the curve. And yet, as movement and economies slow down, the search for a way out is intensifying.
The question “Are we there yet?” has suddenly taken on another meaning as governments, scientists, businesses and citizens across the world are all looking for what the right answer might be.



When can we reopen society?

The restriction of movement strains countries across the globe, with significant economic impact and restrictions of fundamental civil liberties. How can governments restart economic and cultural life without risking the collapse of the healthcare system that we have largely managed to avoid so far? How will we know how much reopening of society is too much?

With so many variables to consider, I wanted to explore if traffic data can be a piece in the deconfinement puzzle.



The deconfinement challenge

In a press conference on April 15, German chancellor Angela Merkel explained the challenge of reopening society: a small change in the reproduction rate of the virus can have a significant impact on the ability of a society’s healthcare system to affront the situation.

On the other hand, the desire for a gradual revival of economic life has intensified around the globe. The lingering fear is that the negative consequences of an extended pause in economic activity can aggravate an already difficult situation.

While some are still in their infancy, solutions already exist that support deconfinement strategies. If developed to respect the privacy of their users, tracking apps could be an effective way to help break the infection chain.

Regardless of the solution of choice, with infection rate being an extremely lagging indicator, governments still need a reliable way to estimate the effects of their policy making. When border closures are lifted, will people react in a way that puts public health at risk? When shops reopen, will there be an insurgence of people in shopping areas? If governments only use infection numbers as the basis for revising and adapting their deconfinement policies, they will likely be too slow to react.



Using traffic data to inform and adapt deconfinement measures

At TomTom, we’re no epidemiologists. We leave it to the experts to determine the best way forward. We are, however, location technology experts and a market leader in traffic information. Data from 600 million connected devices around the world allows us to see traffic patterns which, if used by policy makers, can be a powerful source of information for the reopening of society.

TomTom’s traffic data can show the effects that lockdown measures have had across the world. This series of interactive reports lets you explore how COVID-19 measures have impacted mobility in countries such as Belgium, Italy, the Netherlands, the UK and states such as California.
Explore how COVID-19 measures have impacted mobility in some areas of the world

Click on the links below to see our full reports.

Can traffic be a measure of the degree to which a confinement strategy has been implemented? If the answer is yes, traffic data could close the loop for policy makers, allowing them to track the effects of policy decisions and adjust if necessary.

Let’s look at the traffic reduction in four different countries together with the number of new infections:
Residual mobility in the Netherlands compared to weekly new COVID-19 infections.
Residual mobility in the Netherlands compared to weekly new COVID-19 infections.
Residual mobility in Italy compared to weekly new COVID-19 infections.
Residual mobility in Italy compared to weekly new COVID-19 infections.
Residual mobility in the United Kingdom compared to weekly new COVID-19 infections.
Residual mobility in the United Kingdom compared to weekly new COVID-19 infections.
Residual mobility in Belgium compared to weekly new COVID-19 infections.
Residual mobility in Belgium compared to weekly new COVID-19 infections.
For all four countries, the peak of the infection curve trails the first significant drop in mobility by two to three weeks. Italy has seen more cases earlier than other European countries and has thus reacted sooner and with more restrictive measures. This is seen in the traffic data, which shows a drop to under 20% of January's mobility levels. The turnaround in the number of new infections is sharper than it can be observed in the other three countries.

To establish the level of correlation, rigorous statistical testing is required. Traffic could be expected to behave as an indirect metric. The confinement results in less potential for infections because people have less contact with each other at work, shown indirectly by traffic data indicating a reduction in commutes.

Combined with other data sources, such as surveys – and while not providing the full picture – traffic data can be one source of information for establishing how deconfinement measures are being implemented. This data is timely, available both on a local and global scale and designed to be anonymous.

Let’s take a closer look.



Traffic data is timely

One of the advantages of traffic data is that the effect of a decision can directly be seen in the data. Getting a new datapoint does not require running a survey. It does not have an incubation time and all the necessary sensors to pick up the information are already in place.

An example of the timeliness of this data can be found in the TomTom Traffic Index, which shows real-time congestion levels in major cities around the world.



Traffic data is local

Traffic data allows us to study the effects of restrictive measures by city, province or region.

This can be clearly seen when studying the example of Italy during the early days of the COVID-19 response. Between March 7 and March 13, traffic between the northern cities most affected by the virus reduced significantly compared to other connections through the country.
Remaining mobility in Italy between March 7, 2020 and March 13, 2020 as a percentage of January traffic.
Remaining mobility in Italy between March 7, 2020 and March 13, 2020 as a percentage of January traffic.
Histogram of remaining mobility in Italy between March 7, 2020 and March 13, 2020 as a percentage of January traffic.
Histogram of remaining mobility in Italy between March 7, 2020 and March 13, 2020 as a percentage of January traffic.
Explore how COVID-19 measures have impacted mobility in Italy
Another example of this is our analysis of mobility in the state of California.
Remaining mobility in California between March 14, 2020 and March 20, 2020 as a percentage of January traffic.
Remaining mobility in California between March 14, 2020 and March 20, 2020 as a percentage of January traffic.
Histogram of remaining mobility in California between March 14, 2020 and March 20, 2020 as a percentage of January traffic.
Histogram of remaining mobility in California between March 14, 2020 and March 20, 2020 as a percentage of January traffic.
Before the state-wide stay-at-home order of March 19, counties implemented different individual responses leading to clearly visible differences across regions.

If a part of a deconfinement strategy were to open some areas earlier than others, the local nature of this data could allow decision-makers to effectively track the effects on a regional level and to react quickly on spillovers to other regions.
Explore how COVID-19 measures have impacted mobility in California



Traffic data is global

While the effects on traffic can be studied at a regional level, traffic data is also available around the globe and lets us study behavior across borders. Aggregation and standardization are already being considered, making it easy to compare traffic phenomena across borders.

An example of this can be seen in the mobility analysis of Belgium.
Remaining mobility in Belgium between April 4, 2020 and April 10, 2020 as a percentage of January traffic.
Remaining mobility in Belgium between April 4, 2020 and April 10, 2020 as a percentage of January traffic.
In the week leading up to the Easter weekend, there is a clearly visible difference between mobility in Dutch and German border cities. These cities show slightly higher residual mobility, while French border cities show slightly lower residual mobility than the Belgian cities in the sample. Nevertheless, cross border traffic reduced in line with the overall drop of mobility inside Belgium.

In Europe, many countries imposed temporary travel restrictions. However, open borders are an important part of the European project. Traffic data makes it possible to track the amount of cross border mobility in order to carefully lift the existent travel restrictions.
Explore how COVID-19 measures have impacted mobility in Belgium



Traffic data is anonymous

The considerations of the role of data and technology in the fight against COVID-19 have raised important questions around privacy. Having taken a privacy-by-design approach to create our products and services, the aggregation level of TomTom’s traffic data makes it impossible to identify individual drivers, while delivering crisp insights into movement patterns and behavior. This is why many cities and governments across the world are already using TomTom traffic data for their planning needs.



Can we help?

To me, traffic data can be a useful source of insight for anyone who is faced with making mobility decisions in these unusual times. As traffic patterns shift and travel restrictions come and go, these changes are reflected in our dataset, painting a clear picture of mobility.
Track the coronavirus through traffic

If you want to use traffic data to help you assess the impact of the coronavirus and explore ways to execute a deconfinement strategy, get in touch.

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