150115 What is AirMarkets

© 2015 AirMarkets Corporation. All Rights Reserved 1

The AirMarketsTM Microsimulator

What is AirMarkets? A Brief Discussion Introduction AirMarkets Corporation provides clients with forecasts of global air transportation demand and supply, using a state-of-the-art computational platform called the AirMarkets Microsimulator (AirMarkets). Our clients include aircraft manufacturers, scheduled and on-demand fleet operators, airport authorities and managers, investors, and federal, state and local government strategists who need empirically valid data for decision-making. The kinds of decisions for which these data are valuable include new aircraft development programs; fleet purchases including fleet mix considerations; air transport service network design and revenue management; airport investments and operational changes; airport vendor firm location strategies; fuel and maintenance service planning; and new air service business models. The unique value of the analysis generated by AirMarkets results from its ability to represent consumer behaviors in a form that enables asking and answering strategic “What if?” questions about the effects of future trends in air transportation across the spectrum from the tactical and very local to the strategic and global domains. In this briefing document, we provide a high-level description of AirMarkets, the underlying science and technology, and value proposition for your applications.

© 2015 AirMarkets Corporation. All Rights Reserved

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What, Exactly, Is AirMarkets? AirMarkets is a computer microsimulation platform that reproduces the air travel decision-making behavior of passengers. These passengers may be flying in a directed network of scheduled air services or in a random network of on-demand services to move among city-pair travel markets. Directional networks consist of repeating schedules of flights from defined origins to destinations. Random networks feature changing schedules and changing origins and destinations. The platform generates estimates of the demand, flight load, and revenue for a specific flight or set of flights offering service from any city to any other city in the world, scheduled or on-demand, under any pricing policy, with existing or theoretical aircraft performance and price configurations. These estimates can be in the forms of numbers of passengers or revenue totals, or can be the probability distributions of those values given the inherent variation that exists for all human travel behavior. Although the results can be portrayed in a variety of ways depending on the decision context in which AirMarkets is applied, the fundamental output of the simulation is a database, which describes how many passengers will pay how much money to travel in every city pair in the world that has available air transport service of any kind. The power of AirMarkets derives from the application of a model to account for human consumer decision-making at the individual trip level, employed in a computationally efficient system. Two fundamental assumptions govern the design and implementation of the AirMarkets microsimulation. The first is that all air travel is purchased by individuals making decisions for themselves or a group of travelers. For the vast majority of air travelers, this purchase is for a ticket for a flight at a specific scheduled time, operated by a commercial airline. For the smaller fraction of travelers who have either the piloting skills or financial means (or both), the purchase might be for ownership, charter, or rental of a privately owned airplane under the control of the traveler. The second assumption is that for all but a very, very few travelers, all air travel has negative utility. That is, there is no benefit to air travel except to get from one place to another. The act of traveling itself has no natural or intrinsic value; the travel only satisfies a need derived from leisure, business, family or other purposes. Thus, the act of air travel is only an economic activity, with no social, psychological, belief, mystical or mythic value. These assumptions serve the science of travel demand modeling very well.

Forecasting Challenges

• The future is weakly represented by the past

• Expectations based on history under-represent shifts in context in which the future will unfold

• Our beliefs about the future is shaded by our successes, cultures, rewards, all of which are subject to change


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Agent-Based Modeling AirMarkets is an Agent-Based Model, or ABM. ABMs have been deployed to support improved decision-making in computational economics, epidemiology, human cognition, consumer behaviors, traffic congestion, biology, and human social systems. An ABM differs from other kinds of history-based forecasting models in that the entities and relationships that make up the system being modeled are represented individually and uniquely, rather than being summarized and characterized by averages or other single-dimensional measures. ABMs have become possible in recent years because of the availability of inexpensive computer processing and storage

power, so there is no operational issue with maintaining detailed data on millions of passengers or hundreds of thousands of markets. The result is that the ABM approach enables strategic to tactical exploration of the results of “What if?” questions and assumptions. The AirMarkets ABM permits the client to investigate and forecast the consequences of design and operational decisions affecting the uptake of a transportation capability in the global markets. The AirMarkets Model Structure AirMarkets produces its basic output from three major components. First, the global collection of potential air travelers is called the synthetic population, comprised of “agents” or mathematical representations of consumers and their decision-making processes. Second, the network for an available transportation service is called the scenario, from which travelers (or agents in the microsimulation) can choose a transportation service option. Third, the mechanism by which travelers become aware of available travel options is called the distribution system, representing the business model in which the options for travel are made available to the agents. The equations used to model all three of these components are generated from empirical research on travel markets over the past several years. This research forms the basis on which the platform represents human traveler consumer behaviors, choices, and decisions. In addition, for new agent behaviors, new networks, or new business models, the client’s assumptions about any aspect of the consumer- and the supply-side characteristics can be specifically implemented. Synthetic Populations: The synthetic population is the collection of agents that represents the air travelers. Each passenger agent represents one or more individual passengers (to account for groups of people traveling together, such as a family or business group). The passenger(s) want to travel in a specified city-pair market (from an origin city to a destination city), with a specified trip purpose, a maximum willingness-to-pay, an ideal

Agent Based Models

• Represent consumer decision-making, demographics, geographics, economics, with probabilities

• Enable generation of data where none historically existed

• Support “What If?” analyses in the presence of uncertainty


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departure or arrival time, and unique values associated with money, with time, and with travel comfort. There is one agent for each passenger group that is expected to travel in every city-pair market in the world sometime during a time period referred to as the standard week. Each agent is assigned a booking instant, which is a specific time before departure when the agent will engage the scenario to find and purchase a specific travel option. These numeric values for each passenger agent are called the agent characteristics, and represent the human travel consumer behaviors. These values are unique to each agent, and are functions of income, age, and other definable properties, and are drawn from the probability distributions for those characteristics evident in the air traveling population found in each market. These distributions are found in the empirical research that supports the generation of the synthetic population. For example, there exists an inverse relationship between the value of money and the value of time specific to higher income segments of the population of consumers. For this part of the population of wealthier consumers, as the value of time increases, the value of money decreases, in turn affecting the consumption behaviors of that part of the population. In addition, the synthetic population can represent the unique socio-demographics for the populations existing in all global markets. For example, in certain parts of the world, travel consumers behave differently regarding daytime versus nighttime travel schedules. In other populations, notably in the U.S., younger consumers (the so-called Millennials), exhibit different consumption behaviors, for example for automobiles and for technology devices. Geographic distributions of synthetic populations enable another vital component of the business of air travel – total unconstrained demand. Air travel demand is more than a simple economic-driven phenomenon – understanding the relationship between population distributions and their propensities to travel comprises an important feature of AirMarkets. As a metaphor, think of total unconstrained transportation demand as akin to the question, “How many fish are in the sea?” Then think of constrained (or satisfied) demand as “How many fish can you catch?” By imposing constraints created by aircraft capacity limitations, schedule considerations, aircraft performance and price, and the competitive mode choice environment, it becomes possible to answer the question, “How many travelers will choose each of the modal options offered?” These constraints shape the underlying, unconstrained actual demand for travel from one city to another into the satisfied demand. Extensive research and development by the AirMarkets team has conquered this issue in a way unmatched in the air travel modeling industry. Our work extends the Gravity Law concept that is well understood in the travel demand discipline (as an analogy between Newton’s discovery of

Demand-Side and Supply-Side Modeling

1. How many fish are in the sea?

2. How many fish can you catch?

3. In transportation, these questions translate into “How many possible customers are there?” and “How many customers will be attracted to your business?”


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the gravitational attraction force between planets of given masses and the travel demand attraction “force” between cities of given population densities). We incorporate air service network constraints, trip purpose modifiers, pricing policies, and other factors of the transportation Gravity Law, to refine this technology for use in the platform. Scenarios: The available air travel services that the passenger agents can choose from are contained in the scenario component of AirMarkets. The schedule for all commercial air carriers operating in the world is a prominent feature of the platform and is always included in the modeling process. Flights typically consist of nonstop, one-stop, two-stop and three-stop itineraries that connect origin-destination city pairs. The itinerary descriptions include the origin and destination cities (with relevant geographical coordinates and time zone attributes), the operator, the day and time of departure and arrival, the number and type of stops, the total duration, the fare, and the cabin seating class configuration of the aircraft (defining comfort levels). For a typical commercial carrier, agents with multiple itineraries are carried on each airplane; in addition, we implement attributes reflecting dynamically differing fares as part of the revenue management protocols for that carrier.

Other forms of air transport can also be included in the scenario. For example, air taxi service can be provided, where the departure (or arrival) times are set to the ideal time property of the passenger agent, and the fare depends on total distance travelled. In addition, non-air transport (e. g. high speed rail or automotive) can be added to explore mode choice options for travel in markets where there is direct air travel competition.

Supply-side agents in the scenario component are

usually air carrier operators, such as commercial airlines or air charter services. The agents could also be retailers that offer passenger in-airport products and services, aircraft maintenance services, or any other air travel support activity. Agents also can be avatars, in the sense that a scenario agent might be managed by a human who is running the simulation, rather than being a purely electronic entity. This method of interactively running the model would be valuable if the client/decision-maker is interested in a variety of “What if?” situations that yield different

demand, load and revenue outcomes.

Distribution Systems: The third component of the AirMarkets simulator is the collection of air travel information distribution systems. These agents can be used to represent various marketing and business models for the supply of air services to the passenger agents, for example through social media solicitation of passengers. For scheduled commercial air travel, these systems can represent sources of available flight data for travel agents, referred to as the Global Distribution Systems (GDS’s); though, there is no

Three Components of AirMarkets Microsimulator

1. Synthetic Population of Consumers

2. Scenarios of transportation networks and modal options

3. Distribution Systems of transportation service business models and operations


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organizational limit on their form within the simulation. The distribution system represents the relationship between the service provider and the traveling public. Consumer awareness of a modal option is a vital component of the demand satisfaction process and accounts for consumer comfort, confidence, knowledge, sense of safety, etc. for the modal offering. As with the scenario agents, distribution systems can also be avatars representing clients who are interactively testing business structure or marketing strategies. The Operation of AirMarkets While computationally complex, how AirMarkets works is quite simple in concept. Synthetic population agents, representing potential passengers looking for air transportation, are taken in order of booking instant from earliest to latest. All are looking to purchase a travel option for some time in the standard week. The distribution agent provides to the passenger agent a list of available options from the scenario agents. The attributes of the options are then combined with the characteristics of the passenger, and the probability that each option will be acceptable to the passenger agent is computed. Then a random number is generated which is used to finally select from the available alternatives, the tickets (or whatever sales units are appropriate) for that option are purchased, the results recorded in the output files, and the option removed from consideration for subsequent passenger agents. The results are graphed or provided in spreadsheets or other appropriate forms (animations are possible) based on standard week statistics. These statistics can then be annualized as needed by the client. The booking period begins 120 days before the first day of the standard week. The rationale for this feature is based on the empirical finding that no flight or fare class on a flight anywhere in the world is sold out prior to 120 days before departure. As bookings proceed, two other processes come into play. One, representing revenue management policies, adjusts prices according to protocols selected by the simulation operator or client. The second is the cancellation of prior bookings – which return the seats to inventory for future sale – by the agents that have already ticketed an option. A general idea of the AirMarkets logic is illustrated in the diagram above. When a simulation run is complete, the results are moved to permanent storage for future examination and analysis. These results are often produced as comma separated value (csv) files for loading into Excel or other analysis packages (e. g. R) for additional, deeper analysis. In addition, specific markets, flights, airports or carriers can be identified (in a construct called a focus) and the results just for those specific market entities isolated and archived. This data management scheme simplifies the portrayal of the simulation results considerably.


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Finally, multiple simulations can be applied to the

same organization of the synthetic population, scenario and distribution system. This allows, for example, sensitivity studies or Monte Carlo analysis of the stochastic variability inherent in human behaviors, such as the actual choice of flight option. Through this analysis, risks associated with forecasts are statistically and validly represented for use by clients in making key strategic and tactical decisions.

What If? : 1. We are proposing a new aircraft

with improved performance and economics?

2. We are studying a new air service network?

3. We want to explore a new air mobility business model?

4. Our airport is recruiting new service providers?

5. We are investing in a new fleet?


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Summary The AirMarkets Microsimulator enables deep insight into global transportation demand forecasts through state-of-the-art ABM capabilities. Combined with the decades of knowledge and experience of the principals in the company in air transportation, aircraft design, air carrier operations, airspace technologies, investment strategies, and aviation business innovations, AirMarkets Corporation provides clients with unparalleled access to decision support in strategic and tactical arenas. Whether your firm is making a decision about a new aircraft design, a new fleet mix purchase decision, a new transportation service business model, an airport management strategy, or an aviation investment firm project, AirMarkets brings a forecasting perspective that is not available from other sources. The future of global air mobility is undergoing tectonic shifts because of new aeronautics technologies, transportation business models, demographic shifts, economic trends, energy price behaviors, environmental considerations, and emerging market needs around the world. Our aim is to offer the most comprehensive, relevant, reliable, defensible, and useful data to inform your strategic decisions and tactical operations management in the best interests of your investors, shareholders, Stake holders, and customers.

150115 What is AirMarkets

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