# Detecting collective human behaviour in internet traffic volume data

Cyberspace. A consensual hallucination experienced daily by billions of legitimate operators, in every nation, by children being taught mathematical concepts…A graphical representation of data abstracted from the banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding…

William Gibson, Neuromancer, 1984

## Abstract

The internet is omnipresent and now extends into nearly every facet of society. Individuals and organisations interact with the internet in some form throughout the day. In this article, we explore the possibility to characterise large-scale human behaviour and social-economic events which manifest in the form an internet traffic volume footprint visible at various scales.

## Introduction

As part of our ongoing research in the Data Science Campus, we are currently exploring the wider theme of deriving social/economic indicators from alternative and novel data sources for potential use in new and experimental statistical outputs. One project to emerge from this exciting research area aims to explore if it is possible to extract social and economic insights from real time internet traffic volume data.

The internet is omnipresent and now extends into nearly every facet of society. It has become a necessity and the facilitating medium for much of the economy. A (now dated) 2010 study estimated the Internet’s contribution to UK GDP to be as much as 5.6%. This contribution can be broken down into a number of components, including, but not limited to, the internet value chain, which includes online services, enabling technologies, including cloud computing, connectivity and devices. Now, in 2019, this contribution is likely to be significantly higher and complex with contributions from new industries and emerging technologies. As the internet grows and new industries are spawned, it is important to consider new ways to measure it’s effect on the economy and society.

Individuals and organisations interact with the internet in some form throughout the working day. The sum of these interactions can be loosely described as social and economic related activity. This activity may include streaming services, inter-business communication, inter-process communication, financial transactions, through to web browsing and traditional email use, to name but a few. In addition, these services, processes and interactions are heterogeneous, in the sense that they are delivered using different protocols and layers as per the OSI model. While these services vary greatly in the Application layer, they all share one thing in common: the transmission and flow of data in some form.

In light of this shared characteristic, our proposal is to attempt to measure and study data driven economic activity at the Data link layer: by analysing the amount of data flowing through a network at a given time point, we hope to observe a human footprint of social-economic activity.

## Data sources

Due to the distributed nature of the internet and it’s network topology, it is difficult to understand geographic characteristics of network usage. A data packet destined for London from elsewhere in the UK may be routed through any number of physical locations both in and outside of the country. Inter -continental data originating from the US and destined for mainland Europe may in turn be routed via the UK. As such, it is difficult to attribute varying amounts of internet traffic to a specific location or even continent.

While the internet is a distributed network, there exists a physical topology of internet backbone service providers. One specific type of provider, an Internet eXchange Point (IXP) aims to “keep local traffic local” by operating network infrastructure designed to carry internet traffic between local businesses and traffic originating from or destined to a local area from connected peer networks.

There exist 483 active IXP’s globally (see Wikipedia list and PeeringDB) The largest number of IXP’s is in Europe (197), followed by Asia (101), North America (91), Latin America (35), Africa (35), Oceania (12) and the Middle East (12). The largest IXP in Europe, Deutscher Commercial Internet Exchange (DE-CIX), located in Frankfurt, Germany, operates at a peak throughput exceeding 6 Tbps (terabits per second) and connects with more than 700 Internet Service Providers (ISPs).

In the UK, there are 10 active IXP’s (according to Wikipedia) operated by different organisations, serving different cities:

Name City —————– ————————— IX Brighton Brighton LINX Cardiff Cardiff IX Leeds Leeds / Western Yorkshire IX Liverpool Liverpool LINX LON1 London LONAP London LINX LON2 London Net-IX London LINX Manchester Manchester LINX Scotland Scotland

Of these, the largest and most established IXP is operated by the London Internet eXchange (LINX), which in the UK, operates exchanges in IXPs in London, Manchester, Edinburgh and Cardiff.

Focusing on London, the LINX network consists of two IXPs, namely LON1 and LON2 which are similar in terms of physical network topology, however differ in terms of network switch hardware. The two IXPs are distributed over multiple London data centre locations and also interconnected by dark fibre for resilience. The two IXPs differ somewhat in terms of connected peers (694 LON1, 315 LON2), which include Internet Service Providers (ISPs), Content Delivery Networks (CDNs) and other entities. The topology of this network can be explored using PeeringDB, which also offers a JSON API. Using this service, the top 20 connected peers to the LON1 (left) and LON2 (right) exchanges by global IXP connectivity are:

Organisation Traffic levels Organisation Traffic levels ————————— ————– ————————— ———– Hurricane Electric 10 Tbps+ Facebook Inc 1 Tbps+

Cloudflare Microsoft

Akamai Technologies 10 Tbps+ Amazon.com

Packet Clearing House 1-5Gbps Limelight Networks Global 1 Tbps+

Google LLC Verizon Digital Media 10 Tbps+ Services (EdgeCast
Networks)

Microsoft Valve Corporation 1 Tbps+

Packet Clearing House AS42 1-5Gbps SoftLayer Technologies, 1 Tbps+ Inc. (an IBM Company)

Yahoo! Riot Games 100-200 Gbps

Amazon.com OVH 1 Tbps+

Netflix 10 Tbps+ G-Core Labs S.A. 1 Tbps+

Fastly, Inc. 1 Tbps+ Forcepoint Cloud

Apple Inc. IPTP Networks 1 Tbps+

Twitch SG.GS 10-20Gbps

Netnod 100-1000Mbps IX Reach 300-500 Gbps

Limelight Networks Global 1 Tbps+ Dropbox

ISC Blizzard Entertainment

Verizon Digital Media 10 Tbps+ Colt 500-1000 Services (EdgeCast Gbps Networks)

Valve Corporation 1 Tbps+ Anexia
———————————————————————————-

Other notable organisations include British Broadcasting Corporation (BBC), British Telecom (BT), Her Majesties Revenue and Customs (HMRC), Janet (UK research network), Sony (Playstation), TalkTalk, Verizon, Vodafone, Virgin media, VISA (international), booking.com, eBay, and Sky broadband.

Most IXPs provide network throughput statistics and historical data showing the amount of bandwidth measured in mega, giga and tera -bits per second. For example, the Amsterdam Internet eXchange (AMS-IX), Moscow Internet eXchange (MSK-IX) and London Internet eXchange (LINX) all provide network traffic statistics at 5 minute intervals. Given it’s size and points of presence in some major UK cities, we have so far chosen to explore the network use statistics belonging to LINX.

## Data characteristics

To date, we have explored data available on the LINX network statistics portal. This data, consists of average IXP network throughput for a given time period measured in bits per second. The highest resolution data available is in 5 minute buckets and extends back as far as June

1. At daily frequency, the time-series data for the LON2 (London) IXP extends back to February 2011.

### Low frequency characteristics

The overall trend is inline with the growth of the internet and can be explained by increased bandwidth, network capacity, size and number of IXP connected peers, increased internet use and the rise of streaming and data intensive services. According to a recent ONS publication, as of 2018, 90% of adults in the UK were recent internet users, which varies between age groups. Nearly all (99%) of adults aged 16-34 were reported as recent internet users.

On a monthly scale, the data indicates increased internet traffic during the winter months and decreased traffic during the summer.

In the above figure, (de-trended) monthly internet traffic since 2011 for a London Internet eXchange Point has been averaged which indicates a yearly parabolic pattern of use. Interestingly, this observation is the reverse of trends in UK road traffic reported in the DfT road traffic estimates, which show an increase in the number of cars parsing a point during summer months, and a decrease during winter months.

If we consider purely leisure related internet use, the increase in traffic during winter months in this data may be due to reduced outdoor activity which may in turn be influenced by reduced daylight hours. If we consider “work” related internet use, the summer drop in traffic may also be further reduced by summer vacations and perhaps the aggregation of longer lunch time breaks during spells of good weather. In general, this month-by-month pattern is interesting in it’s own right, as it may be representative of general human seasonal activity.

Looking at daily internet traffic, many interesting patterns emerge. The above figure shows daily (London) traffic levels from the IXP data for 2016 and 2017 respectively. Each row corresponds to the day of week and each column corresponds to one of the 52 weeks in a year. Each cell represents the average internet use for each day in each week for the respective year - with bright cells representing higher internet use.

From this visualisation it is clear that weekdays are different from weekends and that internet traffic tends to be lower in the summer months. It is also clear that certain days exhibit significantly less traffic volume.

The visualisation above shows 2 years of internet traffic data for the London IXP data. The vertical axis shows the relative intensity of traffic throughout the day, starting at 12am (bottom of the chart), through to 11:59pm (top of chart). Each point on the horizontal axis corresponds to one of the of 730 days in a period covering 2016 to 2017. The dark band at the bottom of the visualisation corresponds to the morning hours (2am - 7am), while the thin vertical dark bands correspond to lower internet traffic during the weekends.

If we zoom in to April - May 2017, shown above, three bank holidays are visible.

1. Friday 14 April (Good Friday) - 17 April (Easter Monday)
2. Monday 1 May (Early May bank holiday)
3. Monday 29 May (Spring bank holiday)

While not surprising, the fact that the internet traffic throughput in this data varies according to work and non-work-days is an indication that the data may be useful for measuring working patterns and anomalous events.

### High frequency characteristics

During the average week, weekends tend to have lower traffic with mid-week days exhibiting the highest amounts of traffic.

Perhaps one of the most interesting features of the data are the differences between the inter-day average internet usage for each hour of each day of the week. The below graph shows the average hourly London IXP usage (in Gbps):

The weekdays follow a similar pattern, with each low point occurring at approximately 4.30am, followed by a sharp rise toward 10am. From 10am, traffic rises at a slower rate toward approximately 4.30pm. It then drops off until around 6pm before rising again to a daily peak at 9pm.

An interesting feature is the difference between Fridays and the rest of the working week: Friday internet traffic tends to drop sooner, at a faster rate, and also tends to be lower during the 6pm low point. We speculate that this may be due to a tendency for people to finish work earlier on Friday and to be more likely engaging in non-internet related leisure during the evening.

For all weekdays, there exists a temporary drop in internet use which seems to reach a minima at approximately 6pm each day. Overall, this is one of the most notable characteristics and could be due to a number of reasons which we will discuss shortly.

The average weekend usage is distinct from the weekday pattern. Besides being somewhat lower, Saturday and Sunday differ due to the missing 6pm dip. We speculate that this phenomena is due to a change in commuting activity during the weekend period: the 6pm dip during the weekday may be due to large-scale post work commuting behavior, where bandwidth consumption (and generation) may change due to absence of use (e.g., when driving a vehicle), be reduced while using mobile data, or change to an ISP which is not connected to the IXP.

In addition, Saturday and Sunday converge at approximately 1pm, at which point, Sunday’s (dotted line) usage tends to approach weekday evening peak use, while Saturday evening forms the lowest evening use in comparison to the rest of the week. Similar to Friday evening, it may be that the lower usage on a Saturday evening is due to increased (non-internet) leisure activity which defaults to the inter-week evening pattern on Sunday due to the next day being a work-day.

In addition to differences in the average daily traffic volume, there are also differences in the timing and extent of extremes during different time periods. The above graph shows the daily difference for traffic volume (left column) and low/high point time (right column) for the early morning low point (first row), late afternoon high point (second row), and finally, the evening peak (third row).

Starting with the early morning low point shown in the first row, we can see that, on average, Monday morning at 4.45am has the lowest internet use in our London sample data. As we move to mid-week, the early morning traffic volume increases, peaking on Wednesday before gradually declining toward the weekend. Interestingly, the time at which this low point occurs is symmetrically earliest mid-week (4am) and latest at the beginning and end of the week, where traffic is lowest at 4.45am on average.

The next notable feature is the extent and timing of the late afternoon peak which occurs just before the 6pm temporary drop in traffic each day. On average, the observed volume at this point is highest mid-week, inline with the overall average daily traffic and starting from Tuesday, occurs earlier each day toward the weekend. We speculate that this point delimits the end of the average working day and beginning of the daily commuting period.

Finally, the highest traffic levels can be observed each day at 9pm, with the exception of Sunday, where this peak time occurs one hour earlier on average. While the overall average daily internet traffic is highest on Wednesday, the evening volume is highest on Tuesdays.

## Internet time use

In this section we explore the possibility that this data may be used to identify large-scale internet usage behaviour. Specifically, we examine the relationship between the data and results from a time-use survey and then explore the possibility that this data may be used to measure various aspects of day-to-day work and leisure phases.

### 2014-2015 time use survey

The Centre for Time Use Research 2014-2015 time use survey, was a UK-wide survey which aimed to create a dataset describing how people spend their time throughout the day on different days of the week. The survey was in the form of a user-kept diary which detailed specific activities for intervals of time.

The left-hand side graph above shows the average amount of internet traffic for all 5 minute time points in a week for a London IXP (lon2), with the mean daily value shown by the orange dashed line. The inter-day variation could be explained by a number of factors. However, it is interesting to note that this variation is also present in the reported working hours in the time-use survey, as shown on the right-hand side of the graph.

In the time-use survey, 3,523 participants logged their work status over a 7 day period in 15 minute intervals. By aggregating this data by day of week, we can see that Wednesday is the busiest day of the week, with Monday and Friday being significantly lower. Sunday is the least busiest day (as expected). Comparing with the internet traffic shown on the left, Wednesday is the busiest, with lower internet use at the weekends. This observation points in the direction that the internet use data may be influenced by economic activity (in this case, worked hours), and may also be of interest as an alternative (high-frequency) complimentary way to measure (internet related) time use.

In addition to work time use, the time-use survey also includes a wide range of other activities. The above graph compares average daily internet traffic (dashed line) with number of people reporting to be commuting or eating for each point in the day. Commuting leads internet traffic use in the early hours of the morning (as expected). At 9am, as reported commuting activity declines the rate of change in internet traffic slows, and by 11am, both internet traffic and commuting activity begin to climb toward the 4.30pm late afternoon peak. Commuting activity proceeded by eating activity seems to account for the 6pm temporary low point.

The time use survey contains an option to indicate use of a device (in combination) with other activities. The definition of a device includes use of a computer and as such, this reported activity appears to be highly similar to the average internet use. In the above figure, we overlay the average number of people reporting to be working at each point in the day along with reported device use. Note that the 2014-2015 survey is from a different time period with respect to our data which extends into 2018, although it is interesting to note the similarity between the two series. The time and rate of change in internet use appears to coincide with working and device use during the morning hours. In addition, the 6pm drop and 9pm peak are inline with reported device use.

### Phase detection

As described previously, the average internet-day (in this data) can be divided up into various points in time and these points in time tend to vary, on-average, throughout the week.

Specifically, these points correspond to the peaks and troughs which occur on a daily basis as shown in the above graph.

In order to detect these points, it is first necessary to smooth the data. To do this, we fit a spline to each 24 hour period (it is also possible to use LOESS regression) and then attempt to identify the 4 sequential high/low points in each 24 hour period.

Having smoothed each day, we identify a change-point as the point at which the sign of the first difference changes. In combination with smoothing, this method is reliable in consistently identifying the 4 points of interest.

As noted previously, the time at which these points occur varies through time as shown in the above animation. In addition to varying throughout the week, interestingly the change-points also vary on average throughout the year.

The figure above shows how the detected change-points vary on average through the year. The time at which the morning low (A) occurs is earliest in November where it tends to occur at 04:05, steadily rising to 04:35 by August the following year. The point which we believe to mark the end of the working day (B) is one of the most interesting. This point varies by approximately 1 hour throughout the year, being at its latest in the winter months (16:40), falling to its earliest point by August (15:35). The third point (C) marks the temporary low which occurs after the end of the work day. We speculate that this point is related to commuting behaviour since it only occurs in weekdays. The final point (D), marks the so called internet rush hour which varies depending on the time-zone. Interestingly, this point occurs latest in the summer months, peaking at 9:05pm in August and occurs as early as 20:15 in the winter.

Based on change-points A,B and D, we have derived 2 indicators which measure the length of the “internet working day” (work phase) and length of commuting period (commute phase) which we define as the difference in time between points (A,B) and (B,D) respectively. The average monthly difference for each of the indicators is shown in the above plots. Both exhibit clear monthly seasonality, with the length of the internet working day being shortest in the summer and the length of the post work commute period being shortest in the winter months. We speculate that this monthly seasonality is due to the availability of daylight hours and road traffic conditions: road traffic tends to be higher in the summer, which may account for the length of the commute period.

## Detecting anomalies and large-scale events

Using Singular Spectrum Analysis, we separate time-series into additive trend and periodic components which when added together, form a model $m$ of the time series $t$. The residuals, or noise, can then be defined as $t - m$ - the unexplained/anomalous part of the time series not captured by the model. These residuals can then be used to identify anomalies in the time series which may correspond to large scale events. Here, we show how the extent of these residuals relate to significant events.

In the first example, we have been able to identify the effects of a significant weather event in the internet traffic data. At the beginning of 2018, there was a prolonged period of cold dubbed Beast from the East. During this period of time, a winter storm named [Storm Emma](https://en.wikipedia.org/wiki/Storm_Emma_(2018) produced a significant amount of snowfall.

The figure above shows 2 days (A) and (B) where heavy snow fell on London. The result on the internet traffic data for the London IXP was a substantial increase in traffic which far exceeded the predicted/expected level.

The above plot shows the resulting model residuals for the same period. The unexpected internet traffic on the second day of snowfall was at one point nearly 100 Gigabits per second (~ +20%) higher than anticipated. This increase in traffic could be due to an increase in people working remotely during the heavy snow and may also be due to people checking traffic updates or streaming video content.

In order to understand the significance of the event, we obtain the quantiles for each residual with respect to all residuals in our 2016-2018 dataset. In this example, the residuals are significantly higher (99 - 99.9 percentile) than all other observations.

We next attempted to identify a significant retail event (Cyber Monday). In the above plot we show the most recent event from 2018 in which a spike in activity, above the 99% threshold occurs on Cyber Monday.

Although there is indeed a high residual on this day, it should be noted that it is short lived and also resides within a range of other (significant) residuals during the same period. As such, it is difficult to assign any (speculative) meaning to this observation. Furthermore, we have been unable to identify such an effect in the previous 2 years.

In the next example, we looked to identify a significant sporting event in the data. It is well known that football world cup games, given their large-scale audience, can impact internet traffic data. The above plot shows the extent of model residuals during the England vs Croatia men’s football world cup semi-final.

In this case, the event had a significant negative impact on traffic throughout the game. What is most interesting is that it possible to observe certain characteristics of the event. Specifically, the half time period and subsequent break in extra-time periods are clearly visible as temporary increases in traffic. In other games (not shown here), an opposite effect occurs in which traffic is substantially higher during the game and drops-off during half-time. This is likely due to the type of broadcast media: some events may not be streamed.

By far the most significant (sustained) exceptional events are bank holidays which have a -7.8% negative impact on average. In the above 2 graphs, the effect of the Christmas and Easter holidays are clearly visible in the form of a substantial decrease in traffic over each respective period. The Christmas period is particularly interesting as the effect covers a long period of time. On average, boxing day tends to have the most significant impact, with a -16% drop in traffic levels on average.

The extent of bank holiday impact appears to depend on the bank holiday as shown in the above graph. This resulting observations are of interest since the average residual during each bank holiday period could be indicative of work force participation.

The previous examples show that various forms of large-scale events can be observed in the internet traffic data. This is of particular interest, since events of this scale may have some form of economic impact and measuring the extent over time may be of use as a potential proxy for the scale of impact. In addition to the events highlighted above, we have also been able to identify significant news events and even a significant decrease in traffic on polling days.

While we have highlighted a handful of events above, traffic anomalies with respect to our model residual occur frequently. The above plot demonstrates the presence of traffic anomalies throughout 2018. In the top row, we can see days in lighter colours in which at some point the traffic was significantly lower than anticipated, whereas the second row shows days in lighter colours where the traffic was significantly higher. In the final row, we show days where the internet traffic was consistently significant through each day: brighter points indicate days where the traffic was above or below the 99% significance level for a long period of time.

## Relationship with economic activity

The production and use of data is a fundamental element of economic activity, in parallel to the production and consumption of goods and services. This idea leads naturally to focusing directly on measuring data generation, flows, use and storage as routes into understanding digitally-based economic activity. – Professor Sir Charles Bean. Independent review of UK economic statistics. Section 3.23.

We are currently exploring the relationship between this data and existing economic indicators. We have so far studied the relationship between the IXP internet traffic use and monthly Gross Value Added (GVA) and have found some promising initial findings which suggest that the data may be correlated with specific sectors of the economy during specific time periods. This is a challenging direction of research since we might expect little relationship with components of GDP given that GDP does not measure certain aspects of the data/internet driven economy. Also we are unable to split the component of internet traffic related to work as opposed to leisure.

We are also exploring the potential for various derivative indicators. For example, we noted before the difference in Friday evening internet activity compared with the rest of the week. It may be possible to use the extent of this difference as a proxy for evening (non internet related) social activity which may in turn be used in the context of understanding the night time economy.

## Relationship with road traffic data

In addition to exploring the relationship with economic activity, we are also exploring an idea that the IXP internet traffic data could be used to indirectly measure road traffic, and more generally, commuting behavior.

This idea is based on the observation that the 4-6pm drop in internet traffic coincides with the commuting period. If we can measure the depth, breadth and timing of this temporary drop in internet traffic, we expect to find a relationship with average road traffic speeds and public transport use.

The above figure shows the relationship with UK-wide average weekday road traffic reported as an index of average car miles in the Department for Transport’s Road traffic estimates compared with (London) IXP internet traffic data. The reported road traffic data are inline with the results from the time-use survey and furthermore exhibit similar characteristics to the internet traffic data. On Friday’s in particular, there is overall increase in road traffic data coupled with a decrease in internet use data.

To date we have explored road traffic count data from the M25 motorway surrounding London. We have found that the monthly seasonality of the data exhibits a negative relationship: Internet traffic tends to be lower in the summer months whilst road traffic tends to be higher. We are currently investigating the relationship between numerous time-series features extracted from both datasets at intraday frequency.

## Conclusion

We have described an interesting data source which we are using as a step in the direction to explore scalable methods for measuring data-driven social-economic activity. The purpose of this post has been to discuss some of our observations and initial ideas and to describe the direction of our current research in this exciting area. We have touched on the possibility to use this type of data as a means to estimate components of time-use surveys, specifically, we have derived a set of internet-day length indicators which vary depending on the day of week and throughout the year. We then demonstrated how large-scale social events manifest in this data in the form of anomalous data consumption, we believe this has the potential to yield insights into large-scale events such as adverse weather conditions and sporting events.

In addition to time-use and large-scale events, we are currently investigating the relationship between internet data consumption and various established economic indicators. As a novel line of investigation, we believe there to be a relationship between the post 4pm dip in internet traffic and peak time road traffic for the geographical area around an IXP. The implication of such a relationship would be a scalable proxy measure for city commuting behaviour which binds the virtual and physical world. Such a proxy measure could be applied globally. We intend to produce a follow up post covering these two exiting avenues of investigation in the near future.

### By Phil Stubbings

Lead Data Scientist at ONS Data Science Campus. Contact: philip.stubbings AT ons.gov.uk

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