air travel seasonality

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Airline Peak and Off-Peak Award Charts: The Ultimate Guide [2024]

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Table of Contents

Why is understanding peak/off-peak seasonality important, airlines that have peak/off-peak award charts, final thoughts.

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Commercial aviation is one of the most seasonal industries in the world. Typically, the busiest time of the year for airlines is from May to October.

Of course, this varies depending on the exact destinations, countries, or even cities that you visit. That being said, customer demand plummets in the winter months from November to March, especially when visiting Europe.

Airline revenue ticket prices fluctuate wildly thanks to revenue management departments, crazy algorithms, and seats sold. The same is generally true for airline award tickets. As a result, many major airlines have chosen to adopt a binary award pricing scheme, whereby there is an off-peak set of dates priced cheaper than the remaining peak dates.

In this guide, we’ll be taking an in-depth look into every major airline’s seasonality charts and discussing the nuances of each program, with the ultimate goal of minimizing the number of points you’ll use for award flights.

Travelers with flexible plans ought to book flights during off-peak when possible in order to spend fewer miles.

You’ve already worked so hard to earn your points and miles, so why spend any more than absolutely necessary?

By planning trips around off-peak award dates, you’ll extract the maximum value from your points and miles. There’s a minimal amount of work in understanding what constitutes peak and off-peak dates for each airline you want to fly on, but it’s all worth it in the end!

This guide is not about variable award pricing, which itemizes mileage and award prices based on distinct levels. Instead, we’re going to be talking about the major airlines that have specific peak and off-peak travel dates.

Before talking about each individual airline, keep in mind that some airlines adopt different seasonality policies depending on what route you fly on, while others have a simple binary system.

Let’s look into the airlines and dissect their peak and off-peak award charts.

Aer Lingus is Ireland’s flag carrier. Aer Lingus is owned by the same company that owns British Airways, IAG, and its frequent flyer currency is known as Avios.

Each calendar year, Aer Lingus publishes its off-peak and peak periods. The off-peak periods constitute two-thirds of the year, whereby you can book award flights for fewer Avios.

Additionally, Aer Lingus has a distance-based award chart along with peak/off-peak pricing. You can redeem Aer Lingus Avios for great value, especially if booking during off-peak dates.

Aer Lingus’s off-peak dates for 2024  are as follows:

• January 8, 2024, to March 21, 2024
• April 8, 2024, to June 6, 2024
• September 2, 2024, to December 12, 2024

Now that we’ve pieced together the seasonality, let’s talk briefly about how many Avios you’d save by booking off-peak. Here’s Aer Lingus’s award chart for one-way flights:

Suppose you wanted to fly from Miami (MIA) to Dublin (DUB) in Aer Lingus business class on September 18, 2024. Assuming you find availability on this date, you’ll be traveling on off-peak dates, meaning you’ll pay 62,500 Avios one-way as opposed to 75,000 Avios (the peak season price).

Let’s also suppose that the departing segment of your trip falls on peak dates while the returning flight falls on off-peak dates. In this case, you’d simply pay the peak price one-way and the off-peak price the other way.

Aer Lingus’ system is pretty straightforward, but you can save up to 12,500 Avios each way by planning your off-peak travel accordingly.

All Nippon Airways

All Nippon Airways’ Mileage Club loyalty program is a fantastic overall program. Although its online user interface is somewhat dated, ANA has so many fantastic redemptions that you can’t miss out on.

ANA Mileage Club takes some time to learn about, but once that time is invested, you’ll find yourself digging up value redemption after value redemption.

Hot Tip: You can start racking up ANA miles pretty easily by following our guide on how to earn lots of ANA Mileage Club miles !

You can only book round-trip award bookings with ANA, so keep that in mind, too.

However, you can book one-way award bookings on ANA using Virgin Points via Virgin Atlantic’s Flying Club .

Most airlines split up their peak/off-peak dates into a simple binary system, whereby a date is either a peak or off-peak date. ANA has 3 seasonality periods — low season, regular season, and high season.

ANA uses seasonality  only for ANA flights . Seasonality does not apply to partners like United Airlines. The following is a chart that depicts the most updated seasonality dates for flights between Japan and Europe/North America until early 2025:

Keep in mind that there are different seasonality periods depending on what regions you’re flying to/from , and even for domestic flights. For example, Hawaii has a slightly different set of dates to the above as it is in Zone 5, while the rest of North America is in Zone 6.

For flights between North America and Japan , here’s the round-trip award chart:

So, if you choose to fly on ANA business class round-trip from New York City (JFK) to Tokyo (NRT), departing on January 10, 2024, and returning on January 31, 2024, you’ll pay the low season price — an incredibly low 75,000 ANA miles.

On the other hand, flying during high season on the same route will cost a decent 90,000 miles. This represents a 20% hike in prices just for flying during different times.

Bottom Line: In order to figure out the price you’ll pay for a specific ANA flight during a specific date range, you’ll need to figure out the seasonality of the route first. Then, you can figure out the award price by matching the award chart pricing with the correct seasonality and route. Keep in mind that this only applies if you intend to use ANA miles on ANA flights. 

American Airlines

American Airlines  does offer off-peak awards, and these off-peak awards are often fantastic deals. However, American Airlines only does this for economy flights . Additionally, qualifying tickets include partner airlines and aren’t limited to flights operated by American Airlines.

That being said, American Airlines has off-peak economy tickets for just 2 regions — Contiguous 48 U.S. states and Canada and Europe — and these tickets are called Main Cabin Off-Peak .

The Main Cabin off-peak dates for 2024 are January 10 to March 14 and November 1 to December 14 . Tickets are 22,500 AAdvantage miles one-way.

Hot Tip: In order to minimize your out-of-pocket costs at the hands of fuel surcharges , try to plan your travel solely on American Airlines flights. 

Asiana Airlines

Asiana Airlines has deservedly garnered a great reputation with its loyalty program Asiana Club . In addition, Asiana Airlines continues to be a top airline of choice to earn miles on, thanks to its attractive (and niche!) ways to redeem for maximum value .

Asiana Airlines has off-peak and peak pricing  only for its own flights . It also has different peak season dates departing on your exact route. Lastly, you can avoid peak season surcharges if you’re an Asiana Club Diamond Plus or Platinum member for both award bookings  and upgrades.

Asiana Airlines charges 50% additional miles for flights during high season, so you’ll absolutely want to avoid flying during popular times. Here is the peak season chart for 2024:

Off-peak dates are every other date not listed.

The award chart for Asiana Airlines flights is as follows for round-trip flights:

For example, let’s say you want to fly from Los Angeles (LAX) to Sydney (SYD) via Seoul (ICN) on Asiana Airlines, using Asiana Club miles, departing on July 3, 2024, and returning on August 20, 2024. The first segment is during the peak season, while the returning segment is during the off-peak season.

Furthermore, these flights have Business Smartium Class installed, which is Asiana’s best long-haul business class seat. This means that you’ll pay 150,000 miles plus 100,000 miles for the returning flight during off-peak season. You’ll definitely want to be careful to avoid peak season if you can since a 50% price hike is huge.

Bottom Line: Asiana Airlines raises prices for peak season award tickets by 50% across the board. Therefore, if flying on Asiana Airlines while redeeming Asiana Airlines miles, you should book off-peak tickets when you can. 

British Airways

British Airways has previously employed a distance-based award chart with multiple zone brackets. In addition, there’s off-peak and peak pricing. British Airways also has had multiple award charts, but British Airways, Iberia, and Aer Lingus were the only airlines eligible for off-peak pricing.

While award rate charts for 2024 have not been made publicly available , according to Head for Points , British Airways’ peak and off-peak dates for 2024 are as follows:

Off-Peak Dates:

• January 8 – February 8, 2024
• February 13-14, 2024
• February 20 – March 14, 2024
• March 16-28, 2024
• April 2-3, 2024
• April 9, 2024
• April 15 – May 3, 2024
• May 7-24, 2024
• May 28, 2024
• June 3-7, 2024
• June 10-14, 2024
• June 17-21, 2024
• June 24-28, 2024
• July 1-5, 2024
• July 8-9, 2024
• July 16, 2024
• July 23. 2024
• July 30, 2024
• August 6, 2024
• August 13, 2024
• August 20, 2024
• August 27, 2024
• September 9-13, 2024
• September 16-20, 2024
• September 23-27, 2024
• September 30 – October 25, 2024
• October 29, 2024
• November 4 – December 6, 2024
• December 9-13, 2024
• December 26, 2024

If your travel dates fall outside of these dates, your award ticket will be priced as a peak flight.

British Airways’ peak and off-peak calendars have consisted of wide bands, but they also contained multiple single-date occurrences for off-peak dates. Once you’ve identified the seasonality of your travel dates, you’ll want to figure out how much your flight will cost, which has historically been distance-based. Great Circle Mapper is a valid resource to gauge your flight distance.

Hot Tip: See our guides on the top ways to earn lots of British Airways Avios , and then make sure you’re redeeming them for the best value possible !

Iberia is Spain’s flag carrier. The airline happens to be owned by the same company that owns British Airways and Aer Lingus (discussed in earlier sections of this guide), but interestingly, the peak and off-peak calendars haven’t matched those when using Avios from different frequent flyer programs.

Additionally, Iberia has used peak and off-peak dates for flights on Iberia, Iberia Express, and Iberia Regional/Air Nostrum. For all other airlines, a different award chart will apply and seasonality is not used.

Award rates and eligible peak/off-peak dates for 2024 have not been made publicly available yet.

Just like its main competitor airline Asiana Airlines, Korean Air uses peak and off-peak pricing. Korean Air SKYPASS is a fantastic program to redeem miles on , with plenty of options ranging from Korean Air first class to Etihad first class .

Although there aren’t very many ways to accrue Korean Air miles from transferable points, there are still great ways to earn lots of Korean Air SKYPASS miles .

Korean Air employs a region-based award chart. Also, one-way flight redemptions are only allowed on Korean Air. For all partners, you must book round-trip if you want to use miles.

Hot Tip: Korean Air is one of the few airlines that still operate the A380. Check out our review of Korean Air’s A380 first class .

To determine whether or not your flights fall into peak season, you’ll need to know the route and the travel dates. Peak dates for Korean Air are:

Korean Air’s round-trip award chart is as follows (one-way awards are half the price):

*Direct flights only

If flying from New York (JFK) to Seoul (ICN) on Korean Air first class on December 17, 2024, your flight will fall into peak season. For this, you’ll pay 120,000 Korean Air miles one-way compared to 80,000 Korean Air miles for off-peak travel.

Virgin Atlantic

Virgin Atlantic employs standard and peak seasons for Virgin Atlantic flights only, so they are not applicable to airlines like All Nippon Airways. Furthermore, Virgin Atlantic splits up seasonality calendars between Caribbean routes and all others.

For Caribbean routes, the standard (off-peak) season consists of the following date ranges:

• March 4-21, 2024
• April 16 – May 26, 2024
• June 3 – July 19, 2024
• September 3 – October 24, 2024
• November 6 – December 6, 2024

On the other hand, Caribbean peak dates are on all other dates.

The rest of Virgin Atlantic flights follow these standard dates:

• January 4 – March 21, 2024
• April 16 – June 15, 2024

All other dates are peak dates for non-Caribbean flights.

Flights on Virgin Atlantic can be significantly cheaper if planned during standard dates. Here’s the Virgin Atlantic award chart for round-trip, standard season bookings:

And here’s the Virgin Atlantic award chart for round-trip peak season bookings:

Let’s take an example flight from London (LHR) to Las Vegas (LAS), which is on the U.S. West Coast. If you book a flight during peak season in Upper Class , you’ll pay 77,500 miles, as opposed to the standard price of 67,500 miles.

Overall, there are a lot of airlines that have unique peak and off-peak season policies. For the most part, airlines tend to limit the price variation from seasonality to their own airlines. For example, Asiana Airlines levies peak surcharges only on flights operated by Asiana and not on any other carriers.

An exception to this would be American Airlines AAdvantage , which makes off-peak pricing in economy available on partner airlines such as British Airways.

Keeping track of peak dates and booking travel flexibly to avoid peak season is a recipe for conserving your hard-earned points and miles. As a result, you’ll want to confirm that you’re getting the best value redemption prior to committing to a flight.

Now, you have a one-stop shop for a guide on the airlines that carry peak and off-peak policies for award bookings. Happy booking!

Frequently Asked Questions

What time is off-peak for flights.

Off-peak travel times vary by airline, route, and dates. Each airline has defined separate date ranges for off-peak seasons where a flight will cost fewer miles.

What is off-peak travel?

Off-peak travel is defined as traveling at a time where there is less than maximum demand. Some off-peak travel ideas would be to fly to Europe during the winter months of January through March.

What is peak travel?

Peak travel is defined as the period of travel in which there is maximum demand. You’ll often see large crowds, expensive hotel prices, and more expensive airplane tickets.

Is off-peak cheaper than peak?

Off-peak is almost always cheaper than peak travel. Because airlines don’t expect to sell out their seats, they release “cheaper” seats that you can use miles for. Also, the mileage price is cheaper during off-peak compared to peak. The exact discount varies from airline to airline, so you’ll need to reference one of the airlines above to figure out how much cheaper it is to travel during peak dates with points.

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Peak season demand shows the desire for air travel

IATA announced passenger data for August 2022 showing continued momentum in the air travel recovery.

Total traffic  in August 2022 (measured in revenue passenger kilometers or RPKs) was up 67.7% compared with August 2021. Globally, traffic is now at 73.7% of pre-crisis levels.

Domestic traffic  for August 2022 was up 26.5% compared with the same period last year. Total August 2022 domestic traffic was at 85.4% of the August 2019 level.

International traffic  rose 115.6% versus August 2021 with airlines in Asia delivering the strongest growth rates. August 2022 international RPKs reached 67.4% of August 2019 levels.

“The Northern Hemisphere peak summer travel season finished on a high note. Considering the prevailing economic uncertainties, travel demand is progressing well. And the removal or easing of travel restrictions at some key Asian destinations, including Japan, will certainly accelerate the recovery in Asia. The mainland of China is the last major market retaining severe COVID-19 entry restrictions,” said Willie Walsh, IATA’s Director General.

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Fares tell the tale of seasonality as airports stay packed

Without enough seats on U.S. airlines, the seasonality typically witnessed is now found in fares, with passenger numbers remaining relatively stable.

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Of the many characteristics of air travel changed by COVID-19, seasonality is the most recent to return. However, for the United States, the airline industry faces a starkly different circumstance than it did leading into 2020 — not enough seats. The result is a very different view of seasonality in 2022 than existed in 2019.

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Particularly leading into the fall and winter months, the health of air travel has been determined by the travel that continues to occur despite seasonal impacts, as business travel takes over. Higher fares leading into the fall conference season were looked for in order to offset the high volume leisure travel of the summer.

Today, seasonality continues to exist. What is different, however, is the way in which seasonality is presenting in the fall numbers. Rather than stable fares and falling passenger numbers, the U.S. is seeing stable passenger numbers and falling fares.

Taking a broad look at the U.S. market in this TAC Analysis, we consider what falling fares mean for the coming holiday months and into 2023. Despite warnings of softness, we examine how the seemingly contradictory lower fares are a further sign of air travel demand returning to normal.

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Chairman of ASG Gediminas Ziemelis: A global approach to seasonality in aviation

As an essential revenue generating asset, an aircraft generates value when it’s airborne and operating at close to peak capacity. Meanwhile, an aircraft that is idle for any reason (malfunction, maintenance, flight cancellation, etc.) simply bleeds money. According to a rather conservative estimate by Boeing, a 1–2-hour aircraft on ground event might cost an airline $10-20,000. Those in the industry will tell you that the actual cost is more in the region of $150,000, if not more. Yet, while most AOG events are hard to predict, airlines and air operators, especially those focusing on travel in Europe, know that there’s one thing worse than an unexpected incapacity to fly. That’s the seasonal nature of the business, with capacity linked tightly to the travel patterns of travellers and holidaymakers.

Seasonality remains a factor even post-recovery

Before the COVID-19 pandemic, a discernible trend emerged in the global travel industry: leisure travel has begun outpacing business travel in many countries and regions. From 2010 to 2019, the compound annual growth rate for leisure air trips stood at 6.6%, substantially higher than the 3.3% rate for business air trips, according to McKinsey & Company. This disparity in growth rates has only broadened in certain areas during the post-pandemic recovery phase of air travel. As the industry continues to rebound from the 2020 downturn, the resurgence in leisure traffic has assumed the fastest pace. The problem with leisure travel, however, is the fact that it is more seasonally driven, with marked peaks and troughs aligning with the Northern Hemisphere’s summer and winter months.

According to recent data from IATA, in 2023, European carriers’ traffic saw a 22% year-on-year increase. While the trend is positive, with a parallel increase in capacity and load factor, the recovery is slower than in other regions. Recovery of specifically winter season travel in Europe might be even slower due to the tightening of purse strings for many Europeans. With the European Central Bank reluctant to lower record-high interest rates, we can expect Europeans to be more cost sensitive when planning their holidays, even though some airline executives might believe that consumer spending on travel is immune to the cost of living crisis. Time will tell.

It is worth noting that even if the recovery is speedier than expected, seasonality in Europe is not going away. We have all seen reports of major European airlines posting substantial profits in 2023 Q3, with

some performing better than in 2019. But what happens when more than 50% of your annual operating profits come from a single quarter? This disproportion is problematic for several reasons. For one, the uptick in demand during the summer can lead to delayed flights, overstretched staff and unhappy customers. Still, those symptoms don’t put companies in financial jeopardy. Something that can’t be said when it comes to the accumulation of additional aircraft and staff. Acquiring capital-intensive aircraft and having them fly at half-capacity or sit idle during the low season naturally leads to losses.

Better planning is key but is not a silver bullet

To stay profitable, airlines must implement strategic measures to effectively manage summer demand peaks, ensure stable operations, and crucially, mitigate or reverse winter losses. Counteracting the impact of seasonality requires several critical approaches that airlines should consider adopting.

This begins with being more rigorous when it comes to planning for the entire year. Key strategies to keep in mind involve optimizing yields through established pricing and revenue management techniques. Here, we are already seeing a surge in Artificial Intelligence solutions and the role of AI will only increase in the nearest future.

An airline’s plan for the year should account for more than just summer-winter fluctuations. Having commercial agreements with tour operators, government agencies and other clients in need of air transport during massive events (from the Hajj to the Olympics) is a key factor.

In addition to being better planners, airlines should also look for potential surges in demand linked to specific events. For example, after hosting the World Cup in 2022, Qatar became the destination of choice for Gulf travellers in 2023. Thanks to a flurry of high-profile events and an elevated profile, Qatar broke the 4 million tourist barrier for the first time. Unsurprisingly, 85% of all visitors arrived by air, according to the country’s Civil Aviation Authority.

Combatting seasonality – an ACMI perspective

Outsourced capacity providers are increasingly integral to the operational strategy of individual airlines. Having a leaner internal fleet helps a company to absorb seasonal decreases in demand for airlines. It is telling that in 2023 almost 738,000 block hours (BH) were operated by ACMI companies around the world, according to ACC Aviation. This number shows an almost 100% year-on-year increase, and with pandemic-related restrictions behind us, we can expect more airlines to incorporate outsourced capacity into their strategy.

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Seasonality in Travel and How to Maximize the Revenue Opportunity

Understanding seasonal travel patterns is a fundamental building block to run a profitable charter business. Seasonality is a characteristic of a time series of data such as bookings, which experiences predictable changes that repeat every calendar year. Cyclical changes to demand over a calendar year are classified as seasonal. In travel, booking volumes vary by destination and understanding the magnitude of the cyclical changes is required to adapt the decision making process in arriving at a price quote to maximize sales. Every destination exhibits seasonal demand patterns and can be broadly classified as high (peak) season, shoulder season, and low (off-peak) season. 

The approach to address seasonality in travel is a multi-step process.

• Demand variation by week or month over a calendar year 
• Demand variations by day of week 

To a large extent these two types of seasonal variations can be modeled with statistical models such as Holt-Winters, ARIMA (auto-regressive integrated moving average), SEATS (signal extraction in ARIMA time series), STL (seasonal and trend decomposition with Loess), and many more.

In addition, holidays, and special events influence demand patterns. Modeling seasonality to address holiday demand is challenging. There are two types of holidays. They are fixed date holidays and floating date holidays. 

Christmas and the 4 th of July are examples of fixed date holidays, but the demand is influenced by the day of week these holidays fall on. Thanksgiving is an example of a floating date holiday, but always happens on the fourth Thursday in November. Memorial Day is similar and is always the last Monday in May.  Moving holidays like Easter are a bigger challenge to address since it is based on the lunar calendar (the first Sunday after the full moon after the Spring Equinox) and could be in March one year and April the next. For this reason, to forecast seasonality with predictive models requires 7 years of data which may not have been captured and stored on a database. Therefore, users focus on using they market intelligence to adjust the baseline forecast to address seasonality. 

Demand for charters during special events are probably the most difficult to forecast with any degree of accuracy.  Examples of special events are the NBA All Star game, NFL’s Superbowl, the MLB All-Star game, etc. where the destination market changes every year.

Here are a few examples of seasonal variations in demand.

Illustrates a typical seasonal demand pattern at a destination. There is a big summer season and a smaller winter season that happen at the same times of year and repeat annually.  Such patterns are easy to capture in a variety of time-series models that incorporate seasonality.

Illustrates what many businesses saw with COVID-19; both demand and seasonality were non-existent. In the graph, demand is picking up again as seen at the far right but the larger question is if the old seasonal pattern will reappear.

Illustrates the demand by day of week at a destination. For many businesses, demand is high from Monday through Thursday, with Friday slowing down, and the weekend has lower demand.  These patterns need to be estimated simultaneously with annual patterns.

So why is destination seasonality important for charter operators? Leveraging a combination of global demand data and user overrides based on a user’s marketplace intelligence, seasonal variation in demand is used to determine if the price for a charter flight should be marked up (e.g., high season), stay the same (e.g., shoulder season) or marked down (e.g., low season) by destination week of year or month. Holidays and special events will require an additional adjustment to the price quote, either model based or based on user overrides. In summary, seasonally adjusting data is challenging, and comparing business results across time is not easy and requires business expertise combined with statistical modeling. A seasonal forecast can improve the bottom-line for a profitable charter operation.

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ACI World Insights

Airport markets and seasonal variations

Demand for air transport across a number of tourist destinations is subject to variations in any given year. When variability is a recurring phenomenon, the data series that describes traffic over time is said to have a seasonal component. From a statistical perspective, this seasonal component is inherently non-stationary, in that the behaviour of the data is dependent on time. Major fluctuations experienced by airports throughout the year occur most commonly among airports serving major tourist destinations. International measures of traffic seasonality provide insights for understanding the dynamics of air transport demand. A deeper understanding of demand and its drivers permits airports to plan for capacity and resource use during peak periods. Information regarding the seasonality of traffic also permits airlines to manage their fleets efficiently at different airports at different peaks. Naturally, the seasonality of traffic affects other markets beyond air transport. Since a large proportion of passengers are recreational travelers, the tourism industry is also significantly affected by variations in air transport demand. Like airports and airlines, hotels and other businesses focusing on leisure activities rely on measures of seasonality to plan their resource bases effectively.

ACI’s analysis of seasonality patterns in the global passenger traffic data set shows the series tends to peak year after year in the months of July and August. In a sample of more than 1,000 airports, July and August are the most prevalent peak months for over 50% of airports. This two-month period coincides with a higher propensity to travel during the summer vacation season in the Northern Hemisphere. Charts 1 and 2 show the variability in monthly traffic for the global data series over a seven-year period.

Monthly passenger traffic by region (2010-2016)

Measures of seasonality

A variety of measures are used to assess the level of seasonality and variation in traffic figures for any given airport. This section focuses exclusively on three such measures, the Gini Coefficient, the seasonality ratio and the seasonality indicator (or peak month proportion).

The Gini Coefficient, which is traditionally used to measure income inequality in populations, may also be used to evaluate fluctuations in traffic by calculating the relative main difference between every month of passenger traffic in a given year. The Gini Coefficient ranges from a minimum value of zero, where traffic is evenly distributed across each month, to a theoretical maximum of one, indicating complete seasonality: if a given airport had a Gini Coefficient value of one in a given year, this would imply it handled all that year’s passengers in one month.

The seasonality ratio is calculated by dividing an airport’s highest monthly traffic by its median monthly traffic. Although seasonality ratio calculations typically use an average of monthly traffic as the divisor, employing the median value instead minimizes the impact of any outliers on the calculated indices.

The seasonality indicator calculates the traffic that is allotted to the peak month as a proportion of total annual traffic. Tables 1 provide rankings for airports handling over 1 million passengers annually that have significant seasonality components to their passenger traffic. The airports are ranked according to the calculated value based on a 12-month period (2016) and results differ based on the chosen method.

Top 10 airports ranked by highest Gini coefficient with corresponding seasonality ratio and peak month (airports >1million)

Bourgas Airport, the second-busiest airport in Bulgaria for passenger traffic, is the most seasonal airport in the world. Another Bulgarian airport, Varna, is in the top five for all three measures of seasonality which ACI’s analyses employ. Bourgas (BOJ) and Varna (VAR) are located on Bulgaria’s Black Sea coast and during the summer months many tourists flock to these cities’ resorts and beaches. The Gini Coefficient for Bourgas is 0.66, which indicates a high level of seasonality in the data series compared with other airports. The closer the value is to one, the higher the variability with respect to month-to month passenger traffic. The seasonality ratio for Bourgas shows its peak traffic month has nearly 29 times its median monthly passenger traffic. The seasonality indicator (or the peak month proportion) reveals almost 30% of Bourgas’ annual traffic occurs during a single month. August, a summer month in the Northern Hemisphere, is typically the peak month for most of the 10 top seasonal airports.

The Mediterranean effect

Although different measures of seasonality yield different results, there is a common thread among indices. Some 80% of the airports in the top 30 most seasonal airports are located in the Mediterranean region. In Europe, monthly passenger traffic variations reflect the mainstream holiday period from July to September and movements from north to south.

Tourism-oriented airports show the strongest seasonality patterns. On a regional basis, European airports exhibit the greatest level of seasonality, handling almost 11% of their total annual passenger volume in the month of August. The region with the least seasonal variation is Asia-Pacific, the proportion of its airports’ annual passenger traffic ranging from 8.9% in August, the peak month, to 7.6% in February. Table 30 presents the various seasonality indicators on a regional basis. On a relative scale, it can be seen that Europe has the highest level of seasonality among its airports. On a global level, passenger traffic tends to peak in August, with the lowest passenger throughput occurring during the month of February.

Does air cargo exhibit seasonality?

Most discussion of airport seasonality focuses on passenger traffic. However, air cargo also demonstrates some seasonality. Instead of experiencing a peak in the northern summer, like passenger traffic, air cargo experiences a significant trough in the first quarter of every year. Because air cargo traffic is so highly concentrated at airports in China and South East Asia, the Chinese New Year significantly affects it. Many businesses are closed for the holiday season and, as a result, many shipments by air are postponed to a later date. This sharp decline can be seen during every Chinese New Year.

In 2016, the distribution of air cargo indicates that approximately 7% of total traffic volume was handled during February, the month with the lowest volume following the Chinese New Year. Traffic then experienced a peak in the month of March, which saw 9% of annual traffic. Besides these regular annual phenomena, traffic volumes increased during the months of October, November and December, reflecting the holiday season (i.e., Christmas orders). However, apart from the month of February, monthly air cargo volumes are much less variable than monthly passenger traffic volumes.

With comprehensive data coverage for over 2,400 airports in 175 countries worldwide, ACI’s World Airport Traffic Report remains the authoritative source and industry reference for the latest airport traffic trends, rankings and data rankings on air transport demand.

Boasting traffic forecasts for over 100 country markets, the World Airport Traffic Forecasts (WATF) dataset presents detailed metrics which include total number of passengers (broken down into international and domestic traffic), total air cargo and total aircraft movements. Absolute figures, compounded annual growth rates (CAGR), market shares and global growth contributions are presented over three time horizons: short-, medium- and long-term over the 2017–2040 period.

For a more detailed analysis and insights on air transport demand, please visit ACI’s Economic and Statistic’s suite of products .

Patrick Lucas

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Systematic review of passenger demand forecasting in aviation industry

Renju aleyamma zachariah.

1 Sabre Travel Technologies India Private Limited, Bengaluru, Karnataka India

Sahil Sharma

2 Computer Science and Engineering Department, Punjab Engineering College, Chandigarh, India

Vijay Kumar

3 Department of Information Technology, Dr. B R Ambedkar National Institute of Technology, Jalandhar, Punjab India

Associated Data

This research is a part of the review of the existing literature. Hence, no dataset was required for practical purposes.

Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.

Introduction

The work of academic researchers and industry personnel has substantially aided aviation traffic forecasting in recent decades. They analyzed the need for air travel from several perspectives. Air transport demand studies span international, national, regional, and intercity levels and airports. The demand for air travel changes as different types of travelers and their preferred locations varies. Only some studies have specifically examined the various modes of air travel due to the range of traveler types and their desired destinations. Most individuals fly to get somewhere and do something; few fly just for fun, indicating that demand for air travel is mainly derived [ 77 ]. In other words, geographically fixed activities, especially those associated with business, pleasure, visiting friends and family, and other reasons, substantially impact air travel demand. Figure  1 depicts the diverse travel objectives by classifying travelers as business, leisure, or others.

Travel Purpose by Air

Because of increased travel opportunities, the air transport industry has grown significantly during the past decades. Even though the industry has suffered due to the pandemic, political and market-driven events discourage passengers from traveling. Forecasting future demand for the aviation industry is critical, as is knowledge of determinants. The most standard predictive models used in aviation demand studies are econometric and time series models. Many forecasting models rely on aggregate demographic or economic factors such as total population, gross domestic product (GDP), or per capita income. Similarly, most airline cost estimates are based on highly aggregated characteristics such as average airline yield. However, as evident by the recent dramatic increase in oil prices and the eventual collapse of global markets owing to pandemics, passenger demand in the aviation industry plays a crucial role.

Based on the above facts, this study has three folds. The first step is to review the available air travel literature at various levels. The second step is to identify the primary factors influencing aviation demand and asses alternative forecasting passenger demand techniques based on their efficiency in dealing with the volatile nature of air travel. The third step entails presenting a detailed mapping of the existing challenges with aviation demand forecasts, which will be critical in the current unstable economy or post-pandemic era [ 46 ].

The airline industry and the travel economy highly emphasize predicting the number of passengers per trip. It has attracted airline executives’ interest in developing better predictive models to integrate into their business models. Mid-term and short-term forecasts provide critical information for monthly operations and maintenance decisions, such as fleet scheduling, ticket fares, and the opening of airports or new business ventures, allowing time to market strategy implementation.

We were inspired to conduct this investigation for the reasons listed below:

• There has yet to be a recent systematic review that considers all aspects of predicting aviation demand.
• To draw attention to the various challenges in forecasting aviation demand.
• To research various performance evaluation metrics.
• To discuss applications and limitations in aviation demand forecasting.

Contribution

This work is motivated by substantial research surveys to identify the main factors that determine aviation demand [ 43 ] and forecast passenger demand techniques [ 43 ]. Because of recent global shifts, anticipating aviation demand has become increasingly important. Because of market uncertainty and volatility, most forecasting research has relied on deep learning techniques and statistical models. The purpose of this paper is to investigate the numerous aspects that influence aviation demand forecasting as well as the various forecasting approaches. The contributions of this study are as follows:

• The impact of various factors on aviation demand is investigated.
• Pros and cons of Artificial Intelligence techniques for aviation demand forecasting are discussed.
• Commonly used performance metrics are discussed.
• Different application areas of Aviation demand forecasting.
• The current challenges associated with aviation demand forecasting techniques are also studied.

The word cloud depicts the top 100 Aviation demand forecast keywords (see Fig.  2 ). The keywords related to the aviation demand forecast algorithm, such as “Forecast,” “demand,” “airport,” and “passenger,” are widely used in this word cloud.

Word cloud of passenger forecasting literature

The remainder of this paper is organized as follows: Section 2 discusses the background of aviation demand. These variables drive aviation along with a Machine learning-based framework for passenger demand foresting. Section 3 presents the research methodology used in this study. Section 4 discusses various techniques used for forecasting aviation demand. The evaluation of forecasting techniques is in Section 5 . Section 6 discusses the applications of aviation demand. Section 7 summarizes the current research challenges, followed by future research directions—section 8 presents the discussions, followed by the concluding remarks in Section 9 .

This section briefly describes drivers and machine learning models for passenger demand forecasting.

Drivers of aviation demand

Aviation demand is affected by economic, geographical, service quality, airfare, and market factors. Service quality criteria include aircraft size, flight time, frequency of flights, and in-house service [ 104 ]. Market determinants include the hubbing strategy, year, and period of observation.

Figure  3 summarizes the various factors that influence passenger demand. Regional economic activity is controlled by commercial, industrial, and cultural factors [ 29 ]. The two most important economic factors are income and population size.

Aviation Demand Drivers

Economic growth

Most research was devoted to identifying and measuring economic factors influencing aviation demand. Financial elements include the gross domestic product (GDP), GDP per capita, income, and population size [ 91 ]. The rate of GDP growth is used to assess economic progress. Several studies have been conducted to forecast GDP growth based on passenger demand forecasting. Even though the study’s findings show that GDP is a primary driver of aviation demand, Zhang et al. [ 118 ] investigated the genuine market demand in the Chinese market. A stochastic frontier analysis and model average technique is used to model the uncertainty component. With the advent of new technologies, the goal is to use scientific methods to determine the genuine demand (Table ​ (Table1 1 ).

Different predictor variables for forecasting passenger throughput

However, predicting the extent of changes in economic factors that may affect the aviation industry, particularly passenger volume, is challenging. Wang et al. [ 109 ] used Bayesian network analysis to bridge the gap. It is a probabilistic method for dealing with global uncertainty. Users can use a Bayesian network to make decisions. The Bayesian network’s probabilistic output provides a more comprehensive view of global uncertainty. The Bayesian network was developed by combining airport-level data with city or country-level. The data shows that the findings, GDP, and inflation have an impact on passenger demand, whereas fuel prices have a direct effect on cargo volume. This study’s scope is limited to 45 cities in developed countries. This result may not be correct in the case of undeveloped countries.

The African study emphasizes the link between aviation demand and economic development in six African countries [ 2 ]. The study was conducted using three distinct income levels (i.e., low, middle, and high income). A one-way causal relationship exists between economic development and aviation demand in middle-income countries. Direct investment in other industries may help the growth of the aviation industry. The low-income region has a unidirectional causal relationship pattern because the country is a hub for the region’s largest airline group. As a result, the region’s economic strategy should focus on aviation growth and tourism facilitation. However, the elasticity of the interaction between economic growth and aviation demand varies across countries in the short and long run. The marginal contribution coefficient is higher in lower-income and middle-income countries. Economic development changes impact air passenger demand more than air freight demand.

Gallet and Doucouliagos [ 36 ] conducted a detailed study in Cyprus to quantify the income elasticity of air travel. The magnitude of income elasticity influences consumers, perceptions of air travel as a luxury and necessity. Kluge et al. [ 58 ] did this analysis to find passenger demand forecasting in the European market. They considered GDP per capita, urbanization level, geographical location, and the population’s education level. The empirical studies proved that the factors mentioned above influence aviation demand.

Albayrak [ 54 ] investigated the factors influencing air traffic in developing economies. According to their findings, airport physical characteristics significantly impact air passenger traffic, tourism volume, and the number of foreign residents. The airport competition is positively correlated with the increased air passenger demand. The emerging economies’ behavioral patterns are comparable to those of developed economies. The panel data estimation technique estimates the key drivers and their impact on aviation demand. GDP per capita, population, the position of a hub, distance to the closest airport, the number of beds, the number of foreign residents, and the academician ratio are all factors to consider.

Geographical location

Geographical location is an essential factor in influencing passenger demand. A well-connected and well-structured transportation system has an impact on travelers. International passengers prefer well-connected infrastructure. It is impossible to overestimate the importance of smooth transit and well-connected and comfortable terminals. Dubai, Singapore, and Heathrow are examples of well-known transit hubs. Visas on arrival in transit hubs are used by airlines to attract consumers. Tolga and GÖKMEN [ 102 ] identified the impact of geographical location and economic factors on aviation demand.

The use of airport transportation aids in overcoming geographical constraints. Hoyos and Olariaga [ 50 ] conducted research in the Colombian liberalized market. Colombia has a complicated landscape with multiple mountain ranges but inadequate regional connectivity. The domestic passenger could be identified and forecasted using system dynamics. Although the model correctly identified the peak traffic season, including the main geographic features influencing passenger demand would have been a better approach.

Quality of service

Service quality factors significantly influence passenger demand: seating comforts, legroom, meal service, in-flight entertainment, and modern aircraft quality. Passengers and airlines are more sensitive to these aspects in contemporary scenarios such as COVID-19.

Prentice and Kadan [ 81 ] found that airport service quality significantly impacted aviation demand. They assess check-in, ambiance, basic facilities, mobility, and security as airport service quality characteristics. A confirmatory factor analysis with maximum likelihood is performed to determine the reliability and validity of these factors. The environment plays a significant role in deciding aviation demand. Passengers at Heathrow Airport reported a strong connection between scents and their destination experience, prompting the installation of aroma dispensers.

Personalized travel is the new buzzword in the airline industry. One of the most recent drivers of aviation demand is customized travel. The airline customers’ travel preferences and forecasting their travel demands are critical. In a competitive market, airlines can use customer travel preferences to improve services and optimize flight schedules based on need.

Understanding travel behavior is also essential in various aviation-related industries, such as airport design, supplier requirements, and tourism-related firms. Liu et al. [ 66 ] proposed a Multiple Factor Travel Prediction model for aviation demand. Various elements influence a customer’s travel decision, including personal intrinsic factors such as travel preferences.

Another factor driving aviation demand is customer spending capacity. Seсilmis and Koс [ 88 ] investigated the relationship between economic factors and aviation demand. The interaction between aviation demand and economic factors was estimated using cross-sectional data analysis. According to their findings, rising per capita income and lower ticket prices significantly impacted aviation demand.

Impact of tourism

Transportation and tourism are inextricably intertwined industries. Good accessibility, as determined by available transportation services, is crucial for developing any tourist destination. Aviation is an essential mode of transportation for tourism markets. While long-distance travel and international tourism have traditionally been dominated by air travel, deregulation, particularly the emergence of the low-cost carrier sector, has increased aviation’s importance for short and medium-distance tourism. More research is needed to study the growth of aviation and tourism. Furthermore, airports are becoming more proactive and experienced in generating leisure demand and providing an appealing service to leisure travelers.

Divisekera [ 31 ] investigated the relationship between international air travel demand and international tourism demand. The association was discovered by utilizing a consumer analysis method. The study’s main finding is that the degree of substitutability varies between destination pairs, reflecting the diversity of tourist preferences among countries. The cross-price elasticities of transportation and tourism demand are similar. This study does not account for possible interactions between short-distance destinations because it is limited to long-distance intercontinental travel.

Ghalehkhondabi et al. [ 41 ] analyzed demand forecasting in passenger transportation. This study emphasized the logical dependency on passenger transportation. It prioritizes the accuracy of short-term and long-term forecasts, allowing the tourism industry to provide the necessary services at the right moment. Short-term forecasts might be helpful in daily or weekly activities such as determining the price of a seasonal cruise vacation. Long-term forecasts can aid in developing costly infrastructure and facilities, such as extending roads to meet increased visitor demand at a tourist attraction. Over a year (long-term), the expected number of visitors can help develop necessary infrastructures such as hotels and transportation terminals.

Liasidou [ 65 ] did qualitative research to address aviation’s strategic influence on the tourism industry. They considered the tourist destinations such as Cyprus, the Maldives, and Lakshadweep. A questionnaire was created specifically for the study’s location. Peeters et al. [ 78 ] analyzed that the aviation and tourism industries should work together to thrive. Overtourism is a relatively new term, yet it is an emerging concept. The impact of over-tourism on aviation demand prediction and its implications in the post-Covid era was investigated in this study [ 78 ].

Travel websites are a valuable source of information. The keywords tickets to or travel at a specific location were used to collect data on search query volume. Another critical feature for demand prediction is customer testimonials from a travel website. ARIMAX and naive models were used to forecast tourism demand. This novel approach emphasized the importance of multisource online big data. Tourism forecasting methods could be revolutionized in the long run by employing dynamic pricing strategies and proper staff scheduling. Tourist attraction operators can change daily demand predictions based on near real-time and high-frequency forecasting [ 64 ].

Impact of seasonality

Aviation demand for a variety of tourist destinations varies from season to season. When variability occurs regularly, the time series identifies it as having a seasonal component. Because the data’s behavior is time-dependent, this seasonal component is inherently non-stationary [ 20 ]. Global traffic seasonality measures shed light on the dynamics of aviation demand. Airports can better plan for capacity and resource use during peak periods if they understand the market and its dynamics. Information about traffic seasonality also enables airlines to efficiently manage their fleets at various airports during peak periods. Aside from air travel, seasonality impacts businesses such as tourism. The seasonality ratio and the seasonality indicator are used to compute seasonality. The seasonality ratio is calculated by dividing the most significant monthly traffic by its median monthly traffic at an airport. The seasonality indicator calculates the percentage of annual traffic assigned to the peak month.

Chudy-Laskowska [ 25 ] discovered the seasonality component in the dataset using seasonality-based modeling techniques such as exponential smoothing, seasonal auto-regressive integrated moving average (SARIMA), support vector machines (SVM), and artificial neural networks (ANN). The paper’s conclusion on the seasonality measure is ambiguous, although it does observe that dynamic growth is diminishing. Aviation demand is predictable due to its seasonality.

Seasonality is always a challenge in forecasting. Seasonality causes model variations. Samli et al. [ 87 ] used econometric models to examine seasonality in time series data, the seasonality method, and the history of similar periods. One of the model’s most essential aspects is using dummy variables as a seasonality indicator. Although the research was limited to a single airport, the model’s reliability might be evaluated in other markets.

Low-cost carriers

Flying used to be a luxurious experience, but with the emergence of Low-Cost Carriers (LCC), the glamour has disappeared. It has evolved into history’s most practical mode of transportation. Most passengers thought that paying low fares for no-frills air travel was a good deal. The success of LCC is owing to the discounted fares. One of the most important competitive factors for airlines is ticket cost. Most airline passengers want to arrive at their destination as soon as possible and for the least amount of money possible. Another reason for LCC’s success is technology, such as ticketless travel, which reduces reliance on Global Distribution System (GDS) booking and increases ticket fare transparency.

Alarfaj and AlGhowinem [ 6 ] researched to improve LCC passenger demand prediction by considering Islamic holidays in Saudi Arabia. During festival season, millions of people from all over the world go to these destinations. The impact of the holiday season on LCC airlines was investigated using genetic, artificial neural networks, and multiple linear regression. The results demonstrated that demand increased during this season. This study will aid in developing a new fleet for the upcoming season. However, the study was limited to Islamic holidays and did not consider other seasonal factors. It was feasible to change the face of aviation by introducing consumers.

Alsumairi and Tsui [ 9 ] investigated the impact of low-cost carriers on inbound Saudi passengers. Because of LCC’s growing market presence, passengers now have a choice. LCCs generally serve budget-conscious passengers, contributing to converting non-frequent flyers to frequent flyers. Short-term and long-term passenger demand has increased as aviation markets have been liberalized. According to the findings of this study, airline passenger capacity is steadily growing. The model, Box Jenkins Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) with explanatory variables that include LCC variables, reveals the importance of air passenger demand.

Boonekamp et al. [ 18 ] studied the impact of low-cost carriers (LCCs), ethnic ties, and aviation-related jobs as demand drivers for aviation. Strong ethnic relations between countries, as well as a large proportion of aviation-dependent work, accelerate demand. In contrast, a more significant presence of LCC on a route positively impacts the market. Pratt and Schuckert [ 80 ] analyzed the impact of low-cost carriers entering saturated or developed transportation markets such as the United States or Europe. The outcome indicated economic leakage, but it also demonstrated market implications. International tourists that travel on low-cost airlines significantly contribute to the economy.

System failures

The airline system is the backbone of the aviation industry. The system’s expected availability is 99.99%. An outage in the air system can cause flight cancellations, delays, passengers missing connecting flights, inability to check-in, and other issues. If there is an outage, the airline will lose a lot of money, and passengers may be hesitant to travel again. Southwest Airlines lost $5 million to $10 million in ticket sales due to one of its recent outages. The airline must have a backup plan in place. Brause et al. [ 19 ] investigated the possibility of a backup decision support system in the case of a system failure. The decision support system will assist the operator in deciding which and how many passenger handling service stations can be distributed without disrupting flight operations. The failure points can be minimized as the legacy aviation system is phased out and new distributed cloud systems are introduced.

Economic events

Economic events have a significant impact on passenger demand. Air passenger demand in the United States has reached an all-time low due to the September 11 attacks. The Brexit vote is another economic event that directly influences the aviation industry [ 39 ]. Gelhausen et al. [ 38 ] improved the current direct demand model and applied it to the new model. The new model, which is based on co-integration theory, is more adaptable. It directly estimates passenger and flight volume at German airports. The model’s estimated GDP elasticity was 1.31. The model was used to estimate the effects of Brexit on traffic volume at German airports from 2016 to 2018. This model handles two scenarios concerning the impact of Brexit on traffic volume.

Natural disasters significantly impact the operations and passenger demand of the aviation sector. Airports and borders are closed. Flight paths are diverted during natural disasters such as hurricanes, tsunamis, and pandemics. It is necessary to assess both the passengers’ eagerness to fly and the airline’s potential to offer a high degree of safety. Lamb et al. [ 60 ] used a qualitative approach to better understand the social and emotional behavior of flight passengers during the pandemic, emphasizing the importance of putting in place safety measures and a stress-free journey. The airline and airport authorities must make additional investments to accommodate this. Sun et al. [ 98 ] conducted an empirical study on the impact of a pandemic on the aviation industry. Researchers examined the actual effect at various scales, including domestic and international travelers and origin-destination pairs for large markets. The most affected is international travel. The results may not be accurate as the survey used simulated data.

Travel restrictions always impact passengers, and there are several factors to consider, such as planning a trip during a travel restriction. Passenger-centric metrics will be analyzed to understand better how people feel while traveling. If flights are frequently canceled or delayed, passenger demand may suffer. Passenger demand can also be forecasted using passenger-specific indicators. Monmousseau et al. [ 74 ] investigated how passengers in the US aviation industry feel during travel limitations. The data was taken from Twitter or sentiment analysis tools [ 79 ]. More complex analysis and quantitative methods are required for a more accurate result.

Travel restrictions will result in air traffic disruptions and a reduction in global mobility in the short term. It has a worldwide socioeconomic impact at the same time. Iacus et al. [ 51 ] gathered data on air passenger volume worldwide. It investigated how travel restrictions affect the number of air passengers as well as socioeconomic conditions. The non-homogeneous Poison process was used to forecast the aviation demand.

Forecasting aviation demand during the pandemic is critical because the return on investment is one of the factors used to justify their investment. The effect of COVID-19 on Chinese air passenger demand has been investigated [ 111 ]. The data is sourced from a central Global Distribution System (GDS) provider and is updated in real-time. The number of available seats and the passenger flown ratio for a specific route was studied throughout the time. According to the findings of this study, lesser lockdowns and travel restrictions may increase airline recovery chances. As a result, all Chinese airlines are unaffected. This study should be expanded into other markets to determine the actual impact.

Forecasting is difficult in these uncertain times. With the advent of neural networks and geospatial analysis, forecasting is now possible. The technologies should be used to help the aviation industry’s rapid recovery [ 70 ]. According to studies, international travel is the most affected. However, aviation can focus on domestic travel to recover more rapidly.

General framework for passenger demand forecasting

Data is highly volatile as a result of market fluctuations and looming uncertainties. Machine learning-based demand forecasting is more adaptive and allows for faster new data integration into models than conventional approaches. As a result, machine learning-based frameworks are flexible and reliable enough to benefit researchers [ 76 , 85 , 86 ].

The forecasting framework for estimating passenger demand involves data collection, pre-processing, time series analysis, and forecasting [ 4 ]. Many tried to evaluate appropriate suitable methods for their research, and a common approach is not applicable in many scenarios [ 103 ]. The phases of aviation demand forecasting methods are depicted in Fig. ​ Fig.4. 4 . Data can be collected from various sources, including airport and airline official websites. Since aviation demand is nonlinear and is affected by a variety of external factors and it is necessary to forecast passenger demand more accurately. It is crucial to assess and choose the influencing factor in the forecast. Influencing factors can range from social causes, such as football events or exhibitions, to economic reasons, such as the country’s GDP, population index, inflation, or travel restrictions. These variables can be introduced as an exogenous variable or as a multivariate into the model.

General framework of passenger demand forecasting

Data pre-processing is the next critical stage after data collection. The collected data may be susceptible. Data cleaning aids in the filling of the missing values, the treatment of outliers, and the removal of the noise if present. The Decomposition of time series data helps understand the data’s inner dynamics. Time series analysis can be used to study seasonality and trends in time series data [ 106 ]. Before forecasting techniques can be used, time series must satisfy tests such as the stationarity tests [ 27 ]. Normalization of the data is required due to the varying dimensions of data. Data is divided into testing and training datasets. Forecasting is the next major step after the data pre-processing. Aviation demand forecasting techniques vary from qualitative to statistical methods to Artificial Neural Networks. Each forecasting model must be evaluated using the measures like metrics like Mean Square Error (MSE) and Mean Absolute Percentage Errors (MAPE) [ 73 ].

Research methodology

This section presents the survey papers related to aviation demand, followed by the paper selection and exclusion methodology used in this study.

Existing surveys

Wang and Song [ 109 ] assess 115 works in air transportation research written between 1950 and 2008 and examine several factors, including research advancement, publishing journals, demand determinants, estimation techniques, and demand elasticity. His study revealed that the demand for aviation in the US region increased significantly during uncertain incidents such as the terrorist attack and the Sars Pandemic outbreak in the US. Another review by Addepalli et al. [ 2 ] examines the effects of socioeconomic and demographic factors, which are crucial for maintaining the demand for the aviation industry and boosting manufacturing productivity. Gosling and Ballard [ 44 ] and Gosling et al. [ 45 ] provide a summary report of academic and commercial research that forecasts the variation in U.S. air travel demand using disaggregated socio-economic data. Wang and Gao [ 108 ] evaluate 87 studies on demand for air travel that were published between 2010 and 2020 and provide an overview of each one based on its input data and main analytical techniques.

Table ​ Table2 2 presents the highlights of the period and focus areas in aviation demand forecasting in the existing surveys compared to the proposed work.

Comparison of state-of-the-art and proposed survey

Survey methodology

Figure  5 presents the search process for the articles for this survey highlighting the elimination and inclusion criteria. A comprehensive literature search was conducted using popular search engines, databases, and websites such as the social science citation index (SSCI), Google Scholar, ScienceDirect, and arXiv. The preliminary search string consists of “Aviation Demand,” “air transport,” “Passenger count,” “forecasting,” “drivers of aviation demand,” and a combination of these keywords. Second, the identified articles’ citations were tracked down—the scope of the articles to be reviewed.

Search process with elimination and inclusion criteria

The article identification phase presents the technique for identifying the articles based on a keyword search through the electronic databases and the manual search. Further, the second phase is the screening phase responsible for filtering out irrelevant articles. Phase three is the eligibility phase, responsible for shortlisting the articles based on their title and abstract. Final phase four is the inclusion phase, finalizing the article selection process for the quantitative and qualitative analysis.

Air travel demand forecasting is a field of applied research. The choice of an appropriate forecasting model is influenced by the past, various environmental variables, time-series data, and other factors. Since the 1970s, Statistical methods, particularly those based on the Box-Jenkins Auto-Regressive Integrated Moving Average (ARIMA) methodology [ 47 ]. Since the emergence of machine learning and its powerful regression methods, many models have been proposed to outperform the former, which have remained baseline methods in most research works. Deep learning-based strategies outperform traditional methods, and new architectures are being developed [ 89 , 94 ]. Figure  6 summarizes the different techniques used to forecast aviation demand.

Aviation Demand Forecasting Techniques

Econometric models

Econometric models [ 8 ] are statistical models. The econometric forecasting model is a method for predicting future events based on discovering relationships between economic variables. Econometric models are used to determine which economic factors influence aviation demand. The airline has no control over economic determinants. GDP, interest rate, currency conversion rate, the standard of living, income per capita, population size, and exchange rate are all Common economic determinants used in modeling. Economic variables are explainable independent variables. They are used to increase demand for aviation. Long-term forecasting can be done by using econometric models. There are three models in econometric modeling: cross-sectional, time series, and panel data.

The vast majority of econometric models are country-specific. These models are limited to the country’s major or minor airports. These studies help local governments determine whether to add a new hub to a specific airport. This model can help determine which economic factors influence passenger demand in a region, country, or city pair.

Suryan [ 99 ] researched to learn more about the economic factors influencing passenger demand in Indonesia. Indonesia’s primary industry is tourism. The government must develop a well-structured aviation infrastructure to encourage more passengers. Traditional regression models and panel regression models were used in the study. In this particular study, the economic considerations were not taken into account. Bastola [ 15 ] conducted the research in Nepal. The econometric model employed the total number of passengers flown as an input variable to estimate air passengers’ throughput. The number of passengers flown was the dependent variable, while the number of tourists who visited and the country’s gross domestic product was the independent variables. Air Passenger Demand Model (APDM) used simple and multiple regression models to forecast air passenger demand. The main challenge in predicting short-term air traffic demand is that determinants that influence the current econometric forecasting models do not account for economic crises, pandemics, and fuel prices.

Carmona-Benítez et al. [ 21 ] proposed an econometric dynamic to better investigate the dynamic nature of economic factors to understand the economic determinants of passenger demand better. This study was conducted in the Mexican air transportation industry. They suggested that EDM be measured using the Arellano-Bover method and verified using the Sargan and Arellano-Bond Autocorrelation tests. The panel data identified the economic determinants that influence air passenger demand by location, and these determinants explain pax demand patterns. As an explanatory variable for passenger demand, the EDM uses the total number of flights or airline frequencies at the airport. The passenger demand forecast considered states with more frequent flights. Holt-Winters method is used to predict econometric determinants. The passenger demand per location was expected using these determinant models. Developing a database per city to analyze the dynamic econometric model is a constraint. The disadvantage is that airports must be classified as hubs based on market conditions.

Endogeneity, or reverse causality, is a significant flaw in econometric models. A common remedy is to use instrumental variables in two or three-staged least squares regression. Lagged explanatory variables can be instrumental in addressing issues in time series models [ 49 ]. The Table  3 presents below the comparative analysis of the various models in the literature.

Comparative analysis of models in terms of data format, the method used, hardware/software and market

In a nutshell, econometric analysis is a type of multivariate analysis that investigates the relationship between independent economic, demographic, and passenger demand variables (price, personal income, GDP, and population size).

Statistical models

Time series forecasting is a technique for predicting future events based on historical data. Data observed at regular intervals, such as monthly, yearly, quarterly, hourly, and so on, is called time-series data. Time series forecasting aims to forecast how data will change—simple time series forecasting methods to predict the future based on a single historical event. When only one variable changes over time, univariate forecasting is utilized. Multivariate time series forecasting is used when multiple variables and their values change over time.

Statistical models are used to forecast aviation demand. The data’s stationarity must be checked before statistical modeling because statistical variables such as mean, variance, and autocorrelation should not change over time. Auto-Regressive Integrated Moving Average (ARIMA) and SARIMA [ 71 ] are state-of-the-art statistical models. Trend and seasonal factors are incorporated in aviation demand time series data.

Holt-Winter [ 32 ] is a statistical model that can deal with univariate time when seasonality and trend are present. Bermúdez et al. [ 16 ] presented a formulation for an additive Holt-Winters forecasting technique that simplifies the computation of point predictions. It helps to predict intervals by obtaining maximum likelihood estimates of smoothing parameters and initial conditions. Additive, uncorrelated, homoscedastic, and average errors were used to introduce the stochastic component of the model. Data transformation was used to improve the model training. The point forecast technique fails to account for low demand during the Gulf War.

Madhavan et al. [ 69 ] attempted to forecast aviation demand throughput in the Indian aviation industry. The bayesian structural time series (BSTS) approach is used. The traditional models were effective for short-term forecasting of general air passenger demand. Both international and domestic throughputs were considered in this study. Recommendations and research directions for both medium- and long-term projections of the Indian airline sector were also summarized.

Time series-based forecasting is difficult due to the increased uncertainty and irregularity of air passenger movement. Sun et al. [ 97 ] proposed a nonlinear vector auto-regression neural network (NVARNN) method to predict passenger throughput. First, the input features were identified and extracted using the mean impact value (MIV) way. The second NVARNN method is used to forecast for modeling purposes. The model is evaluated on the passengers at Beijing International Airport. According to this study, multivariate forecasting approaches consistently outperformed univariate forecasting approaches. Neural networks were preferable to SARIMA models due to their complexity. The scope of this study was limited to six factors influencing air passenger flow.

Machine learning models

In the last ten years, machine learning models have established themselves as serious competitors to traditional statistical models in forecasting [ 17 ]. Machine learning models have grown in popularity over the last decade, and researchers have investigated using them to increase the precision of time-series forecasts. The data constraint aspect of the problem has been partially resolved, and researchers have started investigating thousands of characteristics and cutting-edge machine learning models capable of handling big data applied in time series data for prediction accuracy [ 3 ].

Uncertain economic conditions and planners’ optimism or bias are the leading causes of forecasting errors. These mistakes may be responsible for ill-advised infrastructure and poor aviation management. Suh and Ryerson [ 95 ] developed forecasting models by combining prior prediction errors from similar airports, i.e., learning from a peer. The study also addressed concerns about a possible slowdown in passenger volume in the future. A binary Logistic Regression model was investigated to foretell the possibility of a drastic drop in passenger numbers. Regional socioeconomic changes can expect a sharp decline in airport demand. The forecast error is positive, implying that the predicted value is greater than the actual value [ 11 ].

Samli et al. [ 87 ] investigated passenger demand in the aviation industry using machine learning models such as Artificial Neural Networks, Linear Regression [ 30 ], Gradient Boosting [ 22 ], and Random Forest [ 7 , 117 ]. World Bank dataset was used in the study. This study will help identify the factors influencing international travel demand and participation in different regions. As features and models become more complex, the overfitting nature of models becomes a significant concern. Machine models do not reveal the temporal nature of the time series. The data for the Covid-19 pandemic was not considered due to the unique circumstances.

Time-series clustering techniques can be used for classification and forecasting. The relevant features were identified in a variety of ways and helped in the identification of common groups during training. Chen et al. [ 23 ] used K-means and decision trees to identify the factors influencing regional passenger demand. After identifying critical features, clustering can help to group airports and areas based on features. Identifying the feature was given more weight than the temporal nature of the time series.

Artificial intelligence models

Deep learning for time series prediction is a relatively new venture. It is a promising tool because of its flexibility. It helps in modeling highly complex and nonlinear temporal time series data without prior knowledge of the business’s functional aspects. Deep learning models captured the time series data and predicted the events more accurately than the competitors. Brause et al. [ 19 ] forecasted future airline passenger values in Indonesia using ARIMA and ANN. The main aim was to find the best-fitting model among ARIMA and ANN. The overfitting problem occurs in neural network models when the dataset is small. ARIMA model outperformed the neural network on both domestic and international passenger traffic. Gelhausen et al. [ 38 ]investigated the efficacy and applicability of Long Short-Term Memory (LSTM), Deep Neural Networks, and SARIMA forecasting approaches at Incheon International Airport. The study considered both tried and proven procedures. The study focused on monthly and weekly forecasting. Both SARIMA and LSTM techniques provided accurate forecasting and improved predictive capabilities. The LSTM model outperformed the SARIMA model in terms of prediction.

Machine learning techniques can help aviation to improve prediction performance, which relies on traditional statistical models. Time series analysis and deep neural network modeling are becoming more critical in aviation maintenance and operation. When it comes to selecting a suitable model, model accuracy is essential. As a result, it is vital to improving decision-making accuracy in fields such as time series forecasting and industries such as aviation. Deep learning techniques have the potential to improve accuracy, and the study [ 56 ] emphasizes the importance of employing the most up-to-date methods in the aviation industry. Iacus et al. [ 51 ] demonstrated how aviation demand is analyzed and modeled using traditional statistical models and deep learning techniques. The experiments showed that deep learning might help estimate aviation travel demand. Evolutionary meta-heuristic algorithms have been implemented to improve the performance of ANNs. The findings demonstrate that applying these methods accelerates the neural network (NN) prediction rate with actual data adaptation [ 75 ].

Hybrid models

Combination models have outperformed individual models in recent years. Researchers attempted to incorporate the strengths of both unique model architectures. It is critical to forecasting statistical indicators. A hybrid technique was used to indicate passenger demand in the airline industry. Xu et al. [ 116 ] introduced a new forecasting model that combines SARIMA and Single Vector Regression (SVR) models. SARIMA began by analyzing the time series and identifying the control parameters. The SARIMA model eliminated the series’ non-stationarity by employing seasonal differencing in proper order. SARIMA was used to find SVR input determinants. SVM was used to capture linear and nonlinear patterns in time series data. To improve the forecasting accuracy, a hybrid model was used. The forecasting model was only tested on short-term demand. External factors influenced aviation demand that was not considered during model construction. This study used historical data on statistical determinants as its data source. They found that the incorporation of noise may improve forecasting accuracy.

The support vector regression model is up-and-coming due to its ability to capture data nonlinearity. Stavelin et al. [ 94 ] investigated the characteristics of air passenger demand using a hybrid model. A combination of support vector regressor and particle swarm optimization (PSO-SVR) was used to forecast air passenger demand. First, historical airport passenger throughput data was used to extract conditional and decision attributes. Second, the particle swarm optimization algorithm maximized the kernel function variables and penalty attributes. Finally, the SVR machine was used to forecast the airport passenger throughput. External factors or economic determinants were not considered in this hybrid model’s forecast (Table ​ (Table4 4 ).

Pros and Cons of different forecasting techniques

Li [ 62 ] used a hybrid forecasting model that utilized ARIMA-Regression concepts and the IOWHA operator. Regression models were used to identify the independent variable that affects aviation demand. Independent variables such as inbound tourist count, gross national income, railway passenger count, and GDP are employed for multiple regression. ARIMA model was used to forecast the demand. ARIMA-based hybrid forecasting models outperformed the other models.

On the other hand, aviation demand exhibits significant nonlinearity and non-stationarity. Variable mode Decomposition (VMD) was used to simplify the original data by decomposing it into several mode functions. Using the unit root test, these modes were classified into stable and unstable series. Meanwhile, the ARMA and KELM models were used to forecast the stationary and non-stationary components. Finally, the result was integrated using a KELM model that incorporated all outcomes predicted. Passenger demands from Beijing, Guangzhou, and Pudong airports were used to evaluate the innovative model’s system performance [ 53 ].

Based on a prior study, several additional significant social media aspects could be used as influencers to forecast airline passenger demand. For example, sentiment analysis of different Twitter hash tags may reveal the existence of a particular event in the city of origin or destination of a flight, enhancing demand predictions. This feature extraction includes searching for specific keywords or sets of keywords, calculating their frequency, understanding the time and place, context, and so on. The use of social media features can have an even more significant impact on projecting aviation demand than economic and social factors for short-term forecasting [ 1 ].

DNN models show good behavior when the time series data is not linear. Deep learning is computationally very complex, showing the model’s tendency to overfit. A recent study found that hybrid models worked well and attempted to combine the best features of various models [ 115 ]. Complex deep neural networks are the future when time-series data contain uncertainty.

Performance evaluation

Goodness-of-Fit (GOF) tests compute the expected and actual value difference. These measures are used to compare the outcomes of different studies or competing models and to evaluate statistical hypotheses. GOFs are frequently used to assess the performance of forecasting models [ 6 ]. Evaluation measures are necessary to determine the quality of the trained model. Several techniques and algorithms can be used to assess how closely a forecast matches the actual result. Most of these techniques can be divided into two categories. These are scale-dependent and percentage-dependent errors. While percentage errors are measured in percentages, scale-dependent errors are measured in the number of mistakes. Most studies use scale-dependent errors such as mean absolute error (MAE), mean squared error (MSE), root represent squared error (RMSE), and normalized root mean squared error (NRMSE). The disparity between actual and anticipated values is called forecasting errors [ 107 ]. Forecasting errors are classified into two types such as: random and systematic.

Table ​ Table5 5 summarizes the performance measures used to evaluate aviation demand forecasting techniques. The most important performance measures are Mean Absolute Error (MAE), mean absolute percentage error (MAPE), Mean Square Error (MSE), Sum-of-Squares-for-Error (SSE), Root Mean Square Error (RMSE), Symmetric mean absolute percentage error (SMAPE), Mean squared prediction error (MSPE), Mean Forecast Error (MFE) and R-square. In Table ​ Table5, 5 , ✘ means the mentioned evaluation measure is not used in the study, while ✓ means the mention evaluation metrics are used in the study.

Evaluation of passenger demand forecasting in terms of performance measures

Comparison of state-of-the-art methods

It is clear from Table ​ Table5 5 that the most widely used evaluation metric is MAPE. A comparison of various MAPE values is presented from the literature, including the name of the best model of that research work in Table  6 .

Comparison of aviation demand model on MAPE

Total absolute forecast errors divided by actual values over time yield the mean absolute percentage error (MAPE). It’s a metric for assessing precision by looking at the percentage of mistakes made. Better forecasts can be made when the MAPE value is closer to zero.

Applications

The applications of passenger demand forecasting are tourism, revenue management, and infrastructure planning [ 96 ]. These are discussed in succeeding subsections.

Tourism industry

Tourism is an important industry that is inextricably linked to aviation. Tourism relies on air transportation to deliver visitors, and the air transportation industry relies on tourism to generate demand for its services. Estimating the volume of passenger transportation is an inseparable part of tourism demand forecasting and is an essential factor in the strategic planning of the tourism industry. Most air transport demand is derived; from tourism contributes to aviation demand [ 34 ]. Air travel and tourism are highly correlated and sensitive to economic, social, and political changes. The improvement in accessibility provided by modern airport infrastructure and the establishment of direct airline connections may attract tourists to a destination [ 77 ].

Revenue management

An airline’s goal is to maximize revenue by effectively matching the limited supply of seats with different segments of demand. The demand in the airline business is stochastic. Forecasting the demand (Forecasting Module) and then allocating available seats at other fares (Optimization Module) is at the core of Revenue Management (RM) systems. The accuracy of demand forecasts is crucial to increase the performance of RM systems. Revenue is improved by optimizing the pricing of each flight seat to make as much money as possible. Effective revenue management necessitates external understanding elements such as rivals and worldwide events. It also requires a large amount of historical data. This paper studies the optimal seat capacity problem between the source and destination [ 87 ].

Another area of revenue management is optimizing the profit by dynamically pricing the seat based on demand. A ticket sale does not cease when the available seats are depleted. The airline decides to overbook in anticipation of a cancellation or no-show. Overbooking can be accomplished by predicting passenger demand (Table ​ (Table7 7 ).

Applications of passenger demand forecasting

Infrastructure management

Strategic planning concerns infrastructure development, such as terminal capacity, new airports, or vehicle capacity. Long-term passenger demand necessitates a more detailed forecasting level in network resource planning and aims to optimize resource allocation across the network based on passenger loads in each area [ 13 ]. This is a complex problem to solve because resources must be carefully allocated according to demand on each route. The crew assignment problem is a part of network resource planning. Larger vehicles require more experienced crews and additional cabin crews [ 10 ]. The supply side, demand side, and market fluctuations have all been studied as influencing variables for the development of China’s aviation industry. Research shows that the proportion of air passengers is relatively significant and growing.

Current limitations and future research directions

This section discusses the central issue in aviation demand forecasting and future research directions.

Short-term forecasts encompass day-to-day operations; medium-term forecasts cover up to five years and include route-planning decisions; long-term forecasts cover more than five years and have an airport and airline infrastructure planning decisions.

Challenges associated with forecasting

Various challenges associated with forecasting have been discussed in this sub-section.

Short-term forecasting challenges

Short-term passenger gives vital input into daily operations management, aircraft scheduling, maintenance planning, advertising and sales campaigns, and opening new sales offices [ 114 ]. Therefore, data will be highly seasonal and unpredictable. The main challenge is predicting with accuracy. Xie et al. [ 114 ] found that the hybrid seasonal decomposition and least squares support vector regression approaches were used to forecast short-term air passenger demand. The seasonal characteristic and nonlinear nature of air passengers were exploited to improve forecast effectiveness.

Most airports forecast their short-term passenger demand based on experience. Short-term forecasting involves multiple seasonality and external data that drive the business. Indian aviation industry focuses on the BSTS model to incorporate uncertainty and delivers better accuracy than the others. A machine learning approach is proposed based on time series models, feature extraction, and feature selection techniques to improve short-term air passenger forecasting. A particular emphasis was placed on developing the modified version of Principal Component Analysis (PCA) to drive additional characteristics [ 26 ].

Long-term forecasting challenges

Long-term forecasting is required for strategic planning to see the aviation industry in a profitable position. Long-term forecasting accuracy is critical because it involves budget and new investment initiatives. Most studies address issues such as the feasibility of constructing a new airport in the city or recent infrastructure upgrades. In general, time series models are always helpful for long-term forecasting. More than one variable can be used as a predictor in multivariate forecasting. The model becomes inconsistent and biased when these predictors are associated with the error term. Empirical analysis demonstrates that the proposed hybrid approaches outperform other time series models in long-term complex time series with high volatility and irregularity [ 91 ] (Table ​ (Table8 8 ).

Challenges associated with passenger forecasting

Forecasting challenges with pandemic

Economic forecasting has become more divergent. The pandemic has introduced a great deal of uncertainty in all aspects of life. Factors such as the financial speed of policy changes in all sectors impact forecasting passenger demand directly or indirectly. Few studies have found that aviation recovered after the pandemic [ 52 ].

Future challenges

Aviation is driven by demand. It is vulnerable to shocks and fluctuations in the market. For example, a country’s geopolitical situation or poor economic condition can impact aviation demand. The business environment is categorized into two parts, such as, internal and external environments. The main factors in the internal environment are man, money, marketing, machinery, and management structure. External factors primarily drive demand; nevertheless, how a company responds to the external environment affects whether or not it will be successful. Cutting-edge forecasting techniques such as deep learning and hybrid models address these highly volatile situations [ 55 , 90 ].

Causality is commonly emphasized in forecasting studies. Models performed better in terms of reliability and accuracy when forecasting aviation demand. The economic, social, and natural disasters and airline service to airport services are all variables. However, most studies focused on economic factors. At the same time, the effect of financial shock or pandemic has received little attention. These challenges have been laid out for Aviation demand forecasting researchers.

Future research directions

Future research must take advantage of big data, and alternative modeling techniques should be explored to take advantage of big data. Numerous forecasting methods have been developed using emerging big data applications. But the use of those techniques should be extended to the aviation industry. A fascinating subject for future research might be the study of the creation of innovative demand forecasting techniques employing big data.

Critical social media features like sentiment analysis are another new influencer. Additionally, including dynamic data in forecasting techniques can improve forecast accuracy. If a model is accurate for a current system, it does not imply that it will be accurate for the same system in a year. Developing dynamic demand forecasting methods that can make decisions based on real-time data would assist airlines and related applications.

Discussions

This paper presents a comprehensive survey and considerable research for aviation demand forecasting. This survey has examined a wide range of the existing studies of air passenger demand by many researchers, as well as the methods generally used to forecast future markets and the external factors that influence future direction. Initially, the different factors that impact aviation demand forecasting were discussed. The techniques employed for passenger demand forecasting have been discussed. The most recent quantitative forecasting techniques are econometric, statistical, machine learning, artificial intelligence, and hybrid models. Various model evaluation metrics that have been employed in research studies are discussed. The current and future challenges related to aviation demand in the real world have been discussed. Different application areas where passenger demand forecasting is widely used are discussed. A comparative study of quantitative forecasting methods and their performance metrics evaluation may be considered in future reviews.

The proper forecasting technique is selected based on the history of the time series data, environmental factors, and the responsibilities of the airline and airport. Accurate projection is a critical criterion when using models to identify the right independent variable or determinant factors. Short-term forecasts perform better than long-term forecasts in terms of tracking changes in seasonal patterns and business cycles; however, short-term forecasts are volatile due to non-linearity and irregularity in time series data. Another problem for econometric models is endogeneity or reverse causality. The solution is to use instrumental variables. Traditional statistical models are the best fit for linear time series forecasting. It does not require a large amount of computational power. It is simple to use, and statisticians can understand the behavior and nature of the data. Due to the limitations of many traditional forecasting models, forecasting economic time series is typically difficult, prompting academic researchers and business practitioners to develop more effective forecasting models.

Conclusions

This study is a helpful guide and handy reference for other researchers interested in carrying out similar research on aviation demand. This study provides an overview of the major demand-influencing factors and demand-forecasting approaches by examining previous studies on aviation demand. For other academics interested in this topic, this study integrates the many challenges of forecasting aviation demand and might be used as a reference.

The significance of forecasting method accuracy is one observable trait in the manuscripts reviewed. However, there is little research on the use of accurate data for forecasting. Future studies must focus on big data and sentiment analysis in aviation demand forecasting. Developing dynamic demand forecasting methods that can make decisions based on real-time data will assist airlines and related applications such as tourism plans effectively and save money.

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Seasonally-Adjusted Transportation Data

Seasonal adjustment is the process of estimating and removing movement in a time series caused by regular seasonal variation in activity, e.g., an increase in air travel during summer months. Seasonal movement makes it difficult to see underlying changes in the data. Monthly shifts in data as well as short and long-term trends can be best seen through seasonally-adjusted data.

Seasonally-Adjusted Airline Data

• Airline Traffic Data Press releases
• Available Seat Miles (Domestic and International)
• Available Seat Miles (Domestic)
• Available Seat Miles (International)
• Enplanements (Domestic and International)
• Enplanements (Domestic)
• Enplanements (International)
• Load Factor (Domestic and International)
• Load Factor (Domestic)
• Load Factor (International)
• Revenue Passenger Miles (Domestic and International)
• Revenue Passenger Miles (Domestic)
• Revenue Passenger Miles (International)
• Revenue Passenger Miles
• Revenue Ton-Miles
• U.S. Department of Transportation, Bureau of Transportation Statistics (BTS), U.S. Air Carrier Traffic Statistics  (unadjusted)

Seasonally-Adjusted Vehicle Miles Traveled

• Seasonally-adjusted data
• U.S. Department of Transportation, Bureau of Transportation Statistics (BTS) calculation from U.S. Department of Transportation, Federal Highway Administration, Traffic Volumes and Trends
• U.S. Department of Transportation, U.S. Department of Transportation, Federal Highway Administration, Traffic Volumes and Trends  (unadjusted)
• Seasonally Adjusting Vehicle Miles Traveled

Seasonally-Adjusted Modal Data for the Transportation Services Index (TSI)

The monthly data used to create the TSI are highly seasonal. In order to portray real growth in the modal data, BTS uses a method called X12-ARIMA to seasonally-adjust the data.  Whereas the Total, Passenger and Freight TSI indicate the trends of the combined modes, the seasonally-adjusted modal data allow the user to understand which modes are experiencing the changes in trend over time.

Seasonally-adjusted Freight and Passenger TSI Modal Data

• Truck tonnage
• Rail Freight Carloads
• Rail Freight Intermodal Traffic
• Rail Passenger Miles
• Public Transit Ridership
• Pipeline Movement
• Natural Gas Consumption
• U.S. Waterways Tonnage
• Methodology: Seasonal Adjustment of the Transportation Services Index Time Series Data
• ARIMA model specifications
• Transportation Services Index

Documentation

• Seasonally-Adjusted Data: What it Really Means

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• Data Descriptor
• Open access
• Published: 20 January 2022

Global holiday datasets for understanding seasonal human mobility and population dynamics

• Shengjie Lai   ORCID: orcid.org/0000-0001-9781-8148 1 ,
• Alessandro Sorichetta   ORCID: orcid.org/0000-0002-3576-5826 1 ,
• Jessica Steele   ORCID: orcid.org/0000-0001-6741-1195 1 ,
• Corrine W. Ruktanonchai 1 , 2 ,
• Alexander D. Cunningham 1 ,
• Grant Rogers 1 ,
• Patrycja Koper 1 ,
• Dorothea Woods 1 ,
• Maksym Bondarenko   ORCID: orcid.org/0000-0003-4958-6551 1 ,
• Nick W. Ruktanonchai 1 , 2 ,
• Weifeng Shi   ORCID: orcid.org/0000-0002-8717-2942 3 &
• Andrew J. Tatem 1  

Scientific Data volume  9 , Article number:  17 ( 2022 ) Cite this article

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Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17833922

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Background & summary.

Human populations are increasingly mobile, across both high- and low-income settings 1 , 2 , 3 . This also has substantial impacts on population distributions and dynamics, economies, social development and planning 4 , 5 , 6 , 7 , 8 , 9 . Domestic and international movements both show significant seasonal variations across countries 10 , 11 . This seasonality of human mobility has been attributed to multiple socioeconomic and climatic drivers across the globe 12 . Among them, some determinants play a greater role than others, including school terms, religious festivals, and national holidays 13 , 14 . For example, major national public or religious holidays are associated with shifts in the scope of travel and drive particularly strong fluctuations. Increasing volumes of travel are also commonly found around Christmas in Kenya, Namibia, and the United States, while travel decreases during Ramadan in Pakistan 13 . The ‘Golden week’ holidays of the National Day and Lunar New Year in China have also witnessed massive domestic and international movements 15 . Additionally, the seasonal changes of population densities between the major holiday period (July and August) and more traditional working periods (from September to June) in Portugal and France revealed clear spatial patterns: most cities are characterized by a large decrease in population densities during the holiday period, whereas less-populated areas and well-known tourist sites show large increases 16 .

The directionality of seasonal movements may also change over the course of a year, with the relative importance of particular routes changing. For example, travel from urban to rural areas increases in December in Namibia, while reverse population movements returning to cities occur in January, suggesting travel from urban areas for Christmas and back after the holiday 3 , 13 . Additionally, human mobility also changes seasonally with school terms and breaks. For instance, the largest increase in travel volume happens around Christmas in Kenya, Namibia and the United States, in line with school holidays 13 . Air traffic further tends to peak during long public holiday periods and school breaks over summer and winter that may cross months 17 , and holidays have also been revealed to coincide with seasonal mobility patterns measured by travel surveys, novel data sources (e.g., mobile phone call detail records), and social media, among others 13 . For example, anonymous cell phone data have been used to evaluate the change in traffic patterns caused by holidays, and patterns varied each day due to holiday effects (before the holiday, during the holiday, and after the holiday) 18 .

The homogenization and synchronicity of holidays across large regions of the globe could further facilitate pathogen spread through increased travel connectivity during national holidays and school breaks 13 . In particular, the mobility across countries during the coronavirus disease 2019 (COVID-19) pandemic has demonstrated how fast countries could be reached by an emerging pathogen and new variants 19 , 20 , 21 , 22 . For example, it was estimated that 5 million people including workers and students left Wuhan in China before the Lunar New Year holiday in January 2020 23 . Conversely, the timing of holidays and school breaks and travel restrictions may also reduce the close contact in some population groups, and then mitigate the spread of pathogens 24 , 25 .

Public and school holidays are therefore one of the main factors determining seasonal changes in human mobility and population dynamics, subsequently affecting the transmission of infectious diseases and many socioeconomic activities. However, the dates and timings of holidays may vary across years. Comprehensive and contemporary datasets of historical public and school holidays for nations around the world and their changes over time are critical for understanding the seasonality of human domestic and international movements. This has many potential applications across disciplines, from travel estimation, transport planning and management, resource allocation, to public health service provision and monitoring efforts. Despite this, the worldwide data of public and school holidays across years since 2010 have not been assembled into a single time series, free to obtain and easy to use.

This study aims to overcome this data gap identified by producing global, temporally explicit datasets of public and school holidays across countries and multiple years. Specifically, an open access archive of comprehensive datasets of public and school holidays across the world at the daily, weekly, and monthly level has been created. To illustrate the usage of holiday datasets for understanding seasonal patterns of human mobility, these time series of holidays are also compared against the statistics of airline passengers by month from 2010 to 2018, assembled in this study, and a dataset of monthly international airline ticket bookings across the world in 2015–2019, used in a previous COVID-19 research 22 . All products are available through the WorldPop website ( https://www.worldpop.org/ ) 26 , 27 , 28 , 29 , 30 , 31 , 32 .

Five steps were taken to assemble and validate the holiday datasets: 1) collating national public holidays for countries/territories/areas across the globe from 2010 to 2019; 2) collating school holidays in 2019 and retrospectively generating the school holiday data from 2010 to 2018; 3) merging and aggregating data of public and school holidays to generate time series at the daily, weekly, and monthly level; 4) collating monthly statistics of airline passengers travelling domestically and internationally, compared with the seasonal distribution of holidays; and 5) using the Official Aviation Guide’s (OAG) global dataset of international air passenger ticket bookings across 223 countries/territories/areas from 2015 to 2019, for further assessing the correlation between seasonal mobility and holiday patterns. Figure  1 provides a schematic overview of the study design.

Schematic overview of the workflow to generate and analyse datasets. First, national public holidays from 2010 to 2019 were assembled, and school holidays in 2019 were collated to retrospectively generate school holidays from 2010 to 2018. Second, public and school holidays were merged to generate daily, weekly, and monthly time series. Third, the statistics and time series of monthly airline passenger numbers were collated to compared with the seasonal distribution of holidays across countries/territories/areas. Additionally, the Official Aviation Guide’s (OAG) global dataset of international air passenger ticket bookings from 2015 to 2019 were used for further assessing the correlations between seasonal mobility and holiday patterns.

Public holiday data

Public holidays, also referred to as national holidays, bank holidays, or official holidays in different countries or regions, are usually non-working days of celebration or commemoration during the year established by law. Sovereign nations and territories generally observe public holidays based on events of significance to their history, such as the anniversary of a significant historical event (e.g., the National Day) or a religious celebration like Diwali, Christmas, Hanukkah, Ramadan, etc. Moreover, public holidays vary by country and sometimes by year. They can land on a specific day of the year or be tied to a certain week of the month, such as Thanksgiving, or follow other calendar systems like the Lunar Calendar. To commemorate special events, there has also been a number of ad hoc public holidays that were announced on short notice (<4 weeks), such as the 2-week-ahead announcement of extra holiday in the opening ceremony for the 2017 Southeast Asian Games held in Malaysia 33 .

In this study, we define national public holidays as ones established by law or announced by the corresponding authorities. A standardized data collection form was used to gather information on public holidays on a country-by-country basis from 2010 to 2019, with variables including: the name and ISO 3166 alpha-3 code of the country or territory, name and date of the holiday, and type of the holiday (e.g., public holiday, observance, special holiday, and half-day holiday). We also collected information on special working days occurring on weekends or non-working days that were officially and temporarily taken as replacements of non-working days during the week, such as the 7- or 8-day ‘Golden week’ holidays in China.

To assemble this dataset, we systematically searched information on public holidays for each country or territory via Google by using the search terms: [Public OR Federal OR official OR bank] AND [holidays] AND [Name of the country or territory] AND [Year]. Where data for a given area were available from multiple publicly available sources, we prioritized the data from official central or federal government/authority websites. If such data did not exist through official websites, other websites with openly available data were also considered, including: the Time and Date ( www.timeanddate.com/holidays/ ), the Festivo ( https://getfestivo.com/countries ), the Office Holidays ( https://www.officeholidays.com/countries ), the Bhutanese Calendar ( https://www.bhutanesecalendar.com ), and the Nager.Date ( https://date.nager.at/ ). However, comparing with the data in the latter half of the 2010s, data spanning 2010–2014 were not widely available. We therefore identified missing data in the dataset by comparing the number of holidays by year for each region. For missing data on public holidays in a year that were tied to a specific day of each year, we interpolated the records into the dataset. For public holidays with variable dates across the years, we inferred dates where possible if they landed on a certain day of the week in a certain month or followed other calendar systems like the Lunar Calendar. Finally, we merged these interpolated and inferred public holiday within the final dataset 27 .

School holiday data

School holidays (also referred to as vacations, breaks, and recesses) are the periods during which primary and secondary schools are closed or no classes or other mandatory activities are held. The dates and periods of school holidays vary considerably throughout the world, and there is usually some variation even within the same jurisdiction, with governments sometimes legislating only the total number of school days required. In this study, we defined school holidays for primary and secondary schools only, excluding higher education, such as universities. Because short holidays or mid-term breaks commonly overlap with public holidays (e.g., the Easter or Thanksgiving), we focused on long school holidays with breaks lasting more than 2 weeks, e.g., summer and winter holidays between academic years. We similarly created a standardized form to collect and collate data, with the variables including name and ISO 3166-alpha3 code of country or territory, name of the school holiday (e.g., spring/summer/autumn/winter holiday, or break between school years), the first date and the last date of the holiday.

We systematically searched the information on school holidays for each single country or territory in Google by using the search terms: [school] AND [Holiday OR Break OR Term] AND [Name of the country or territory] AND [Year]. We prioritized data from official central or federal government/authority websites. If these data were unavailable at the country level, we collected information on school holidays for capital regions, announced by local governments or educational departments. For example, school holidays in China varied across provinces, so we therefore relied on information about school holidays within Beijing. For those countries without available data from official websites, we also searched publicly available data from websites including: the School Holidays ( https://school-holidays.net/ ), the Public Holidays Asia ( https://publicholidays.asia/ ), the School Holidays Europe ( https://www.schoolholidayseurope.eu/ ), and the Holiday Calendar ( https://holidaycalendar.com/ ).

Due to variability in dates of school academic years and terms across schools, regions and countries, median dates were used for discrepancies in beginning and end dates of school holidays across regions within a country for the same year. However, historical data on school holidays are not widely available from websites, and school breaks in each country vary by year, but generally occur during the same season. For example, Namibia and Kenya have school breaks in April, August, and December/January, and Pakistan has a single long break from July–August 13 . Therefore, we firstly collated information on school holidays in 2019, and then estimated beginning and end dates of school holidays between 2009 and 2018 using the same information from 2019. Of note, if the beginning dates in 2010–2018 were on Thursday or Friday, they were adjusted to the nearest Monday, and if the end dates in 2010–2018 were on Monday or Tuesday, they were adjusted to the nearest Sunday 28 .

Holiday time series

We then created time series on a daily basis for each country or territory from 1 January 2010 through 31 December 2019, and generated the fields of year, month, and week number in each year. The time series were merged with the public holiday data to decide whether each day of the year was a holiday or not. Similarly, this was merged with the school holidays data to identify whether the day was a school holiday or not. We added a variable (i.e., hl_sch) to indicate whether each day included a public or school holiday 29 . Finally, the daily time series were aggregated to generate weekly time series 30 and monthly time series 31 , by calculating how many days in each week or month contained school or public holidays.

Airline passenger statistics

To understand the impact of holidays on seasonal mobility and illustrate the usage of holiday datasets, we also collated monthly statistics of airline passengers travelling domestically and internationally, as censuses and surveys commonly do not collect the data of seasonal population movements across countries. The air travel data span 2010 to 2018 were systematically searched and collected from the National Offices of Statistics or Departments of Transportation across continents and countries 34 , 35 , 36 , 37 , 38 , 39 , 40 . We also used publicly accessible databases of airline passengers at the airport level from the Anna Aero ( https://www.anna.aero/databases/ ). All data were then aggregated from the airport level to national level. We merged data into a time series at the monthly level 32 , which included the following variables: ISO 3166-alpha3 code of each country or territory, year, month, total number of air passengers (obtained from statistics), number of internal air travellers, number of international air travellers, and the total number of airline passengers using data obtained from other sources such as Anna Aero.

As the statistics of airline passengers might not be available for all countries across the world, particularly in the low- and middle-income settings, we further used OAG’s global dataset ( https://www.oag.com/ ) of international travellers based on air ticket bookings from 2015 to 2019, for investigating the correlations between holidays and mobility for countries that were not covered by air traffic statistics assembled by this study. The OAG data of international traffic flows have been used in our previous study to understand the international spreading risk of COVID-19 at the early stage of pandemic from December 2019 to May 2020 22 . The data obtained from OAG are not publicly available due to stringent data licensing agreements, but the information on the process of requesting access to the dataset that supports the findings of this study is available from the corresponding author.

Data Records

The datasets of public and school holidays and airline passenger statistics assembled by this study are publicly and freely available through the WorldPop Repository ( https://www.worldpop.org/ ) 27 , 28 , 29 , 30 , 31 , 32 . A collection of these datasets with DOIs has been compiled and described in Table  1 .

Technical Validation

All data collected, assembled and used were (i) already validated by the corresponding data collector, owner and/or distributor, (ii) visualised to present their spatiotemporal and spatial patterns, and (iii) further quality-checked, in the framework of this project, for the synchronicity and correlations between holiday patterns and seasonal human mobility derived from air travel datasets.

Public and school holidays

Time series data of public and school holidays were assembled for 232 countries/territories/areas across the world, with noticeable seasonality from 2010 to 2019 (Figs.  2 and 3 ) and across regions (Fig.  4 ). We checked the number and seasonal patterns of holidays over years by country, as holidays generally occur at similar periods across years in each country. Our datasets present clear seasonality in holidays by country, but the timings of some holidays based on the Lunar Calendar, for example, change by year. However, some countries further move non-working days from the weekend to the weekday and combine with public holidays to allow for longer holidays. For instance, China has a ‘golden week’ with 7 to 8 days of national holiday, including the Chinese New Year in January/February, and the Mid-Autumn Festival and National Day in late September and early October, facilitating long-distance family visits (Fig.  2 ).

Heatmaps of weekly time series of holidays in 2010 and 2019. Each row in the heatmap represents a country/territory, sorted by the latitudes of their capitals from North to South. ( a ) and ( b ) present the number of holidays by week in 2010 and 2019 across the world, respectively. ( c ) and ( d ) show weekly holiday patterns for 16 countries in 2010 and 2019, respectively.

Heatmaps of monthly time series of holidays across the world in the 2010–2019 period. Each row in the heatmap represents a country/territory/area, sorted by the latitudes of their capitals from North to South. ( a ) The months with or without public and school holidays. ( b ) The days of holidays for each month.

Average number of public and school holidays by month and country/territory/area in the 2010–2019 period. The regions without data are filled with grey colour.

Winter and summer school holidays contributed markedly to holiday seasonality. Most countries have summer holidays or vacations as the longest break in the school year, lasting between 5 and 14 weeks. For example, summer holidays in Ireland, Italy, Lithuania and Russia normally last three months, compared to 6-8 weeks in the United Kingdom, and the Netherlands and Germany from June to August. The summer break in the southern hemisphere commonly lasts 6–8 weeks from December to February, overlapping with Christmas and New Year’s Day holidays, while the winter break in predominantly Christian countries in the northern hemisphere normally last for about 1–3 weeks surrounding Christmas (Fig.  2 ). Additionally, in countries with a history of Christianity, the Easter holiday is a school break that takes place in the northern hemisphere’s spring and in the southern hemisphere’s autumn, with the date varying by country and level of schooling (e.g., primary versus secondary). For South-East Asian countries celebrating the Spring Festival or Lunar New Year, there is also a long school break towards the beginning of the year, lasting between 4 and 6 weeks around January and February.

Holidays and seasonal population mobility

We collated the statistics of airline passengers for a total of 90 countries/territories/areas from publicly available data sources from 2010 to 2018 (Figs.  5a and 6 ), with the majority of countries in Europe, North America, and East Asia. Comparing air travel data obtained from official statistics versus other sources, we found slight discrepancies. This might be due to a number of factors, including: i) some countries, e.g., Australia and Canada, only reporting monthly statistics of traffic for major airports or airlines; or ii) duplication of air passengers due to data collection from a variety of data sources. For instance, those airport-level data including total number of incoming and outgoing passengers had being aggregated from airport level to national level, and domestic passengers being at more than one airport in the same country might be counted twice, especially in geographically vast countries, e.g., USA, Canada, or China. To overcome these issues, we only used data from other sources at the airport level for countries and years without official statistical data available, and then transformed the actual monthly traffic data to relative values by ranking monthly volumes of airline passengers within each year and country. We found that more people travelled around July – August in the northern hemisphere, while a high volume of air travel occurred in July – August and December – January in the southern hemisphere (Fig.  6 ). These seasonal patterns demonstrated high correlations between human mobility and the duration of public and school holidays, for both domestic and international travel (Figs.  7 and 8 ).

The availability of seasonal air travel data. ( a ) Airline passenger statistics in 90 countries/territories/areas, collated by this study from openly available data sources. ( b ) The availability of international air travel data based on passenger bookings, obtained from the Official Aviation Guide (OAG). The regions without data in maps are filled with grey colour. ( c ) The correlation between the statistics of international airline passengers assembled in this study and OAG data. The air travel data are presented as the proportion of international travellers among each year and country/territory/area. The green solid line represents linear regression fit, with p and R 2 values provided.

Seasonal patterns of holidays and air travel for regions with available airline passenger statistics in 2010–2018, assembled by this study. ( a ) Days of holidays in each month. ( b ) The seasonality of holidays, presented by the average number of days of public and school holidays in the same period across years. ( c ) The rank of monthly volume of domestic and international airline passengers. Months with higher volumes have a higher rank (from the lowest to the highest: 1–12) in each year. Months without data are coloured white. ( d ) The seasonality of air travel, presented by average rank of airline passenger numbers for the same period across years. Each row in the heatmap represents a country/territory/area, sorted by the latitudes of their capitals from North to South.

Boxplots of the monthly volume of airline passengers by the duration of holidays across the world. ( a ) Airline passenger statistics in 90 countries/territories/areas, collated by this study from openly available data sources. ( b ) International air travel data based on passenger bookings, obtained from the Official Aviation Guide. The monthly volume of air travel was transformed as the rank. Months with higher volumes of airline passengers have a higher rank (from the lowest to the highest: 1–12) in each year and country.

Correlations between the duration of holidays and the volume of air travel by month, 2010–2018. ( a ) Domestic and international travel, ( b ) domestic travel, and ( c ) international travel, based on the air travel statistics collated by this study from openly available data sources. ( d ) International air travel data based on passenger bookings, obtained from the Official Aviation Guide. The monthly volume of air travel was transformed as the rank. Months with higher volumes of travellers have a higher rank (from the lowest to the highest: 1–12) in each year and country/territory/area. The colour of each tile means the proportion of months in each air travel rank over the total number of months in each category of the duration of holidays across the world. The Spearman’s rank correlation coefficients (ρ) and p values are provided to assess the relationship between the duration of holidays and the volume of air travel.

However, only limited data of air travel statistics across multiple years were available for countries in Africa, South America, West and Southeast Asia (Fig.  5a ). To overcome this limitation, an extra dataset of global international air travel covering almost all countries from 2015 to 2019 were obtained from the OAG (Fig.  5b ). We found that this dataset highly correlated with the statistics of international airline passengers assembled in this study (Fig.  5c ). The OAG dataset also showed a clear seasonal pattern and there were more people travelling during the period of longer holidays, i.e., July – August across the world and December – January in the southern hemisphere (Figs.  9 and 10 ). A significant Spearman’s rank correlation coefficient was also found between international travel and holidays across the world (Figs.  7b and 8d ).

Average rank of international air travel by month and country/territory/area in the 2015–2019 period. International air travel data were based on passenger bookings, obtained from the Official Aviation Guide. The regions without data are filled with grey colour.

Seasonal patterns of holidays and international travel for air passengers across 223 countries/territories/areas from 2015 to 2019. ( a ) Days of holidays in each month. ( b ) The seasonality of holidays, presented by the average number of days of public and school holidays in the same period across years. ( c ) The rank of monthly volume of international air travellers, obtained from the Official Aviation Guide. Months with higher volumes have a higher rank (from the lowest to the highest: 1–12) in each year. Months without data are coloured white. ( d ) The seasonality of air travel, presented by average rank of traveller counts for the same period across years. Each row in the heatmap represents a country/territory/area, sorted by the latitudes of their capitals from North to South.

Usage Notes

The archive provides ready to use time series at daily, weekly and monthly temporal resolutions and at national spatial scales. This compilation of datasets can facilitate a variety of uses across settings, from quantifying and predicting seasonal population movements, to modelling disease transmission dynamics and interventions, as well as air traffic predictions and estimation of their socioeconomic impact. For example, using the holiday datasets assembled in this study, a recent research has explored how a set of broadly available covariates can describe the seasonal dynamics of population movements in Kenya, therefore enabling better modelling of seasonal mobility across low- and middle-income settings 41 . They found that Kenyan mobility peaked in August and December, closely corresponding to school holiday seasons, and the holiday was found to be an important predictor in the model. Additionally, we can quantify the contribution of holidays on seasonal population mobility derived from traditional or new data sources, e.g., mobile phone call detail records and internet check-in location history data, and statistical and mathematical models using holiday data can be built to predict future mobility across space and over time. Moreover, understanding and predicting human movements using these data should ensure other relevant covariates are used, e.g., temperature and tourism activities. For instance, summer is the most popular season for mobility in most countries in Europe due to two factors: i) the summer months, and particularly August, are those when most people or families traditionally go on holidays, when many activities are closed (e.g., education) or have reduced activity (e.g., manufacturing); ii) the warm temperatures are a very important pull factor for holidays in the majority of these regions. Nonetheless, there are some exceptions. The winter season is popular in some alpine regions due to favourable natural conditions for winter sports/activities, such as skiing. Lastly, domestic and international travel restrictions and social distancing policies aimed at containing outbreaks will likely significantly alter mobility patterns, regardless of climate and holiday factors, and should therefore be accounted for in any models using these data 25 , 42 .

Of note, week numbers in the weekly time series datasets were calculated by year and contain a week 0 for some years; the days of that week should therefore be included in the last week of the preceding year. Further, some countries combine public holidays with weekends to create 3-day or longer holidays, and these holidays may have a stronger impact on mobility than single-day holidays. Lastly, working days or the weekend are not identical across the globe. For example, Nepal has a six-day working week from Sunday to Friday, and the weekend in many Middle East and North Africa countries occur on Friday and Saturday. These country level nuances and timings of holidays with weekends should be accounted for where possible when performing single country analyses.

These analyses and data are subject to some limitations. Firstly, not all data are accessible from official websites or other publicly available sources, especially the holidays in the first half of the 2010–2020 period. We therefore interpolated data for these time periods based on reoccurring holidays across years, where possible. However, it is possible we did not accurately reflect those changes due to the replacement of holidays on the weekend by moving them from weekend to workday. Secondly, we calculated median dates for the beginning and end date of school holidays nationally, which might not represent the actual duration of school breaks at subnational or local levels. Additionally, many schools have the flexibility to adjust their school terms dates, e.g., adding inset days or as a result of snow days, but our datasets will likely not reflect these changes, as they occur organically through individual schools, events or jurisdictions. Thirdly, only air traffic data were collated to understand the seasonality of human movements and potential applications of the public and school holiday datasets. Other traditional data sources such as travel surveys, combining with data from novel sources, e.g., mobile phone data, social media, and internet check-in data, might be valuable in more accurately capturing human mobility across various temporal and spatial scales. In future research, these data sources can be used to better examine driving factors of human mobility, including identification of public and school holidays, public health interventions, natural disasters, and climate changes, among others.

Code availability

R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria) was used to manage data and perform analyses in this study. The code used to generate datasets and plots is available for download from the repository on GitHub at https://github.com/LaiShengjie/Holiday .

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Acknowledgements

The authors would like to acknowledge the statistical offices and data providers for providing openly available relevant data making this research possible. This work was principally funded by the Bill & Melinda Gates Foundation (grant numbers: OPP1134076, INV-024911). S.L. was also supported by grants from the National Natural Science Fund of China (81773498) and the National Science and Technology Major Project of China (2016ZX10004222–009). A.J.T was supported by funding from the Bill & Melinda Gates Foundation (OPP1106427, OPP1032350, OPP1134076, OPP1094793), the Clinton Health Access Initiative, the UK Foreign, Commonwealth and Development Office (FCDO), and the Wellcome Trust (106866/Z/15/Z, 204613/Z/16/Z). The funders had no role in study design, data collection and analysis, decision to publish, and preparation of the manuscript.

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S.L., A.S., J.S., and A.J.T. conceptualized the study. S.L. designed and coordinated the study, undertook data collection and assembly, drafted the manuscript. A.S., J.S., C.R., A.D.C., G.R., P.K., D.W., M.B., N.W.R., and W.S. aided the technical validation of datasets, commented and revised on the manuscript. A.J.T. aided drafting the manuscript and conceived the study. All authors read and approved the final version of the manuscript.

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Lai, S., Sorichetta, A., Steele, J. et al. Global holiday datasets for understanding seasonal human mobility and population dynamics. Sci Data 9 , 17 (2022). https://doi.org/10.1038/s41597-022-01120-z

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While five tropical waves were noted in the National Hurricane Center’s weather discussion on Monday, June 24, NHC meteorologists gave only one a minimal chance of becoming a tropical cyclone over the next seven days.

For now, the unassuming knot of showers and thunderstorms is a few hundred miles east-southeast of the Windward Islands, but there could be slow development once it reaches the western Caribbean late this week, NHC forecasters said. The next names on the 2024 hurricane list are Beryl and Chris.

"This robust tropical wave is booking it below a belt of strong upper-level winds and dry Saharan air," Lowry said on social media.

About 60 tropical waves leave the coast of Africa each hurricane season. One in 10, on average, develops into a depression, named storm or hurricane.

Meteorologist Ana Torres-Vazques with the National Weather Service in Miami said the Saharan dust may approach the Florida Straits by Thursday but it’s too early to say how much or where exactly it will go .

“That will evolve over time and depend on the weather pattern,” she said.

Hurricane season 2024: Rapid intensification forecasts improve, and then there was Otis

Despite forecasts for an overachieving hurricane season , the first few weeks have had just the short-lived Tropical Storm Alberto formed on June 19. Although Alberto was no more than a weak tropical storm, it pushed a dangerous flooding storm surge into Texas and killed four people in Mexico, according to the Associated Press .

Alberto formed amid a flurry of activity in the Gulf of Mexico and waters surrounding Florida that included two areas that didn’t have the oomph to make it to a tropical depression before reaching the coastline.

Through June 19, the Northern Hemisphere measured an accumulated cyclone energy, or ACE, of about 12, which is the third lowest for that time period since 1950, according to Colorado State University senior researcher Phil Klotzbach.

Accumulated cyclone energy is the measure of a storm’s longevity and vitality.

AccuWeather lead hurricane forecaster Alex DaSilva said the season so far has mimicked a typical year, although it may seem achingly slow because there have been several recent past years that came racing out of the gate. In 2023, tropical storms Arlene, Bret and Cindy formed in June. Two years earlier, in 2021, three tropical storms — Bill, Claudette, and Danny — and Hurricane Elsa formed in June.

But, on average, the first named storm of a season forms on June 20, DaSilva said. Alberto formed June 19. The second named storm doesn’t typically form until July 17, with the third trailing on Aug. 3.

“It’s not uncommon to get through most of June and July with two or three storms,” DaSilva said. “It’s basically a storm a month until mid- to late-August and September when you really start to rattle them off.”

2024 hurricane season: 5 tips to stay sane and safe in face of frightful forecast

Lowry said strong upper-level winds and Saharan dust are contributing to the lack of activity. The Central American Gyre, which spawned Alberto and Invest 93-L, which caused heavy rainfall over northeastern Mexico before dissipating, has also “retreated”, Lowry said.

“Mother Nature is taking a break for the next seven days, at least,” wrote FOX Weather hurricane specialist Bryan Norcross in a social media post made before the 2 p.m. forecast by NHC identified what could become Beryl.

Kimberly Miller is a journalist for The Palm Beach Post, part of the USA Today Network of Florida. She covers real estate and how growth affects South Florida's environment. Subscribe to The Dirt for a weekly real estate roundup. If you have news tips, please send them to [email protected].   Help support our local journalism: Subscribe today.

The 5 states with the worst, best traveler's airplane etiquette

Sitting next to someone with poor airplane etiquette can easily ruin your flight, and the U.S. state you’re flying out of may determine that.

From the seat in front of you reclining into your lap to a passenger who hogs both armrests — and isn’t in the middle seat — there are a number of behaviors that can bother your fellow travelers. In a recent report by Solitaire Bliss, 78% of the 2,002 Americans surveyed in April said they feel air travel etiquette has worsened over the past few years.

The participants cited the most common inconsiderate behaviors in airports and airplanes they’ve seen being people reclining seats without asking, using phones loudly in the terminal, and placing bags on terminal seats. 

The survey asked what bad behaviors people have seen and if they are guilty of committing these air travel offenses — and many admitted they do. With survey respondents coming from every U.S. state, the report determined the states with the most polite and inconsiderate fliers. (Yes, these are blanket statements, and both good and bad fliers are everywhere.) The states were ranked on a scale of 0 to 100, with higher scores indicating a greater tendency among travelers from that state to have poor air travel etiquette.

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“With the summer travel season here, we’ll be seeing an influx of stories on the media circuit about poor passenger behavior and airline issues,” Assaf Cohen, founder of Solitaire Bliss, said in an email statement to USA TODAY. “Along with common behaviors like taking up a seat in the terminal using a bag, or reclining a seat without asking, one in eight passengers report having seen a physical altercation.”

Read below to find the top five states with the best and worst travel etiquette.

States with the worst travel etiquette 

3. Virginia

2. Illinois

With the highest score of 94.29, Iowa’s survey respondents were the most guilty of poor travel etiquette, like putting their bags on the terminal seats and passing gas on the plane. Virginia’s top bad habits when flying included placing their luggage on the terminal seats, not putting their phones on airplane mode, and reclining their seats without asking. In Illinois, one in four residents admitted they ignore the seatbelt sign when flying. 

States with the best travel etiquette

2. Wisconsin

1. Arkansas

Call it “Southern hospitality” or “Midwest nice,” but the states that reported the least rude air travel behaviors were along the South and Midwest. Arkansas stole the top spot for having the most well-mannered passengers — or at least those who did not admit to their offenses.

Kathleen Wong is a travel reporter for USA TODAY based in Hawaii. You can reach her at [email protected] .

Delta Air Lines bringing back Cleveland nonstop route to western hub city

• Updated: Jun. 24, 2024, 10:11 p.m. |
• Published: Jun. 24, 2024, 9:59 a.m.

Delta Air Lines is adding back Cleveland to Salt Lake City service in November. (AP Photo/Tony Gutierrez) AP

• Susan Glaser, cleveland.com

CLEVELAND, Ohio – After a four-year hiatus, Delta Air Lines is relaunching nonstop service between Cleveland Hopkins International Airport and Salt Lake City.

The route was suspended in 2020 during the COVID pandemic, although the carrier briefly resumed the flight in early 2022 before canceling it again. Since then, Cleveland Hopkins officials have been eagerly advocating for its return.

The route will relaunch on Saturday, Nov. 23, and will run daily. Tickets are on sale now.

Flights from Cleveland will depart at 7:45 a.m., landing in the Utah capital at 10:18 a.m. Flights from SLC will depart at 5:20 p.m., landing in Cleveland at 10:57 p.m.

Salt Lake City is a hub for Delta, offering connections to dozens of cities throughout the Western United States, Mexico and Canada.

The carrier will fly a 160-seat Boeing 737-800 aircraft on the route.

Frontier Airlines also flies nonstop between Cleveland and Salt Lake City, one of a more than dozen new routes launched by the ultra-low-cost carrier from Hopkins earlier this year. Frontier’s service, however, is seasonal, and offered three times per week.

Delta also recently announced that it is returning nonstop service to Salt Lake City from Pittsburgh. The carrier relaunched its Columbus-to-Salt Lake City route earlier this month.

In 2023, Delta was the fourth largest carrier at Hopkins, carrying about 15% of all passengers. Delta also flies nonstop from Cleveland to Atlanta, Boston, New York City, Minneapolis and Detroit.

“We are thrilled our partner at Delta Air Lines will be resuming this service to one of our top underserved markets,” said Bryant L. Francis, Cleveland’s director of port control. “This addition not only offers Clevelanders nonstop service to Salt Lake City, but also provides dozens of additional connecting points beyond.”

More about Cleveland Hopkins Airport

• You can now reserve a parking spot at Cleveland Hopkins airport
• Spirit Airlines suspending Cleveland to Los Angeles route
• Below-70-degree June days in Cleveland are not unusual, but getting less frequent

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Alaska Airlines launches seasonal, daily flight between Portland and New Orleans 

• June 18, 2024
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Our new route becomes the first nonstop flight to the “Big Easy” from Portland   

Alaska Airlines is continuing to expand the destinations it flies from Portland with the launch of the first nonstop flight to New Orleans beginning this January. Our daily service will operate until next spring, including during Mardi Gras—New Orleans’ largest annual celebration. Guests can purchase tickets starting June 19 on alaskaair.com. 

As the largest carrier in Portland for more than 20 years, it’s important we continue to expand the nonstop destinations we offer our guests and give them choices when planning their next trip,” said Kirsten Amrine, vice president of revenue management and network planning for Alaska Airlines. “We can’t wait to offer another convenient way to connect our guests along the West Coast to New Orleans, a city rich in history and culture.” 

The vibrant city of New Orleans will be Alaska’s 55 th nonstop destination from Portland International Airport when service begins in January. The daily flight will conveniently depart PDX in the morning on our mainline aircraft for guests to enjoy an afternoon in the Big Easy and return to Portland in the evening. 

“We are thrilled that Alaska Airlines is launching nonstop service from New Orleans to Portland,” said Kevin Dolliole, Director of Aviation for Louis Armstrong New Orleans International Airport. “Portland has been the top unserved destination from New Orleans, and this new route not only strengthens the connection between our vibrant cities but also underscores our commitment to enhancing the travel experience for our community and visitors alike.” 

Portland – New Orleans service 

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We’ve grown our PDX presence with new routes, including daily, nonstop flights to Nashville that began this spring and to Atlanta, which is scheduled to begin on Oct. 1. We’ve also kept convenience in mind for guests traveling to and from Portland by significantly adding more flights throughout the day to some of our existing and popular destinations – bringing us to an average of 100+ daily departures from Portland this summer, including Anchorage, Ontario, Reno and Santa Rosa.  

“Alaska’s continued investment in PDX is great news for our travel community. Until now, New Orleans was one of the largest U.S. markets without a nonstop from PDX,” said Dan Pippenger, chief aviation officer at the Port of Portland. “Tens of thousands of travelers already fly between these two great cities every year, and we expect that number to only grow with this new nonstop service.”      

We’re excited about our future in Portland where we’re hard at work designing our new Alaska Lounge. It’s currently scheduled to open in the 2026 timeframe with almost 14,000 square feet of space that will provide nearly double the seating of our current Lounge spaces. Lounge members and guests will enjoy a barista station with hand-crafted espresso beverages and drip coffee from Stumptown; complimentary beer, wine and house spirits; our signature Loungers to relax in; and a custom fireplace.   

All our guests—whether in Portland or across our expanding network—can take advantage of a premium travel experience on their next Alaska flight. We are the West Coast’s premier airline offering our flyers the most legroom in First Class* and Premium Class; no change fees; multiple fare offerings; the most generous loyalty program with the fastest path to elite status; 30 Global Partners; and West Coast food and beverage on board.    

* Out of any U.S. legacy airline excluding lie-flat seats        

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Best Time to Visit Russia

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Want to travel to Russia but not sure where to start? Travel to Russia is a little more complicated than going to the Dominican Republic or France because of the following reasons. 1. Russia is far away. 2. Russia is cold. 3. You will need a visa to travel to Russia.

When to Go on Your Long-Awaited Trip to Russia

Don't worry! You've come to the right place. We'll help you understand Russian weather and find the best time for you to travel to Russia . Let's start with a brief overview of weather averages in Moscow & St. Petersburg.

Find out in the video above why traveling to Russia in summers is an excellent choice!

Weather in Russia

Next to consider when planning your travel to Russia is temperature. Russia is most beautiful in winter. After all, it is a northern country (coldest in the world actually) that spends 8/12 months in winter and knows how to make the most of it. If you travel to Russia in winter, you can save lots of money & expect a better service, & get to do many fun things, such as troika rides. If you don't like the cold, your safer bet is to travel to Russia next summer.

Don't try to fool the weather and travel to Russia in inter-seasons. You might be in for a big surprise. Of course, you will travel to Russia for different reasons than Napoleon, but you should know that snowing starts in October and ends in April! Or you could just ask your travel agent who is helping you to organize your travel to Russia to ensure that you spend as little time as possible in the snow.

Weather Averages in Moscow, Russia

Weather averages in  Moscow  fluctuate significantly depending on the season. July and August are the hottest months in the Russian capital.

The average temperature stays at 19°C (65°F) and often surpasses 30°C (86°F). Due to the continental climate and megapolis environment, peak temperatures often feel boiling hot. The coldest month in Moscow is January with an average temperature of -8°C (18°F).

Tip: the best time to visit Moscow weather-wise is late spring-early June. This is the period when the weather is the most convenient for long hours of sightseeing.

Weather Averages in Saint Petersburg, Russia

Weather averages in  St Petersburg  are generally lower than for Russia's capital, Moscow. July & August are the hottest months in St Pete's (and also have the longest daily sunshine hours.

The average temperature during summers is usually at 18°C (64°F). However, sometimes it gets really hot and thermometers climb up to 30°C (86°F). The coldest month obviously is January with an average temperature of -6°C (22°F). The wettest months are October & November.

Tip: the best time to visit St. Petersburg weather-wise is late spring-early June. This is the season when the weather is the most convenient for long hours of sightseeing.

The Best Time for Flying to Russia

There are few flights, so airfares fluctuate depending on seasons (New York - Moscow flight is $450 - $1200). It's cheapest to Travel to Russia in November, March and late August and most expensive during summers.

Overview of Travel Seasons in Russia

The high season for traveling to Russia is May through October. This is the best time to  tour Russia  and admire its masterpieces like the fountains of  Peterhof ,  parks of Pushkin , and the quaint countryside of the  Golden Ring . However, during summers main tourist routes are crowded and sometimes even impossible to access. So the best time to visit Russia is in inter-seasons like late spring and early fall. In addition, if you are brave enough, visiting Russia in winter can be just as rewarding if not more. Watch the video on the left and discover what's traveling Russia in winter like.

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Moscow in Winter: Weather, What to Pack, and What to See

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Travelers who enjoy bundling up for a snowy climate will appreciate all that Moscow has to offer, come winter. This ornamental Russian city shows its vibrant cultural heritage when temperatures plummet and the snow starts falling on Red Square. Unlike other cities, whose residents scurry away to hibernate in December, the people of Moscow embrace their sub-zero climate in style. They don their furs and ushanka hats (traditional hats with earflaps) to peruse Christmas markets, dine out at restaurants, and attend the opera.

In winter, Moscow's scenery looks awe-inspiring under a dusting of snow. There's something undeniably charming about seeing historic sites like the colorful, tented rooftops of Saint Basil's Cathedral capped with a layer of icy frost. The food here is warm and comforting, and the cultural winter events are not to be missed. Plus, it's cheaper to visit Moscow during the winter and it's far less crowded with tourists.

Moscow is not where you go to get a suntan over the holidays. In fact, the Moscow winter is enough to chill any hearty tourist to the bone—but that's all part of the fun. The average high for December, according to the National Oceanic and Atmospheric Administration (NOAA), is 27 degrees Fahrenheit (minus 2.7 degrees Celsius); for January, it's 23 degrees Fahrenheit (minus 5 degrees Celsius); and for February, it's 26 degrees Fahrenheit (minus 3.3 degrees Celsius). Take these average temperatures with a grain of salt, however, as it certainly isn't abnormal for the air to dip into the teens.

The Moscow cold is often accompanied by generous amounts of ice and snow deposited by frequent winter storms. The city goes unfazed by these storms—cars still drive around and people in boots trod through the snowpack. You'll see thick icicles growing on roof overhangs, so be sure not to linger underneath them while you're out touring the area's magnificent cathedrals.

Lastly, don't be surprised if your flights in or out get canceled or delayed. This can be one of the drawbacks to traveling in the winter, in general.

What to Pack

Stuffing your suitcase with bulky (and heavy) winter clothing can be frustrating and expensive, which tends to deter Moscow winter travel altogether. A trip to the city between early December and late February requires enough accessories to cover the extremities: wooly hats, cold-weather socks, knit scarves, and a good pair of gloves. Also pack a coat that falls below the hips, weatherproof boots, and ski pants, if you have them. Remember, fashion is second only to avoiding hypothermia in this polar city.

The Moscow winter calendar is brimming with cultural events for travelers to attend each winter. Many events take place during the Christmas holiday; then, the city caps off the winter with a farewell festival, come February.

• The annual Russian Winter Festival , which spans an entire month starting mid-December, takes place in several locations throughout the city. Head to Izmailovo Park or Revolution Square to see everything from over-the-top ice sculptures to traditional dance performances. Watch the professional ice skaters and visit food trucks that serve traditional fare.
• Moscow's New Year’s Eve celebration is one of the city's biggest events of the year. Tens of thousands of people spend it in Kremlin—Moscow's central complex—watching the Kremlin tower strike midnight, while fireworks crack in the background. Others attend the Christmas tree light show at Red Square.
• Christmas in Russia falls on January 7, and the week between New Year’s Eve and Christmas Day is a time for Russians to relax. Families focus on spending time together at home, preparing traditional foods like ukha (fish soup) and sauerkraut. Tourists can use this uncrowded time wisely by seeking out the city's culinary gems. Make sure to check the hours of operations on restaurants, shops, and other businesses before visiting them during this week. While much of the city's businesses might be closed, you might get special treatment at the places that stay open.
• Maslenitsa , Russia’s farewell-to-winter festival, occurs in late February or early March. This pagan celebration is marked by games, contests, and cultural traditions. It’s held in the Red Square area every year and draws crowds of Muscovites and visitors alike.

Winter Travel Tips

• In order to obtain a Russian travel visa, you'll need to be invited by a relative or friend who is a citizen or a hosting tour company.
• Traveling to Moscow in the winter helps you avoid the summer crowds; however, flight delays due to weather are common. Plan an extra day on either end of your trip in case you get held up.
• If you plan to visit a Russian banya, a Slavic steam bathhouse, take note that most people bathe in the buff. However, most bathhouses are separated by sex.
• Plan alternating indoor and outdoor activities so that you don't get too cold. A visit to the Tretyakov Gallery, the State Armory Museum, or the Pushkin Museum of Fine Arts provides a nice respite from the frigid temperatures.

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