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1. This is an author-created, un-
copyedited version of an
article accepted for
publication in Measuring
Business Excellence.
Article information
To cite this document:
Ammirato, S., Felicetti, A. M., Linzalone, R.,
Volpentesta, A. P., & Schiuma, G. (2020). A
systematic literature review of revenue management
in passenger transportation. Measuring Business
Excellence.
Permanent link to this document:
https://doi.org/10.1108/MBE-09-2019-0096

2. A Systematic literature review of Revenue
Management in passenger transportation.
Salvatore Ammirato (Department of Mechanical, Energy and
Management Engineering, University of Calabria, Rende, Italy)
Alberto Michele Felicetti (Department of Mechanical, Energy
and Management Engineering, University of Calabria, Rende,
Italy)
Roberto Linzalone (Department of Mechanical, Energy and
Management Engineering, University of Calabria, Rende, Italy)
Antonio Palmiro Volpentesta (Department of Mechanical,
Energy and Management Engineering, University of Calabria,
Rende, Italy)
Giovanni Schiuma (Department of Mathematics, Computer
Science and Economics, University of Basilicata, Potenza, Italy)
Structured Abstract:
Purpose: This paper proposes a deep and complete literature review of
the Revenue Management (RM) issue in the passenger transportation
industry. The purpose is to investigate structural elements and
characteristics able to make sense of the current body of RM literature
under a managerial perspective.
Design/methodology/approach: The RM literature landscape is
analysed according to a Systematic Literature Review approach in order
to point out the main topics around which the scientific literature has
grown in the last 30 years. Topics are further categorized in themes by
means of an agglomerative hierarchical clustering procedure.
Findings: First, the scientific literature of the last 30 years has been
categorized in 10 topics and 4 themes. Second, topics and themes are
put in contrast with stages of the RM business process in order to
highlight research areas still underdressed by the literature. Third, the
paper suggests research lines for future-related works that could
positively contribute to elevate RM from a system of techniques to a

3. ‘strategic management’ theory.
Originality/value: Available literature reviews of RM in passenger
transportation are focused on quantitative aspects and are poorly
structured in the systemic view of the RM. This research is the first of
its kind since it aims to assess the whole body of literature, rather than
specific themes, and to do it under a managerial perspective.
Keywords: business process, management science, yield management,
dynamic pricing, Latent Dirichlet Allocation
1 Introduction
In the recent years, due to globalization, international trade agreements, free of
circulation of people and goods cross countries, technological innovations and the
Internet, the Transportation Industry is prospering and is emerging as strategic for the
attainment of economic development and social needs (Hong et al. 2011; Duranton, and
Turner, 2012). Transportation of passengers, in particular, is becoming a very competitive
industry and an attracting arena for scholars from various disciplines and theoretical
domains such as Transportation, Operations Research, Information sciences,
Infrastructures, etc. (e.g. Barnarth et al., 2003; Kamargianni et al., 2016).
Nonetheless passenger transportation is attracting Management practitioners and
scholars, since in the recent 30 years it is offering many theoretical and practical
challenges. Disruptive innovations in passenger transportation, like those based on
Business Model theories (i.e. Flixbus in the bus sector or Uber in the car one), or those
based on Revenue Management approaches in air transportation, are catalysing the
research in this field, and are paving the way to new paradigms of business in passenger
transportation (Tongur and Engwall, 2014; Christensen et al., 2015).
Traditionally, revenue management in passenger transportation entails maximizing
revenue by optimizing seats availability and price levels (Talluri and Van Ryzin, 2004).
However, a holistic view of transportation systems is emerging in recent years, according
to which smartness, sustainability and passenger safety have become the three key
elements of a modern transportation system (Haque et al., 2013). Transportation service
providers operate in a context requiring an approach to revenue management that goes

4. beyond its classical definition, in order to face both companies’, consumers’ and other
stakeholders’ needs. A new “sustainable” perspective for revenue management that
considers not only economic viability, but also social and environmental performances is
emerging (Lovric et al. 2013). Passenger loading balance, route optimization and fare
diversification offer at the same time opportunities to improve company profitability,
environmental impact and social inclusiveness. This new perspective opens up the way
for a renewed interest toward revenue management.
This paper deals with the Revenue Management (RM) issue, specifically addressed to
the passenger transportation industry, under a managerial perspective. RM techniques
have been prevalently used to market and sale perishable and limited services, through the
management of the available capacity (hotel rooms, seats in air transport, rental cars in a
rent-a-car), with the aim to maximize and optimize the volume of business (Vinod 2016).
Namely, RM is the collection of strategies and tactics used by companies to scientifically
manage the demand for products and services through the dynamic combination of price,
marketing and distribution levers (Talluri et al. 2008). The managerial interest towards
RM is due to its potentials in improving Business Process Performance both at the firm
level, and at the industry one (Talluri and van Ryzin 2006). In this sense, many scholars
studied RM as a business process therefore analysing the set and combination of activities
performed by revenue managers on strategic, tactical and operational level and
identifying stages, information flows and sequence of the activities (Ivanov, 2014)
(Tranter et al., 2008) (Emeksiz et al., 2006) (Vinod 2004) (Talluri and Van Ryzin 2006).
Several Studies and Literature Reviews have been developed by researchers in order
to systematize past studies, and to collect practices and evidences. The first line of
Reviews can be traced back to research aims, the second to practice aims. About the first
line, scholars attempted to the foundations of RM, trying to structure the concept of yield
management1
and figuring the potential field of application besides the airlines (Kimes,
1989), or to make a glossary of RM’s terminology (McGill and Van Ryzin, 1999). As an
alternative, the research reviews are focused on specific issues: optimization approaches
in the airlines (Belobaba 2015), unconstrained Methods in Revenue Management (Guo
et al., 2012), dynamic pricing2
models and methods (Elmaghraby and Keskinocak, 2003),
1
Yield refers to either yield per available seat mile or yield per revenue passenger mile (Kimes 1989).
2
Dynamic pricing is the strategy of offering a price reflecting current level of demand and occupancy,
and amend it according to changes in demand and occupancy rate (Ivanov 2014) overt time.

5. problems and the hidden costs coming from code sharing alliances practices in Airline
(Gerlach et al., 2013), distribution problems in airlines (Fiig et al. 2015). About the
second line, scholars focused on various practical issues: impacts of promoting branded
products and ancillary services across airlines’ channels of distribution (Vinod and
Moore, 2009), RM tools in a demand dependent modelling set (Weatherford and Ratliff,
2010); opportunities and best practices of codeshare and alliances in revenue management
(Ratliff and Weatherford, 2013).
Despite the effort spent by scholars and practitioners for systematizing the knowledge
and the research of RM, still scarce and unfocused are the contributions. Even though
advances have been made with regard to quantitative modelling of the basic issues
subtended (i.e. inventory, pricing, demand forecasting), RM’s literature appears to lack in
strategic framing and linkage of the main topics. The call to a wider and integrated picture
of the RM body of knowledge appears still unaddressed: “although many people associate
revenue management with quantitative techniques such as forecasting, optimization, and
overbooking, this only paints part of the revenue-management picture” (Kimes, 2003 –
p.138). Overall, the state-of-the-art picture of RM appears still fragmented showing a gap
not fulfilled. This gap, in our opinion, hampers the further development and the elevation
of RM from a system of techniques (Vinod 2016) to a ‘strategic management’ theory,
which appears plausible and desirable in light of the maturity of the extant literature. With
this paper we aim to investigate this gap highlighting structural elements and
characteristics able to make sense of the current body of RM literature, underdeveloped
elements, as well as promising areas for future-related works (Light and Pillemar, 1984).
To reach this aim, we deep analyse the RM literature landscape according to a Systematic
Literature Review approach (Denyer and Tranfield 2009) in order to clarify the main
topics around which the literature has grown in the last 30 years. Unlike all the past
works, this research aims to assess the whole body of literature, rather than specific
themes, and to do it under a managerial perspective.
The paper is organized as follows. Section 2 provides a description of the
methodology. Section 3 presents the results, Section 4 proposes a discussion of results
and, in the end, Section 5 draws the conclusions.

6. 2 Methodology.
Following the principles and the process of a Systematic Literature Review in
Management studies (Denyer and Tranfield 2009), our research methodology was
organized in four steps: question formulation, papers location and selection, papers
analysis and classification, definition of themes.
Research Question.
According to the indication in (Denyer et al., 2008) regarding the question
formulation, the research questions can be stated as follows:
RQ: Which is the structure, of the Revenue Management body of literature according
to the scientific research in Management? Which promising and underdeveloped elements
can be identified to address gaps and future developments?
Papers location and selection
Paper location. We searched for all published scientific articles within the Elsevier
Scopus database, a recognized citation database of over 50 million of scientific articles,
39.647 journals, from about 6.000 editors. To assure the selection of papers with high
scientific quality, the search process was restricted to those articles, published in English
until the date of December 31, 2018, that were published in academic journals. Therefore,
we removed other papers’ source types (i.e: Book series, Conference Proceedings and
Trade Publications) from the search.
On the basis of the prior experience of the review team, of the literature related to
research question, and of an internal discussion among authors, an initial set of keywords
was elaborated. From that set of keywords, a first sample of resulting articles informed a
refinement of the keywords, and allowed to administer a more precise search with new
and more coherent results. Hence, we grouped the keywords into two domains: the
theoretical domain (i.e. the managerial theme) and the application domain (i.e. the
passenger transportation system) (Table 1).
--- PLEASE INSERT HERE TABLE 1 ---

7. The keywords were constructed into search strings, in order to administer the search
to the SCOPUS scientific data base. The following search string was structured: The
search must contain one keyword of the Theoretical domain (ie revenue management OR
yield management OR dynamic pricing) AND one keyword from the Application domain
(ie train OR trains OR ……). Through this procedure, we identified an initial sample of
616 papers published in 186 different Scientific Journals
Paper selection. In order to carry out the research under a managerial perspective, we
decided to exclude form the sample those papers which have totally other disciplinary
perspectives. To reach this aim, we excluded from the sample of 616 all the papers
published in Journals not indexed in at least one of the following 4 world-wide
acknowledged Management scientific data bases:
 Academic Journal Guide 2018 – ABS;
 Journal ranking in Economics and Management – CNRS (dec. 2018);
 Web of Science OS/ISI (WOS) - categories: Business, Business & Finance,
Economics, Management, Public Administration, Operations Research &
Management Science,
 SCOPUS/SCIMAGO (SCOPUS) - categories: Business, Management & Accounting,
Economics, Econometrics & Finance, Public Administration, Management Science
& Operations Research,
Since 95 papers showed no membership in the 4 mentioned databases, we removed them
from the sample. At the end, a final set P of 521 suitable papers represented the so-called
‘core contributions’ (Denyer and Tranfield 2009).
Papers analysis and classification.
The Analysis of documents has been implemented through a text mining approach,
based on the Latent Dirichlet Allocation (LDA) (Blei 2012). LDA uses Bayesian
Estimation Techniques to infer a document-term matrix, that is a table describing the
degree of membership (topic proportion) of each sampled paper (document) to each topic.
LDA technique takes in input the documents to be analysed (the 521 articles in P) and
the number of topics k to be extracted. As suggested by Chang et al. (2009) and Blei
(2012), we selected k using a reasonable practice of evaluation among alternative values
in such a way that the interpretation of the machine-generated model results becomes as
easy as possible from the point of view of a human reader. We have evaluated multiple

8. outputs of LDA with k ranging from 2 to 15 and have consensually agreed that the most
meaningful set of topics is reached with k = 10.
The LDA procedure gave, as output, a group of keywords associated to each topic
(Table 2) and the document-term matrix.
--- PLEASE INSERT HERE TABLE 2 ---
In order to deduce meaningful descriptions of each topic, we implemented a human-based
review of a restricted, representative and relevant, subset Q ⊆ P of high quality papers. Q
consisted of those articles in P that match ALL the following criteria:
1. were published in academic journals ranked at a ‘‘C’’ level or higher of the German
Academic Association for Business Ranking or equivalent values of ISI Impact Factor
(IF>= 0.7) or ABS Academic Journal Quality Guide (higher than 2° category)
(Grimaldi et al., 2017).
2. Have a Scopus Citation Index (CI) value >= 10 (Ball, 2005)
3. Have a topic proportion (TP) value of 0,25 or higher (Blei and Lafferty, 2006).
Papers included in the subset Q are 29 and are listed in table 3
--- PLEASE INSERT HERE TABLE 3 ---
The 10 topics detected with the LDA procedure, are named and discussed according
to the 29 selected papers. In particular, the discussions are developed on the basis of
papers’ main concepts which are original proposals by papers’ authors or reformulations
of studies they cited (in this case we reported them in the topic description).
TOPIC 1. Product Inventory and seat allocation.
Scheduling of transportations and RM are closely connected (Casey 2014). Starting
from data at industry-level the objective of papers categorized under this topic is to
allocate traffic, based on both the company’s and the competitors’ schedules, and to
recommend actions on company inventory (namely, inventory control recommendations)
(Casey 2014)
Assuming that prices are determined outside the company, the capacity allocation
decisions are pure inventory problem and RM acts as “on seat-inventory control” for
coping with uncertain demand (Chew et al., 2009). Two perspectives emerge from the

9. analysis: a schedule optimization problem (aimed to maximize high-value routes and
schedules) and a revenue optimization problem (aimed to maximize sold seats).
Ideally, a seat inventory allocation problem would consider that two or more
transportation companies would be competing and operating on the same origin to
destination route (Sen Mazumdar and Parthasarathy, 2014). Most of seat allocation works
are based on static problem formulation where the transportation companies set a booking
limit and once the sale of discounted tickets is closed, it's not reopened (Gao et al., 2010;
Netessine and Shumsky 2005). Some authors focus on how firms can profit from offering
multiple fares and limiting the availability of seats at low fares when they cannot segment
the market (Dana 1999).
TOPIC 2. Monitoring of Industry data and Events.
Data are fundamental elements to design transportation offering which translated in
the scheduling of transportation services (Faust et al., 2017). In this sense Industry data is
crucial to perform the vital activities of transportation: schedule design, fleet assignment,
aircraft maintenance routing, crew scheduling. Strategic analysis of Industry Data is a key
topic to avoid falling into strategic errors (Swan 2007). Another crucial issue is the
Industry events meaning those companies’ changes (mergers, acquisitions, alliances) with
impacts on the Industry structure (Dobson and Piga 2013).
TOPIC 3. Optimization Models and Function capacity/ time.
A first overview of optimization models and techniques used in solving RM problems
is presented in (Pak and Piersma, 2002). Optimization techniques have been essential in
the development of RM, particularly for multi-period inventory management, single-leg
and network capacity control, pricing , overbooking (Weatherford and Ratliff, 2010).
Studies includes both deterministic (i.e. based on linear programming (Möller et al.,
2004)) and stochastic approaches (mainly based on Markov decision processes and
nonlinear programming (Walczak and Brumelle 2007). Bertsimas and Popescu (2003)
proposed a linear-programming approach for virtual nesting booking control. Karaesmen
and van Ryzin (2004) addressed the overbooking problem by means of a stochastic
algorithm. Other authors addressed RM optimization models under a time-dependent
point-of view: Fröidh (2008) investigated the impact of travelling time in determining the
sensitivity to the price of travellers, while Chen and Plambeck (2004) addressed the

10. decision whether to accept or deny customer requests for a discount-fare ticket as a multi-
period inventory management.
TOPIC 4. Fare structure.
Due to keen competition in the industry, competitors' fares often limit the effect of
price option. In contrast, seat inventory management is a lever solely under the control of
the airline. In some “older” studies, a problem emerges that managers in travel and leisure
industry face hard time constraints and have almost no control in the short run over
available space (Gallego and van Ryzin, 1994).
Without changing fares, an optimal seat allocation among fare classes can optimize
revenue effectively (Feng and Xiao 2001). This requires airline reservation systems to
have point-to-sale capability. The potential economic gain of effective seat inventory
control, hence, has made major carriers worldwide commit tremendous effort to the
intensive modelling studies and system designs on the optimal control policy.
Market segmentation through time-of-purchase mechanisms (e.g., advance purchase
requirements, cancellation penalties) provides an explanation for the benefits of yield
management (Weatherford and Bodily 1992).
TOPIC 5. Information Systems.
The use of information systems to support pricing strategies has the objective of
bringing companies to sell the right capacity to the right customers at the right price and
at the right time (Kimes and Wirtz 2003). The continuing shift to Internet-based
reservation systems make easier to record more and different types of information
(Gallego and Şahin 2010) and allowed the coordination of inventory management, sales
and marketing, yield and revenue management, ticketing, and departure control systems
(Buhalis 2004). Yield management is considered as coordinating 5Cs: calendar, clock,
capacity, cost and customer (Buhalis and O’Connor, 2005). Historical demand patterns,
competitor pricing as well as events and occurrences affecting demand can be scanned
electronically to provide revenue-management critical information. Monitoring sales
through mobile ticketing allows marketers to adjust the product and price or/and to
initiate promotional campaigns (Talluri & van Ryzin, 2006). Increased adoption and
development of dynamic pricing and revenue management can be attributed to the
increased availability of demand data while the ease of changing prices is due to IS and

11. the availability of decision-support tools that handle large-scale optimization
(Elmaghraby and Keskinocak 2003).
TOPIC 6. Fare and Price changes by segments and market.
Two main branches of policies can be identified: fixed prices approaches and dynamic
pricing (Talluri et al. 2008). In the first one, it is postulated a fixed price for the items of
the inventory. In the second case a changing price over time is considered, according to
dynamic acquisition and elaboration of market data, available inventory, actual
reservation collected and forecasted (Elmaghraby and Keskinocak 2003).
In allocating capacity, managers face a trade-off between two types of potential
losses; Yield loss—selling at a low price, and losing a better price later, and Spoilage
loss—waiting in vain to sell at a high price, and losing the opportunity of an earlier low
price offer (Biyalogorsky et al. 1999).
Papier and Thonemann (2011) propose a model to define fares for two segments of
customers of rental services: classic and premium services. In (Van Ryzin and Vulcano
2008) authors proposed a model based on a discrete model of capacity and demand that
leverage on virtual nesting. Itinerary fare-class combinations are mapped ("indexed") into
a relatively small number of "virtual classes" on each resource (flight leg) of the network,
while Cooper (2002) described asymptotic properties of revenue management policies
derived from the solution of a deterministic optimization problem, by segmenting
consumers in fare-classes.
TOPIC 7. Computational methods for dynamic pricing.
(Elmaghraby and Keskinocak 2003; Vinod 2016) provide an exhaustive review of
dynamic pricing computational models in the presence of inventory considerations. Two
categories of models: the first one, focuses on market environments where there is no
opportunity for inventory replenishment over the selling horizon and demand is
independent over time. The second category focuses on market environments where the
seller may replenish inventory over time, demand is independent over time, and
customers behave myopically.
Typically, dynamic pricing approaches assume that demand is a stochastic function of
price, and that only one price is available (posted) at a given time (Gallego and van Ryzin
1994). Results of this research were extended to network connections in (Gallego and
Van Ryzin 1997). Feng and Gallego (1995) addressed the problem of deciding the

12. optimal timing of price change. This work was successively extended to take into account
multiple classes (Feng and Xiao 2000). More recently, De Palma et al. (2015) present a
computational method to explore optimal scheduling and pricing in a simple dynamic
model. (Amirgholy and Gonzales 2016) proposed a dynamic pricing model for an
effective demand responsive transit (DRT) system.
TOPIC 8. Forecasting supply reservations.
The development of forecasting methods allows to estimate more accurately the
probabilistic parameters describing the booking requests arrival process. Supply
forecasting is crucial for the quality of revenue management decisions (e.g. overbooking,
capacity control, pricing). Pölt (2004) found a significant relation between the reduction
of forecast error and the increase in company revenues, generated through the adoption of
RM systems. Capacity, price and demand are the three main area of investigation of RM
forecasting techniques (Chiang, Chen, and Xu 2007) .
Lee (1990) classified forecasting methods in RM into three broad classes: historical
booking models, advanced booking models and combined models. An overview of
forecasting approach in passenger transportation is provided in (Zaki, 2000). Weatherford
and Pölt (2002) proposed a method of unconstraining bookings to improve forecasting
accuracy for airlines. Neuling et al. (2004) introduced a machine learning algorithm
which uses the passenger name record (PNR) as a data source to improve forecasting
accuracy. In Bilegan et al. (2003), authors develop a stochastic dynamic programming
approach to manage requests for travel occurring during predefined time periods.
Escobari (2012) analysed U.S. airlines data set to empirically study the dynamic pricing
of inventories with uncertain demand. Alizadeh et al. (2013) considered a two-stage
stochastic extension of the bi-level pricing model (i.e.: maximizing the revenue raised
from tariffs, knowing that user flows are assigned to cheapest paths).
TOPIC 9. Allocation of seats in network connections.
This issue encompasses network capacity control, an allocation problem when
customers require a bundle of different resources (e.g. a customer may require two or
more connecting flights). From the Littlewood’s seminal work (Littlewood, 1972), many
researchers have proposed solution addressing seat inventory control problems in network
connections. McGill and van Ryzin (1999) carried out a review on single-resource
capacity problems. Feng and Xiao (2001) proposed a stochastic model to allocate seats

13. among competing origin-destination routes. El-Haber and El-Taha, (2004) proposed a
finite horizon Markov decision process at a discrete time. Zhang and Cooper (2005)
adopted a stochastic optimization approach to address the simultaneous seat inventory
control of parallel flights with the same origin and destination.
Dynamic Programming, a method usually adopted to solve the optimal seat allocation
problem, allows also to determine all of the optimal paths for the airline network (all
combinations of sequences of selling different itineraries/fare classes) (Weatherford and
Khokhlov 2012).
Button et al. (2007) used disaggregate data to examine the pattern of fares set by airlines
depending of class fares and seats availability while Laval et al. (2015) analysed the
dynamic traffic assignment problem on a two-alternative network.
TOPIC 10. Services and profit increase.
How to relate service price and cost of transportation, and which RM objective put at
the basis of sell or not, is the main issue. Increase Revenues or Increase Profits? Research
on consumer choice models have demonstrated the validity of sell-up, cross flight
recapture, down-sell (Hopman et al., 2017; Vinod 2016). Approaches and models to
maximize profits or minimize the losses in revenue that arise from this situation are faced
in some literature streams (Hopman et al. 2017).
Anderson et al. (2004) proposed a new approach to the pricing theory and operating
assets in the face of uncertainty and in the presence of some flexibility in operating
strategies. This approach is based on the real option theory, introducing a framework of
modern financial options pricing to frame and solve pricing. Gaggero and Piga (2010)
investigated the relationship between pricing and market structure on the routes founding
that the elimination of a competitor in a market is likely to have harmful consequences for
consumers.
Definition of themes
The 10 topics were further grouped into t themes by means of an agglomerative
hierarchical clustering procedure. The degree of relationship among topics was
calculated taking into account the Pearson Correlation Coefficient across the topic
proportion for all paper, deriving from the Document Term Matrix obtained as output of
LDA procedure.

14. Let pi,j (with i , j ∈ [1,k]) the Pearson Correlation Coefficient across the topic i and j,
and D k x k a Dissimilarity Matrix where each element D(i,j) = 1 - pi,j.
We performed a hierarchical clustering procedure to obtain t = 4 groups of themes.
We decided to cut the dendrogram where the gap between two successive combination
similarities is largest (Babu et al., 2012).
Figure 1 shows the dendrogram obtained as output of the hierarchical clustering,
where each leaf node represents a topic obtained through the LDA procedure.
--- PLEASE INSERT HERE FIGURE 1 ---
The four themes were also evaluated against the keywords of each topic associated
with the t theme. This evaluation, based on a subjective analysis of the authors led to the
identification of the four main themes in RM literature and their relation with the topics
(Grimaldi et al. 2017).
--- PLEASE INSERT HERE TABLE 4 ---
Discussion
Results highlight that scientific papers dealing with RM can be categorized in 10
topics, which can be further analytically clustered in 4 themes after an agglomerative
hierarchical clustering procedure. According to the aims of this study, in this section we
give an interpretation of such themes from a managerial point of view highlighting
structural elements and characteristics able to make sense of the current body of RM
literature. In this sense it is worth noting that, although the four themes are meant as
theoretical areas of RM, they also have a functional significance and, thus, can overlap
the macro-stages of the RM process. According to the (Light and Pillemer, 1984)
suggestions to highlight underdeveloped elements in the reviewed studies, as well as
promising areas for future-related works, a comparison between the themes/topics of RM
and main contributions in literature on RM, seen under a business process perspective,
will be proposed.
Theme 1. Industry data, demand modelling and forecasting.
This theme includes the forecasting of demand by modelling customers’ requests. The
model considers outside data (industry) and internal management actions influencing the

15. fare structure. The management can decide on the optimal solutions, in light of the model,
of industry data and fare structure. Industry data and events are strategic input data which,
according to our findings, should be combined with marketing data to feed the forecasting
of demand. Anyway, results do not show any market segmentation membership to the
RM, aligning with Ivanov’s results which state that this item is of interest for Marketing
processes, and not for RM (Ivanov 2014). Rather than price, whose value is basically
defined by the market, fare structure and demand optimization models are levers under
the control of the transportation companies. Without considering price, nor fares, an
optimal seat allocation among fare classes cannot optimize revenue effectively (Feng and
Xiao 2001).
Theme 2. Inventory Management.
The second theme groups the topics related to seat allocation and control of
availability by customer class. The Control of Inventory concerns the practices, models
and concepts to check the availability of inventory for a customer segment, and manage
the inventory in accordance to forecasted demand and optimal allocation. The optimal
allocation is managed by amounts of requested inventory, considering customer classes
and Origin/Destination (O/D). Inventory control deploys for single routes (leg-based)
(Feng and Xiao 2001; McGill and van Ryzin 1999), or for multi-routes O/D with multiple
fare classes, no-shows, cancellations and overbooking (El-Haber and El-Taha 2004;
Vinod 2016).
Theme 3 - Pricing Models.
The third theme regards the formulation of the right price, for the right customer, at
the right moment, and regards the adoption of value based policies of pricing. To the first
topic, the dynamic pricing strategies gained attention and adoption in many industries
(Vinod 2016). These strategies were supported by availability of demand data, ease of
data elaboration due to computational technologies, availability of decision support-tools
for dynamic pricing (Elmaghraby and Keskinocak 2003). Forecasting reservation is about
the booking requests arrival process. This is crucial for the quality of revenue
management decisions (e.g. overbooking, capacity control, pricing), and is tightly related
with pricing.
Theme 4. Distribution and Sales.

16. The theme Distribution and Sales regards the automated management and
coordination of sale and distribution of transportation seats, by O/D, within a given
network of connections. This theme deals with the shaping of the fare structure and price,
by segments and markets, to optimize revenues or profits; to this aim new elements like
travel services and ancillaries are pairing to the sale of travels, giving rise to the concept
of Total Revenue Management (Rickey, 2014). This implies the creation of dynamic
customized offers, based on the passenger’s route, scheduling, ancillary and non-air
products available, through companies’ preferred distribution channel. The Fare structure
is then a lever, to move from very restricted to restriction free able to capture both price
sensitive customers (i.e. leisure passengers) and non-price sensitive customers (i.e.
business passengers) (Vinod, 2016). Information Systems play a critical role for the
planning and control of inventory management, sales and marketing, yield and revenue
management, ticketing, and departure control systems (Buhalis 2004), or distribution and
sales supported by mobile ticketing technologies (Talluri & van Ryzin, 2006).
Comparing and contrasting Results.
In order to answer the second part of the RQ (“Which promising and underdeveloped
elements can be identified to address gaps and future developments?”), we can put in
contrast results in table 5 and table 6. Interesting insight emerge from such comparison
that can contyribute to elevate RM literature from a system of models and techniques to a
“strategic management” theory.
In order to give an overview of the surveyed papers distribution, in Table 5 they are
sampled per theme and topic.
--- PLEASE INSERT HERE TABLE 5 ---
What emerges is that all themes are covered by extant literature in a quite balanced
measure. However, two topics (“information systems” and “fare structure”) should be
more investigated since their potentials for improving the RM process performance.
In Table 6, results of a cross comparison between RM topics coming from the
literature review and stages of the RM process models is reported.
--- PLEASE INSERT HERE TABLE 6 ---

17. Despite Industry data and events is a topic of the extant scientific literature on RM, no
explicit recognition is made by the RM process models. RM process’ initial stages do not
fully consider the linkage of company’s RM to the Industry. Since Industry data and
Events plays a strategic influence on operational stages of the RM process (i.e.: travel
prices, routes definition, demand/supply forecasting, etc.), this lack of structural
connections between company strategy and industry evolution addresses a gap in the
practice of RM, whose process structure does not clearly identify it as a stage. The lack of
managerial perspectives is confirmed by the absence of papers dealing with RM
Strategies. Actually the keywords Strategy, Evaluation, Monitoring, that represent key
activities of the Management disciplines, do not appear as topics, and just once the
keyword strategy appears as tenth for a topic, in our literature review on RM. Moreover,
it is interesting to detect how the Goal setting and Channel analysis and selection are
acknowledged, respectively in (Ivanov 2014) and (Tranter et al., 2008) as stages but do
not emerge from our results, as well as from others’ process models stages.
It is interesting to note that Implementation of RM strategies, Evaluation of RM
activities, Monitoring and amendment of the RM strategies, and Performance
measurement and management reporting are addressed as stages of RM process by
(Emeksiz et al. 2006) or (Tranter et al., 2008), while Monitoring the whole process is
addressed by (Ivanov 2014), but on the contrary have no mention among the Topics
resulting from the Literature Review. Again the extant literature on RM shows a gap with
regard to the Management issues and perspectives of the RM research.
Topics 3 and 4 appear in line with the stages of demand forecasting and demand
analysis as proposed by (Emeksiz et al,, 2006; Ivanov 2014; Talluri and van Ryzin 2006;
Tranter et al. 2008) and, thus, are confirmed by most of the RM process models.
The two topics under theme 2 are addressed as topical stage of the RM: inventory
management (Tranter et al. 2008), inventory pooling (Vinod 2004), control of allocation
and overbooking (Talluri and van Ryzin 2006), which represent with different names the
same topic. Our results then confirm the topicality of the Inventory Management and
distinguish the two subtended topics: inventory recommendations and seat allocation (on
single and network resources).
The analysis of theme 3, leads to a substantial convergence both on the topic 7
Computational methods for dynamic pricing, supported by the positions of (Ivanov 2014;
Tranter et al. 2008; Vinod 2004), and on the topic 8 Forecasting supply reservations. This

18. latter topic appears as a double-sided issue, form the one side, it deals with the research
problem of pricing modelling, from the other side, it has a univocal and practical
compliance with the problem of Overbooking. Also this theme is covered by the present
literature.
The Theme 4 Distribution and Sales, appears addressed by the RM process’ stages
only with regard to the topic 6 Fare and Price changes by segments and markets. In
particular (Tranter et al. 2008) focus on the Channel Management, while (Ivanov 2014)
addresses the Implementation of sales techniques. About the topic 5, Information systems,
there is no substantial confirmation from the RM process stages, except for (Talluri and
van Ryzin 2006). Information systems are enabling factors of the RM process, so on the
one hand their impacts are observed in the context of RM, on the other hand Information
Systems still does not represent a strategic stage for the RM process. Our result is actually
supported by (Buhalis 2004; Gallego and Şahin 2010), who support that RM is feasible
thanks to Information Systems.
Finally the topic 10 Services and Profit increase, represents the frontier of RM since it
covers consumer choice models, and are focusing on the use of services and ancillaries as
further resources to add to the travel in order to optimize Revenues (Hopman et al. 2017;
Vinod 2016). It is only embryonically considered by (Vinod 2004) who talks of Revenue
mix controls.
An interesting result emerging from the analysis of table 6 is that two themes are under
covered in the RM process model literature:
1. The strategic planning of the RM process, argued as “goal setting” by Ivanov
(2014) and “Implementation of RM strategies”, by Emeksiz et al. (2006)
2. The monitoring and control of the whole RM process, argued by Vinod (2004),
and Ivanov (2014), and Emeksiz et al. (2006).
5 Conclusions
Findings of this research address the existence of 4 themes which embody 10 topics
within scientific literature on RM. Differently from previous research attempts, that have
been developed on a non-systematic and interpretative review approach, our aim has been
to capture the framework of the RM literature from the bottom to the top of the whole
extant studies. To reach this aim, we actually implied a rigorous methodology that, for the
best of our knowledge, no other previous study implied: the systematic literature review

19. (Denyer and Tranfield 2009). By the mean of automated elaboration (text mining) of a
relevant number of scientific articles (521) we conducted a review on primary sources.
We argue that themes and topics underpinning the RM represent key concepts, whose
disclosure is necessary in order to explain and make sense of the state-of-the-art of the
literature.
This explanation allowed us to address gaps and future research directions that can be
easily summarized as follows. Although all the topic and themes RM process model
literature are investigated by scholars in a balanced way, two topics, namely “information
systems” and “fare structure” should be more investigated since their potentials for
improving the RM process performance, leveraging on new opportunities arising from
Big Data, machine learning techniques and, more generally, the Internet of things.
Moreover, it emerges that two themes are under covered in the RM process model
literature: the strategic planning of the RM process (Emeksiz et al. 2006; Ivanov, 2014)
and the performance monitoring and control of the whole RM process (Vinod, 2004;
Emeksiz et al. 2006; Ivanov 2014). Unfortunately, our research did not reveal any
evidence related to the sustainable revenue management literature stream (Haque et al.,
2013; Lovric et al., 2013). In our opinion, this is a promising perspective which opens up
ways towards a holistic vision for a modern, safe and smart transportation systems.
Implications for research, practice, society. Scholars of RM should take into
account the 10 topics we identified in order to suggest direct future research. Basically we
believe our study is useful to assess the structure of the RM, first of all by the measure of
the consistency of each topic, then of each theme. Consistency is the measure of the
number and quality of the studies developed within each topic, and theme.
Then we want to address implications with regard to the functional relations among
the topics, and themes. We see the opportunity to further develop this reflective study on
the RM body of literature, by a network analysis able to capture the reciprocal and
functional nature of the RM’s topics. They actually act as pillars for the RM “building”,
and as far as the pillars are stronger, the need of advance the building moves from the
pillars to the binding elements. We support the idea that this is the time of moving the
research to this binding studies, able to put together cross-topic analysis.
Directing the future research to respond to the six addressed lines (Industry data and
events, RM Strategies, Evaluation of RM, Monitoring of RM strategies and process,

20. Performance Measurement and Management) will support the strategic adoption of RM in
other transportation systems, different then the Airlines. Up to now RM in Transportation
is almost only Airlines, but once the RM variables are linked to the Industry variables,
then it can be exploited and extended to other Transportations systems (i.e. Bus
transportation). RM can play a strategic role for transportation service providers
Companies, by increasing loading performance of carriers and revenues, and for the
policy makers who should respond to the challenges imposed by social and environmental
sustainability of transportation, by making public transportation systems (i.e. Bus)
competitive and attractive.
Limitations. Methodological choices made in the paper, including the selection
criteria of the papers for detailed analysis dealt on relevant sources in previous literature.
However, these restrictions could lead to the exclusion of interesting works. Despite
SCOPUS data base is probably the world largest one, this study is limited to the scientific
papers available in this single one. The first available paper in SCOPUS is dated 1989,
but there are previous scientific works that date back to the 60s. Moreover, inclusion
criteria adopted for the human-based review of representative and relevant limited strictly
the number of selected papers by excluding some newest articles, due to the Citation
Index. However, although these works were excluded from the human-based analysis,
they were nevertheless considered in the LDA procedure.
Also, the choice to left out books may have an effect on the finding, since
transportation revenue management within books could have been studied more
holistically, offering also a contribution from a strategic management perspective. In any
case, this is beyond the scope of this research, which has however shown that the
literature lacks scientific papers that address the issues of strategic management in RM as
a core aspect of the work.
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26. Fig. 1 – Cluster structure of RM scientific Literature

27. Table 1 – Keywords by domains.
Theoretical domain Application domain
“revenue management”,
“yield management”,
“dynamic pricing”
“flight*”,
“car”,
“train*”
“transport*”,
“ferries”,
“bus”,
“coach”,
“rail*”,
“airplane”,
“airline”
“passenger”

28. Table 2 –Keywords grouped by topics (LDA output)
Topic 1 Topic 9 Topic 2 Topic 3 Topic 4
"invent" "revenu" "manag" "model" "demand"
"custom" "book" "revenu" "optim" "differ"
"product" "avail" "yield" "polici" "model"
"offer" "seat" "industri" "time" "manag"
"distribut" "class" "monit" "expect" "fare"
"avail" "control" "practic" "capac" "structur"
"segment" "alloc" "develop" "function" "studi"
"process" "purchas" "account” "problem" "track"
"rout" "model" "compani" "overbook" "effect"
"hub" "fare" "techniqu" "stochast" "sale"
Topic 7 Topic 8 Topic 5 Topic 6 Topic 10
"problem" "revenu" "system" "market" "price"
"program" "passeng" "oper" "fare" "servic"
"price" "forecast" "inform" "price" "capac"
"dynam" "airlin" "cost" "chang" "sell"
"value" "simul" "improv" "carrier" "profit"
"method" "choic" "strateg" "sales" "consum"
"comput" "data" "transport" "segment" "increas"
"solut" "invent" "custom" "offer" "dynam"
"formul" "optim" "requir" "competit" "firm"
"heurist" "decis" "perform" "time" "advanc"

29. Table 3 – List of 29 most representative papers
No. Title Authors Year Source IF CI TP Topic
1
Joint inventory allocation and
pricing decisions for perishable
products
Chew E.P.,
Lee C., Liu R.
2009
International Journal of
Production Economics
4,407 51 0,267 1
2
Equilibrium price dispersion
under demand uncertainty: The
roles of costly capacity and
market structure
Dana Jr. J.D. 1999
RAND Journal of
Economics
1,573 102 0,264 1
3
Misunderstandings about
airline growth
Swan W. 2007
Journal of Air Transport
Management
2,038 11 0,411 2
4
Revenue management games:
Horizontal and vertical
competition
Netessine S.,
Shumsky
R.A.
2005 Management Science 3,544 93 0,383 2
5
The impact of mergers on fares
structure: Evidence from
european low-cost airlines
Dobson P.W.,
Piga C.A.
2013 Economic Inquiry 1,031 14 0,367 2
6
Modeling and optimization of
multimodal urban networks
with limited parking and
dynamic pricing
Zheng N.,
Geroliminis
N.
2016
Transportation Research
Part B: Methodological
4,081 27 0,383 3
7
Perspectives for a future high-
speed train in the Swedish
domestic travel market
Fröidh O. 2008
Journal of Transport
Geography
2,699 44 0,360 3
8
Semi-Markov information
model for revenue management
and dynamic pricing
Walczak D.,
Brumelle S.
2007 OR Spectrum 2,052 11 0,316 3
9
A dynamic airline seat
inventory control model and its
optimal policy
Feng Y., Xiao
B.
2001 Operations Research 2,263 36 0,425 4
10
Optimal dynamic pricing of
inventories with stochastic
demand over finite horizons
Gallego G.,
van Ryzin G.
1994 Management Science 3,544 709 0,310 4
11
Revenue management with
partially refundable fares
Gallego G., Ş
Ahin O.
2010 Operations Research 2,263 33 0,413 5
12
Dynamic airline revenue
management with multiple
semi-Markov demand
Brumelle S.,
Walczak D.
2003 Operations Research 2,263 46 0,378 5
13
Dynamic yield management
when aircraft assignments are
subject to swap
Wang X.,
Regan A.
2006
Transportation Research
Part B: Methodological
4,081 10 0,349 5
14
Dynamic revenue management
in airline alliances
Wright C.P.,
Groenevelt
H., Shumsky
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2010 Transportation Science 3,338 37 0,347 5
15
Models of the spiral-down
effect in revenue management
Cooper W.L.,
Homem-de-
Mello T.,
Kleywegt A.J.
2006 Operations Research 2,263 87 0,321 5
16
Mathematical programming
models for revenue
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Chen L.,
Homem-de-
Mello T.
2010
European Journal of
Operational Research
3,428 24 0,301 5

30. 17
Research note: Overselling
with opportunistic cancellations
Biyalogorsky
E., Carmon
Z., Fruchter
G.E., Gerstner
E.
1999 Marketing Science 2,794 42 0,414 6
18
Simulation-based optimization
of virtual nesting controls for
network revenue management
Van Ryzin G.,
Vulcano G.
2008 Operations Research 2,263 49 0,387 6
19
Asymptotic behavior of an
allocation policy for revenue
management
Cooper W.L. 2002 Operations Research 2,263 73 0,336 6
20
A reinforcement learning
approach to a single leg airline
revenue management problem
with multiple fare classes and
overbooking
Gosavi A.,
Bandla N.,
Das T.K.
2002
IIE Transactions
(Institute of Industrial
Engineers)
1,759 62 0,280 6
21
Discomfort in mass transit and
its implication for scheduling
and pricing
de Palma A.,
Kilani M.,
Proost S.
2015
Transportation Research
Part B: Methodological
4,081 35 0,435 7
22
Demand responsive transit
systems with time-dependent
demand: User equilibrium,
system optimum, and
management strategy
Amirgholy
M., Gonzales
E.J.
2016
Transportation Research
Part B: Methodological
4,081 17 0,415 7
23
Dynamic pricing in the
presence of inventory
considerations: Research
overview, current practices, and
future directions
Elmaghraby
W.,
Keskinocak P.
2003 Management Science 3,544 612 0,385 7
24
Dynamic Pricing, Advance
Sales and Aggregate Demand
Learning in Airlines
Escobari D. 2012
Journal of Industrial
Economics
1,036 26 0,596 8
25
Two-stage stochastic bilevel
programming over a
transportation network
Alizadeh
S.M.,
Marcotte P.,
Savard G.
2013
Transportation Research
Part B: Methodological
4,081 10 0,318 8
26
Real-time congestion pricing
strategies for toll facilities
Laval J.A.,
Cho H.W.,
Muñoz J.C.,
Yin Y.
2015
Transportation Research
Part B: Methodological
4,081 10 0,304 9
27
Ability to recover full costs
through price discrimination in
deregulated scheduled air
transport markets
Button K.,
Costa A.,
Cruz C.
2007 Transport Reviews 4,647 14 0,284 9
28
Airline competition in the
British Isles
Gaggero
A.A., Piga
C.A.
2010
Transportation Research
Part E: Logistics and
Transportation Review
3,289 23 0,271 10
29
Revenue management: A real
options approach
Anderson
C.K., Davison
M.,
Rasmussen H.
2004 Naval Research Logistics 0,989 22 0,263 10

31. Table 4. Topic-Theme Classification
Theme Topic
1. Industry data, demand modelling
and forecasting
2) Monitoring of Industry data and Events
3) Optimization Models and function capacity/
time
4) Fare structure
2. Inventory Management
1) Product Inventory and seat allocation
9) Allocation of seats in network connections
3. Pricing Models
7) Computational methods for dynamic pricing
8) Forecasting supply reservations
4. Distribution and Sales
5) Information Systems
6) Fare and Price changes by segments and
markets
10) Services and Profit increase

32. Table 5 – Number of paper for each Theme / Topic
Theme
No. of
papers
Topic No. of papers
1 151
2 57
3 60
4 34
2 102
1 53
9 49
3 113
7 64
8 49
4 153
5 37
6 69
10 47

33. Table 6 – Cross-topic comparison of RM literature
THEMES AND TOPICS from
LITERATURE REVIEW
PROCESS’ STAGES/TOPICS (sorted vertically from 1 to n, according to Author’s process
structure)
REVENUE MANAGEMENT LITERATURE
RM THEMES
(own
elaboration)
RM Topics
(own Elaboration)
Tranter et al.,
2008
Emeksiz et al.,
2006
Vinod, 2004 Ivanov, 2014
Talluri and van
Ryzin, 2006
Goal setting
1. Industry data,
demand
modelling and
forecasting
2. Monitoring of
Industry data and
Events
Gathering
information
Sources of data
and information
(i.e. customer,
product, price)
Customer
knowledge
Preparation
Market
segmentation and
selection
Internal
assessment
Competitive
analysis
3) Optimization Models
and function capacity/
time
Demand
forecasting
Supply and
demand
analysis
Market
segmentation
Analysis of data
and demand
Data collection
(mix of data for
Estimation/
Forecasting and
Optimization)
4) Fare structure
Forecasting of
demand and
supply
Implementation
of RM
strategies
2. Inventory
Management
1) Product Inventory
and seat allocation
Inventory
management
Inventory pooling
Control, of
Overbooking and
..
9) Allocation of seats in
network connections
Allocation
3. Pricing
Models
7) Computational
methods for dynamic
pricing
Dynamic value-
based pricing
Demand and
supply
forecasting
Decision (on
prices, rate
structures,
overbookings)
8) Forecasting supply
reservations
Overbooking
controls
4. Distribution
and Sales
5) Information Systems
Reservation
system/PMS/ERP,
provide
information flow
to the Global
distribution
systems,
Sales/CRM units,
Call Center, Web
6) Fare and Price
changes by segments
and markets
Channel and
inventory
management
Implementation
of sales
techniques
10) Services and Profit
increase
Revenue mix
controls

34. Evaluation of
RM activities
Performance
measurement and
management
reporting
Monitoring and
amendment of
the RM
strategies
Monitoring the
whole process