# Using aircraft location data to estimate current economic activity

## Abstract

Aviation is a key sector of the economy, contributing at least 3% to gross domestic product (GDP) in the UK and the US. Currently, airline performance statistics are published with a three month delay. However, aircraft now broadcast their location in real-time using the Automated Dependent Surveillance Broadcast system (ADS-B). In this paper, we analyse a global dataset of flights since July 2016. We first show that it is possible to accurately estimate airline flight volumes using ADS-B data, which is available immediately. Next, we demonstrate that real-time knowledge of flight volumes can be a leading indicator for aviation’s direct contribution to GDP in both the UK and the US. Using ADS-B data could therefore help move us towards real-time estimates of GDP, which would equip policymakers with the information to respond to shocks more quickly.

## Introduction

Most economic statistics, such as GDP, are released with a significant delay. Estimating their current values before they are published is known as “nowcasting”1. Previous studies have used real-time internet data, from sources such as Google2,3,4,5,6, Twitter7,8 and Wikipedia9,10, to nowcast economic data. Better nowcasts are highly valuable, as major economic policy tools such as interest rates can take up to 20 months to fully impact the economy11. When faced with shocks like the 2008 financial crisis, policymakers must therefore respond as quickly as possible, which requires accurate knowledge of the current state of the economy. Failure to do so has deepened past recessions12, leading to political instability across Europe following both the Great Depression and the 2008 financial crisis.

Aviation is a key economic sector, contributing at least 3% to GDP in the UK and the US13,14. Aircraft now broadcast their location, among other data, in real-time using the Automated Dependent Surveillance Broadcast (ADS-B) system. So far, ADS-B data has rarely been used outside its intended application in the aviation sector. An exception is one study that showed that ADS-B data could track corporate jets, therefore providing a leading indicator for certain mergers between firms15. The current paper analyses whether ADS-B data can help nowcast airline performance and aviation’s direct contribution to GDP. In contrast to real-time ADS-B data, the current statistics for these variables are published with a three month delay because they require surveys of businesses16,17,18,19. Faster statistics could help policymakers respond more quickly to future economic shocks, thereby limiting their damage.

## Methods

We retrieve aircraft data from the ADS-B Exchange20. Commercial aircraft in Europe have been required to broadcast ADS-B data since 201721, and it has been mandatory for US aircraft since January 202022. These broadcasts include the aircraft’s speed and location, specified as their altitude, latitude and longitude alongside a timestamp. Each ADS-B message also includes a six-digit hex identification code assigned to the aircraft by the International Civil Aviation Organisation (ICAO). Amongst other things, the ICAO code makes it possible to link an aircraft to its operating airline by looking up the ICAO code in a corresponding database. This pre-processing is carried out by ADS-B Exchange and the operating airline is included in each of the resulting ADS-B records.

ADS-B messages are unencrypted, in order to be receivable by other aircraft, which means they are available to anyone with an ADS-B receiver. The ADS-B Exchange collects data from thousands of receivers20. The resulting database covers global flight activity (Fig. 1). We analyse the period from July 2016 to December 2018. We note that coverage has improved over time, as the number of receivers feeding the database has grown (see Supplementary Fig. S1).

The raw data we analyse contains roughly 25 billion messages. First, we reduce this data from one row for each message to one row for each flight. Figure 2 shows how we identify take-offs and landings by analysing the altitude of an aircraft over time (see also Supplementary Figs. S2 to S6). Applying this method, we extract 67 million separate flights and record their take-off and landing time as well as corresponding locations. Next, we aggregate these flights by month and airline to generate estimates for published airline statistics. This further reduces our dataset to 303 monthly airline flight counts for the UK, and 405 for the US. We include the largest 13 UK and 15 US airlines, with the cut-off for airline inclusion set at 1% of total air traffic in each country. Overall, our ADS-B data captures 83% of UK flights and 41% of US flights since July 2016.

### Published aviation statistics

Both the UK16 and US18 aviation authorities publish monthly airline statistics. They contain a range of performance indicators, such as flight volume and capacity utilisation, but are currently released with a three month delay. Figure 3 depicts the monthly percentage change in both the airline statistics and in flight volumes calculated using the ADS-B data. Visual inspection suggests there is a strong correlation. However, there is clear seasonality for both countries (see Supplementary Figs. S7 and S8). We account for this seasonality in our later analysis.

Finally, we collect economic data from the UK Office for National Statistics (ONS)17 and US Bureau of Economic Analysis (BEA)19. Both the ONS and BEA publish a GDP series that is split by industry, from which we consider air transport. The UK series is a monthly time series dating back to 1997, and the US series is a quarterly time series dating back to 2005. When analysing these series, we consider key time series properties, such as stationarity, to avoid drawing misleading conclusions (see Supplementary Figs. S9 and S10).

## Results

### Estimating airline flight volume

For each airline, we aim to generate rapid estimates of flight volume across time. Some airlines are much larger than others. To ensure comparability between airlines, we therefore normalise the flight volume data by indexing the first period to each airline for 100. We then re-scale subsequent periods so they are measured relative to the first. An airline whose original flight counts were (5000, 6000, … 6500) would be normalised to (100, 120, … 130). A normalised flight volume of 120 reflects a flight volume 20% higher than the first period.

A reasonable baseline model would be an autoregressive (AR) model where we estimate normalised airline flight volumes with their own history:

$${y}_{i,t}={\alpha }_{i}+{\gamma }_{t}+\beta {y}_{i,t-3}+{\varepsilon }_{i,t},$$
(1)

where $${y}_{i,t}$$ is the number of flights and $${\varepsilon }_{i,t}$$ is a noise term for airline i in month t. Due to the three month publication lag for the official flight volume statistics, when nowcasting the flight volume for month t we only have official data from month t−3. The baseline therefore includes an AR(3) term, $${y}_{i,t-3}$$ and $$\beta$$ is the weight on the AR(3) term.

We also derive binary (“dummy”) variables from the longitudinal data structure. $${\gamma }_{t}$$ are coefficients for dummy variables for each month (12 in total), which proxy for seasonality. A positive value for $${\gamma }_{t}$$ would reflect that flight volumes are usually higher than average in month t. $${\alpha }_{i}$$ are coefficients for 28 airline-specific dummy variables, which capture the airline’s average growth over time. A positive value of $${\alpha }_{i}$$ would reflect an increase in the mean flight volume for airline i across the time period.

To measure the performance boost from ADS-B data, we add this data to the baseline model. Denoting $${x}_{i,t}$$ as the ADS-B flight count for airline i in period t, and δ as the weight on the ADS-B term:

$${y}_{i,t}={\alpha }_{i}+{\gamma }_{t}+\delta {x}_{i,t}+\beta {y}_{i,t-3}+{\varepsilon }_{i,t}$$
(2)

Table 1 shows that adding ADS-B data boosts the in-sample accuracy of all baseline models, regardless of whether month and airline dummies are included (see Supplementary Table S1 for similar results when the model includes month dummies or airline dummies alone). This shows that ADS-B data can help estimate dynamic airline-specific changes in flight volume, and does not only proxy seasonality or differences in airline growth.

Our results so far suggest that ADS-B improves in-sample estimates of airline flight volume. However, in-sample scores may overstate true predictive accuracy. To assess out-of-sample performance, we use one-step-ahead adaptive nowcasting23, originally developed to help generate rapid indicators of flu incidence using Google search volume. For each month t in our dataset, we train the model with data from months $$\in \mathrm{[1,}t-\mathrm{1]}$$. We penalise the model’s coefficients, to avoid overfitting, using LASSO regression with 5-fold cross-validation (see Supplementary Methods for regularising out-of-sample forecasts). The penalised model then estimates the test month t. We record the estimate for each airline, and use the mean absolute error (MAE) as the score for month t. As we move through the analysis period, thereby increasing t, we re-fit the model to add new training data (hence “adaptive”). This procedure only uses past data to predict the present, so we know performance is tested out-of-sample.

For both the UK and US, we construct a baseline adaptive nowcasting model which produces estimates for each airline. The baseline adaptive nowcasting model contains airline dummies (see Supplementary Table S2 for model performance with different dummy configurations). The ADS-B model adds ADS-B data to the baseline adaptive nowcasting model.

Figure 4 depicts our adaptive nowcasting results. Adding ADS-B data reduces the MAE by 29% for the UK (baseline MAE = 17.3, ADS-B MAE = 12.2) and 18% for the US (baseline MAE = 7.4, ADS-B MAE = 6.1). We also show the distribution of errors for each model over time in Supplementary Fig. S11. These results hold across a range of dummy specifications and training windows (see Supplementary Tables S2 and S3). Therefore ADS-B data also improves out-of-sample estimates of airline flight volume.

#### Estimating economic activity

We next analyse whether ADS-B data may help estimate aviation’s direct contribution to GDP. Both the UK and US aviation GDP series are non-stationary based on Augmented Dickey-Fuller tests (UK: Dickey-Fuller = −1.4; US: Dickey-Fuller = −2.9; both ps > 0.05). Therefore their distributions are not constant over time, so we cannot use them for regression. Instead, we use the rolling annual percentage change in GDP, which deals effectively with both non-stationarity and seasonality (see Supplementary Figs. S7 to S10).

Our baseline specification for the annual percentage change in aviation’s direct contribution to GDP $$\Delta {z}_{t}$$ is

$$\Delta {z}_{t}=\alpha +\beta \Delta {z}_{t-j}+{\varepsilon }_{t},$$
(3)

where $${\varepsilon }_{t}$$ is a noise term. There is a two month publication lag for the first complete estimate of UK GDP, and a one quarter lag for the US. Therefore $$\Delta {z}_{t-j}$$ is the most recent value known at month t, where $$j=2$$ for the UK and $$j=1$$ for the US. The augmented model includes the rolling annual percentage change in ADS-B flight volume $$\Delta {x}_{t}$$:

$$\Delta {z}_{t}=\alpha +\beta \Delta {z}_{t-j}+\gamma \Delta {x}_{t}+{\varepsilon }_{t}$$
(4)

The in-sample results are promising: adding ADS-B data boosts adjusted $${R}^{2}$$ from 31% to 55% for the UK and 12% to 42% for the US. However, there are only 18 monthly periods for the UK and 6 quarterly periods for the US due to the limited time series of ADS-B data. These sample sizes are clearly too small to assess out-of-sample performance with an adaptive nowcasting model.

We previously showed that ADS-B data could help estimate official flight volumes. To obtain greater insight into whether ADS-B data can improve nowcasts of aviation’s direct contribution to GDP, we therefore substitute the official airline flight volume series in place of the ADS-B data. The official airline series are available for the full period for which we have aviation GDP data for both the UK (from 1997) and the US (from 2005). We again use adaptive nowcasting, but with fixed training window lengths of 60 months for the UK and 8 quarters for the US (see Supplementary Table S6 for results using other training window lengths).

Figure 5 depicts out-of-sample results for GDP estimation. Adding real-time flight volume data reduces the MAE by 7% for the UK (baseline MAE = 4.54, ADS-B MAE = 4.22) and 30% for the US (baseline MAE = 4.65, ADS-B MAE = 3.25). The improvement is greatest during the 2008–2010 financial crisis and the 2012 Euro crisis (see Supplementary Table S5). This may be because the baseline AR model performs worse during volatile economic times.

## Discussion

We have assessed whether ADS-B data can help nowcast aviation statistics, which are currently published with a three month delay. We first show that ADS-B data can accurately estimate airline performance, as measured by their flight volume. Second, we show that real-time knowledge of flight volume is a leading indicator for aviation’s direct contribution to GDP. We find that this indicator is of greatest value during volatile periods, such as the crises between 2008 and 2012. Crisis periods are when rapid estimates for GDP are most crucial for policymakers, as they must take decisions quickly. In certain crises, such as disease outbreaks, real-time information on flight volumes may also be important beyond the economic domain.

Finally, our analysis is restricted to aviation which comprises only 3–5% of GDP in total, including indirect contributions which are not analysed here. However, our methods could be extended to other sectors of the economy where data is shared at a similar level of granularity. As the availability of real-time data increases, we could develop more accurate estimates of a large enough number of economic sectors to build a complete, real-time picture of the economy. In turn, policymakers would be able to respond more effectively to future crises.

## Data availability

This study was a re-analysis of existing datasets that are publicly available at the locations described in the Methods section.

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## Acknowledgements

We thank James Stanford from the ADS-B Exchange. S.M., H.S.M. and T.P. were supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1 (awards TU/B/000006 and TU/B/000008). The authors are also grateful for support provided by the Office for National Statistics Data Science Campus.

## Author information

Authors

### Contributions

S.M., H.S.M. and T.P. designed research; S.M., H.S.M. and T.P. performed research; S.M., H.S.M. and T.P. analysed data; and S.M., H.S.M. and T.P. wrote the paper.

### Corresponding author

Correspondence to Sam Miller.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

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Miller, S., Moat, H.S. & Preis, T. Using aircraft location data to estimate current economic activity. Sci Rep 10, 7576 (2020). https://doi.org/10.1038/s41598-020-63734-w

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