Sustainability 2020, 12, 2732; doi:10.3390/su12072732 www.mdpi.com/journal/sustainability
Is Parking in Europe Ready for Dynamic Pricing?
A Reality Check for the Private Sector
and Giuliano Mingardo
QUINTA Consulting, D-60323 Frankfurt, Germany; email@example.com
Erasmus Centre for Urban, Port and Transport Economics, Erasmus University Rotterdam,
3062PA, Rotterdam, The Netherlands
* Correspondence: firstname.lastname@example.org
Received: 27 January 2020; Accepted: 26 March 2020; Published: 31 March 2020
Abstract: Both Revenue Management (RM) and Dynamic Pricing (DP) are common practices in
many industries—e.g., airlines and hotels—but they are still relatively unknown in the parking
sector. In Europe, with the exception of for airport parking and in some pilot tests, DP is rarely used
by private parking operators or local authorities. The main objective of this conceptual paper is to
set an agenda for introducing DP in the private parking sector at a larger scale. After a short review
of the existing academic and gray literature, we describe the requirements and instruments that
parking companies need to make use of RM. Next, we shortly report on the major existing and/or
planned DP parking schemes in Europe. We continue by providing a comprehensive reality check
discussing the major challenges the sector faces to apply DP. We conclude by suggesting a road map
for private parking operators to successfully implement RM and DP. Finally, we give some
indications for future research.
Keywords: parking; revenue management; dynamic pricing; road map
Revenue Management (RM) and Dynamic Pricing (DP) are common pricing practices in many
industries—e.g., in the airline and hotel industry—but they are still relatively unknown in the
parking sector. We define RM according to Kimes as the “[…] process of allocating the right type of
capacity to the right kind of customer at the right price so as to maximize revenue or yield“  (p.
15), with DP being “[…] a method whereby the available price changes dynamically over time due to
changes in demand/capacity/availability” (p. 118). In Europe, with the exception of airport parking
and some pilot tests, DP is rarely used by private parking operators.
In the last decade, the parking sector has witnessed the introduction of several technological
innovations: dynamic information systems, mobile apps to find, pay and (sometimes) reserve on-
and/or off-street parking, automated number plate recognition (ANPR) systems, sensors, and
cameras to enforce paid parking. These innovations have influenced motorists in the way they make
use of parking, local authorities in the way they implement parking policies, and operators in the
way they manage their parking facilities.
However, pricing schemes in parking have evolved at a slower pace if at all. On the one hand,
initial forms of RM have largely been applied by the majority of private parking operators:
differentiated tariffs according to the time of the day or day of the week, according to the length-of-
stay (short- vs. long-term parking), “early bird” or “daily” tariffs, etc. On the other hand, DP is almost
absent in the private parking sector apart from some recent pilot tests in Germany, France, or
Norway. While the German APCOA Group has initiated some pilot tests to implement DP in bigger
German cities, Belgium Interparking Group has started a pilot test for DP in the French city of Nimes.
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While APCOA’s DP model includes up to five different tariff levels for short-term parking depending
on the occupancy level of the car park, Interparking’s DP model is slightly more advanced since it is
able to consider more input variables, e.g., the arrival day, time of arrival, and length-of-stay. Up to
256 different price lists are theoretically applicable [3,4].
Another private sector which had made some progress in implementing DP at a larger scale are
European airports. With the advent of pre booking systems for airport parking, operators were able
to segment their customers based on their time or price elasticity and offer them a customized parking
product at different prices depending on the lead time, length-of-stay, or occupancy level of car parks
[5,6]. At present, very few local authorities have introduced DP schemes. With the exception of
airports and some cities such as San Francisco  or Madrid , there are only few examples of cities
that have already applied DP or RM for on- and/or off-street parking at a larger scale. Table 1 presents
an overview of European cities that have implemented, or are planning to implement, some forms of
RM or DP in parking.
Table 1. Examples of European cities having implemented or having announced to implement
Revenue Management (RM) or Dynamic Pricing (DP) in parking.
City Facility or area On-street / off-street Operator Start
Madrid City center on-street Private 2006
Basel City center on-street not yet decided 2020
Oslo Gardermoen Airport off-street Private 2018
London Queensway Car Park Baywater off-street Private 2008
Moscow City center on-street Municipality 2012
San Sebastian City center on-street Municipality 2013
The main objective of this paper is to set an agenda for introducing RM or DP in the private
parking sector at a larger scale. This is a conceptual paper inspired by grounded theory: our research
is performed by observing and analyzing already existing information on parking pricing based on
the author’s own professional experience, the literature, and existing real case studies.
The reminder of this paper is structured as follows: after a short review of the existing literature,
we describe the requirements and instruments private parking companies need to make use of RM
or DP. Next, we shortly report on the few existing examples of RM and/or DP in the private and
public parking sector. We continue with providing a qualitative reality check discussing the major
challenges the private sector faces to apply DP mechanisms. We conclude by suggesting a road map
for private parking operators to successfully implement RM and DP and finally give some indications
for future research.
2. Previous Research
Already in 2006, Arnot and Inci  suggested that, to reduce welfare loss, parking rates should
not be fixed to linear fees. Approximately a decade later, Inci  observed that cities were able to
apply RM and DP to parking through the application of smart parking systems in the attempt to
achieve the 85 percent optimal occupancy rate level, as suggested by Shoup . Yet, there is little
academic literature on these topics.
Considering the application of RM in the parking sector, Guadix  suggests an application
model and, based on occupancy data, a demand forecast which is based on future drivers. The
forecasts are used to distribute car parks’ available capacity at different time slots, taking into account
different promotional offers, parking behavior, and demographic factors. Teodorovic and Lucic 
discuss the concepts of parking reservation and parking RM proposing a special parking space
inventory control system, which can accept or reject a driver’s request for parking. The system
assumes that (future) traffic arrival patterns are known to the parking operator. Results suggest that
it is possible to maximize revenues. Gaudix et al.  on the contrary propose to make use of
deterministic and stochastic models to optimize the revenue of a parking lot under the assumption
that individual and subscribed drivers exist. Madsen et al.  study demand elasticity for on-street
parking in Copenhagen analyzing data for the period from 2008 to 2011. They suggest that a spatially
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differentiated parking fee is necessary to create an optimal parking pattern. Earlier, Kelly et al 
found an average price elasticity of demand of −0.29, analyzing on-street parking data in Dublin.
The literature on DP applied to parking seems to be slightly richer, especially over the last five
years. Qian and Rajagopal  investigate the use of dynamic parking fees to better manage the
parking demand generated by morning commuters. Using linear programming techniques, they
show that the best system performance is achieved by pricing the preferred location to such a level
that keeps occupancy levels around 85-95 percent. As a result, they provide empirical evidence for
the optimal occupancy ratio rule of thumb suggested by Shoup  and used it as a reference for
many other cities. Mackowski et al.  develop a dynamic non-cooperative bi-level model to set
real-time parking prices that eliminate cruising, suggesting that their model could fit a parking
Zheng and Geroliminis  develop a macroscopic fundamental diagram multimodal traffic
modeling approach that applies different pricing strategies for parking. Their model, that
incorporates price competition between parking operators, suggests that a dynamic parking pricing
system is effective in order to reduce cruising and congestion.
Tian et al.  formulate a DP model for parking reservations as a stochastic dynamic
programming, in which the optimal price maximizes the operator’s expected revenue. They assume
a Poisson process for the arrival requests with the arrival intensity being influenced by DP. They
show that a DP scheme can generate significant improvements in revenue and inventory during peak
periods while also significantly reducing travelers' cruising costs.
Next, Pierce and Shoup  analyze the data generated in the first year by SFpark, the program
introduced by the city of San Francisco to adjust on-street parking fees based on different occupancy
levels. This is the first prominent example for the application of DP to on-street parking at a larger
scale in the world. The authors focused on price elasticity of demand, finding an average value of
−0.4 with large variations according to the time of the day, the day of the week, and the location
within the city.
Finally, Fichman  performs an analysis of the DP parking system implemented in Pittsburgh
(USA), finding evidence for a change in drivers’ behavior in response to dynamic fees. The author
clearly found a time lag between the change in behavior and the adjusted prices, suggesting the
important role of communication to achieve an optimal situation.
3. Basics of Revenue Management and Dynamic Pricing
The focus of this section is to shortly explore the historical background as well as basic theoretical
requirements and instruments for the application of RM and DP.
3.1. Backgroung, Definition and Requirements
Following the structural changes that took place in the late 1960s and 1970s in the air traffic
sector—i.e., the implementation of the computer aided reservation system (CRS), the introduction of
special fares, the deregulation of the US-American air traffic in 1979 and the advent of low-cost
carriers—most airlines dismissed their rigid tariff structure and introduced DP and inventory
There have been several attempts to define RM. Liebermann  (p.34) describes the wide range
of definitions with the following: “[…] if you ask ten hoteliers what it is, you are apt to get at least
five, and possibly ten, different answers”. Kimes  (p.15) defines RM of traditional airlines as a
“[…] process of allocating the right type of capacity to the right kind of customer at the right price so
as to maximize revenue or yield”. Following this definition, RM can be seen as a quantitative strategy
aimed to assign existing capacities to the demand in order to maximize revenue, under simultaneous
price and capacity constraints.
According to Kimes , companies have to meet the following six basic requirements to apply
• Relatively fixed capacities
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• Possibility of efficient market segmentation
• Inability to store and perishability of service
• Possibility of advanced booking of service
• Strong fluctuation and uncertainty of demand
• Low marginal sales costs and high marginal production costs.
3.2. Instruments of RM
The instruments of RM need to be viewed separately from the application requirements.
According to Friesen and Reinecke , there are five instruments of RM (see Figure 1):
• Data compilation
• Price steering
• Inventory management
Figure 1. Instruments to implement RM (based on Friesen and Reinecke ).
The booking data base serves as the basis to forecast future demand-related behavior, as well as
for demand structures. It contains figures on past and current booking data (capacities, tariffs,
utilization), structure of demand, past cancellation and no-show rates, competitive data, special
occasions and price elasticity. The data base coupled with forecast models provides input for the
optimization, divided into price management [steering] and capacity [inventory] management.
Price management first involves a market segmentation based on the heterogeneous
willingness-to-pay (WTP) of customers. For example, it is normally assumed that business travelers
are price-inelastic, but time-sensitive, while leisure travelers are price-elastic, but not time-sensitive.
Based on the identified market segments, rate fences are determined to separate the different classes
of carriage—e.g., first class, business class, economy class. The different WTP of consumers is the
basis for a segmented price discrimination, which aims for skimming off the consumer’s surplus.
The fact that segmented price discrimination acts on the assumption of a deterministic pattern
of demands necessitates allocation rules. These rules display the course of booking of stochastic
demand at optimal revenue. For that reason, price discrimination is complemented by the
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optimization element of inventory management. In contrast to simple price discrimination including
pre-defined price levels for a certain number of seats, RM reacts to the irregularity of air traffic
demand by constantly adjusting the number of available seats at different prices .
Firstly, the allocation of the restricted capacity within the booking classes and the booking
limitations are being identified on the basis of demand forecast models. Companies need to
determine whether a booking request is confirmed or is withheld for a full-paying customer later on.
In order to solve this operational problem, nesting can be applied—implying that part of the total
capacity for the demand for booking classes with a higher revenue potential is being withheld [24,25].
Additionally, another reliable element of capacity management is overbooking. By deliberately
selling more seats than actual available capacities, the risk of insufficient capacity utilization is
reduced through “no shows”—i.e., passengers who do not show up at departure time - or unplanned
“go shows”—i.e., passengers flying on a flight different from that one they had a reservation for.
Therefore, overbooking serves as a balance of risk between revenue loss and displaced revenue.
Finally, price-quantity steering—as another substantial element of inventory management—
draws on data of price discrimination and product allocation, thus, defining tariff classes to which
parts of the capacity are being assigned. Furthermore, both prices and corresponding capacities are
being adjusted to the expectations of the course of booking. This adjustment process is already part
of the monitoring process, which records the quality of the optimization process as well as making
continuous modifications in price and capacity while reporting in real-time to global distribution
3.3. From RM to DP
DP, also known as demand-based, peak-load, or surge pricing, is a pricing strategy that is
directly related to price steering within RM. Supported by digitalization, DP found its way from the
travel and transport industry into several other sectors such as retail, advertising, or electricity. Based
on specific market conditions or customer characteristics, companies set flexible prices for products
or services in order to account for fluctuations in demand .
Subject to the price elasticity of demand, DP features price increases when demand is strong and
price decreases when demand is weaker to stimulate demand. Thus, a better capacity utilization and
a higher exploitation of WTP is aimed for. However, DP as it is currently applied does not account
for what each individual consumer is willing-to-pay, i.e., personalized DP .
While DP is already an established form of price discrimination in the airline industry, in other
sectors there is an inherent risk that customers feel they are being treated unfairly. Based on the
behavioral concept of price fairness and with almost perfect price transparency, DP could entail
negative word-of-mouth, buying restraints, or even customer rejection .
In summary, the management of the inventory, not of the price, is paramount for RM involving
airlines. Dynamic prices are understood to be a consequence of the strict availability allocation of
differently priced seat capacities [23,26].
4. Application of Dynamic Pricing in the Parking Sector
While DP is an established form of pricing in many sectors today—e.g., airlines, hotels, rental
car companies, and tour operators—the parking industry is clearly lagging behind, both in on- and
off-street parking. Even though parking theoretically meets all requirements for the application of
RM suggested by Kimes —see Section 3.1—it has to be elucidated why the common practice for
short-term parking fees is still to make use of tariff models that are based on hourly fees (or fractions
of hours, i.e., 10 or 15 minutes), reaching a daily maximum fee .
At present, with the exception of European airports and some promising pilot tests established
in European cities (see Table 1), there are only few examples of DP applied in the private parking
sector. To the best of the authors’ knowledge, the two most prominent ones are pursued by public
parking authorities, namely San Francisco’s SFpark program and Madrid.
SFpark is a demand-responsive parking pricing project introduced in a pilot area by the city of
San Francisco (USA) in 2011. The parking meters adapt the parking fee according to location, time of
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the day, and day of the week with the goal of keeping 15 percent of spaces available at any time. The
scheme applies to approximately a quarter of all metered parking spaces of the city and to three
quarters of all city-owned parking garages . A project evaluation  suggests that DP applied to
parking generates benefits for both the local authority and drivers: average parking rates were 1
percent lower on average, while the availability of parking improved and cruising (and related
The city of Madrid (Spain) introduced a DP system in July 2014 in which the parking fee varies
according to the type of vehicle and actual demand. The second aspect is that this system is an
example of DP application for on-street parking fees. Drivers pay a reduced fee of 20 percent or 10
percent when the occupancy level is below 30 percent and between 30 percent and 60 percent,
respectively; the regular fee is applied when the occupancy ratio is between 60 percent and 85 percent;
when the capacity utilization is between 85 percent and 95 percent or above 95 percent, drivers pay
10 percent or 20 percent more .
Airports were among the first in the private parking sector to implement DP and RM. Driven by
intense competitive pressure of off-airport parking suppliers, a strong demand of digital-savvy
customers to pre-book parking spaces in advance, and technological leaps in pre-booking technology,
the first airport operators, especially in Oceania and Europe, started implementing DP and RM
around ten years ago.
Big European hub airports like London-Heathrow, Amsterdam Schiphol, or Oslo Gardermoen
are now making use of many instruments of RM and DP as described in Section 3. With the help of
either internally developed RM systems or external RM systems, suppliers that are specialized in car
parking airports use DP mostly for their pre-booking businesses to better control their inventory and
account for demand peaks, e.g., during the holiday season . Hub airport operators also expect their
car parking revenue to be increased by applying RM and/or DP. Interestingly, American airports,
e.g., the New York Airport Authority or Denver Airport are still lagging behind in this area but are
investing heavily to catch up with their European peers.
Mid- to smaller-sized airport operators in Europe often do not have the resources and funding
to decide to implement RM or DP for their car parking businesses. Hence, instead of using
professional RM systems, those airports make use of the possibility to implement multiple price
buckets in their pre-booking systems, i.e., inventory buckets of a car park priced at different tariff
levels. Even though this form of price discrimination does not fully exploit the maximum WTP of
customers, it represents a promising way to overcome the ‘one-size-fits-all’ pricing approach still
prevailing in the private and public parking sectors .
Every vacant on- or off-street parking lot represents foregone turnover and profit for private and
public parking operators alike and accordingly an inefficient use of scarce (urban) space. Dynamic
steering of tariffs combined with active inventory control gives the parking sector the unique chance
to catch up with other industries, e.g., the airline industry, which have been using this pricing
mechanism very successfully for decades (see Section 3).
In the next section, we describe the preconditions that have to be met for DP to be applied in the
private parking sector in Europe.
5. Is the Private Parking Sector in Europe Ready for Revenue Management and Dynamic Pricing?
In this section, we first prove that parking, as a service, meets all the theoretical requirements of
RM (based on Kimes ). Second, we discuss whether parking, as a sector, has all the instruments
available to implement RM [12,23]. Third, we conduct a reality check discussing the major challenges
the private sector in Europe faces to apply DP mechanisms.
Table 2 provides an overview of the basic requirements of RM applied to the private parking
sector in urban areas. It clearly shows that parking is a service that meets all theoretical requirements
. Capacity is fixed in the short run. Motorists, like airline passengers, have different reasons to
travel and, accordingly, can be segmented into different customer groups. Clearly, an unused space
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at a specific time and place cannot be used at another time and location, making storage of a parking
space literally impossible. Advanced selling and/or booking is already a reality in parking, though
this is often constrained to event-related activities—e.g., booking a parking space for a theatre visit
or a cruise trip. As suggested by many authors [7,16,31,32], demand for parking varies across the day
of the week, the week, the year and across different locations. Finally, while the incremental cost of
selling an additional parking spot to a motorist is close to zero, the cost of providing an additional
space is related to large investments.
Table 2. Requirements for RM applied to on- and off-street parking in urban areas.
application of RM
Fulfilment in the
parking sector? Explanation
Limited capacity Yes
In urban areas, parking supply is fixed in the short-
term and any car park has a limited number of parking
Possibility for market
Motorists can be segmented, for example based on
reason of the trip (i.e., work, leisure…) or the length of
stay (short- vs long-term parking)
Inability to store and
perishability of service Yes If a parking lot is unused at a specific time, it cannot be
stored and used at a later moment
Possibility of advanced
selling/booking Yes Thanks to new technological developments, pre-
booking is already a reality in parking
Strong demand fluctuation Yes Demand for parking fluctuates across the day, week,
Low marginal sale costs and
high marginal production
The incremental cost of selling an additional parking
lot is close to zero, while if capacity is fully utilized, the
cost of an additional unit is very big
Table 3 describes the instruments of RM and its applicability in the private parking sector, which
need to be viewed separately from the application requirements. Single transactional data (i.e., on-
and offline data) serve as a basis to forecast future demand patterns (e.g., pre-booking lead times,
length of stay etc.) as well as demand structures (e.g., short-term vs. long-term parking). The
transactional data base together with the forecast algorithm represents the information input for the
optimization: price steering and inventory management. Price steering is composed of the ability to
differentiate prices for various market segments with an appropriate fencing mechanism. In the
private parking sector, both price differentiation and market segmentation are still only
rudimentarily used, since the ability to appropriately fence a parking lot is quite limited in a single
car park. Inventory management as the second most important pillar of RM is often overseen in the
parking industry. There are very few car parks with 24/7 occupancy levels of 50 percent or higher.
Surprisingly, not all parking operators are aware of the benefits of increasing the occupancy level for
a car park’s overall profitability. At the moment, only some airport operators and some of the largest
private car park operators in Europe apply inventory management to better use their overall car park
capacity during times of strong demand, but also during times of weak demand. Monitoring the
success of applying RM is crucial in any industry. Hence, for private car parking companies, it should
become a routine to continuously extract, clean, mine, and analyze data to be able to regularly review
and readjust single instruments of the whole RM process.
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Table 3. Instruments for RM applied to on- and off-street parking in urban areas (authors’ own
Availability in the
parking sector? Explanation
Transactional data is available in every parking management
system (PMS) as the basis to forecast future demand. However,
only few companies still make use of it.
Plenty of forecasting models are available that can be applied
to the parking industry, but there is hardly any company or
local authority using them.
Price discrimination, market segmentation, and fencing are
hardly used in car parking except for by airports and for event-
At the moment, only some bigger airports apply inventory
management to better steer the capacity of their car parks.
Few local authorities monitor the usage of their car parks,
while bigger private parking companies have a proper
monitoring system in place.
5.3. Reality Check
Finally, we discuss the major challenges the private parking sector in Europe needs to overcome
to make DP a reality (see Table 4). At the moment, European private operators do not feel any urgency
to introduce DP, mainly because they might be afraid of possible negative reactions of customers.
Even though all private parking operators want to maximize revenues, none dares to be the first to
introduce DP on a large scale. They genuinely fear that motorists will simply not accept it .
As we have seen in the previous section, there is little experience with DP in parking. Despite
the success of SFpark, at present there are only few private parking operators in Europe seriously
introducing similar DP schemes.
There is plenty of data available in parking as every paid parking transaction is registered,
recorded and stored. The transactional data collected by a typical PMS (Parking Management
System)—i.e., date and time of entry and exit, length-of-stay, amount, payment method and
oftentimes also the number plate—allows for a comprehensive level of customer segmentation. Yet,
few parking operators make use of parking data to understand drivers’ behavior and/or to get more
insights on customers. Some private operators make more systematic use of the data, but usually for
management reports concerning turnover and occupancy ratios .
Effective internal and external communication is a key to success if the private parking sector in
Europe wants to introduce DP at a larger scale . Parking usually has a bad price image and a
questionable reputation among the general public, e.g., spaces are never enough, and it is always too
expensive. This bad image has its origin in the fact that private operators always focused on the “how
you have to pay for parking” and almost never on “why you have to pay for parking” when
communicating with customers. Despite being comparable with many common services such as
utilities, restaurants, or cinemas, parking companies still have to explain to their customer why they
have to pay for this service. Manville  (p. 141–142) describes this discussing by “[…] Parking
charges are a rent for using space, and the economic benefit of rent comes from collecting it, not in
how its revenue is used. […] Water companies don’t meter because (or entirely because) they need
to cover costs. They meter to prevent people from turning on the tap and then leaving for hours
without turning it off. […] With parking, somewhere this distinction got lost.”
Finally, in most European countries, relevant legislation allows parking operators and local
authorities to decide which pricing structure to implement  and, accordingly, does not represent
a big obstacle to overcome when applying DP.
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Table 4. Challenges that the parking sector must face to introduce DP.
Challenge Readiness Level of the
parking sector Explanation
change No sense of urgency to implement DP at the moment
Experience Little experience with DP, mostly in airport parking
Data and customer
Transaction data on parking patterns available e.g., to
Local authorities and parking operators usually don’t
have people with necessary skills available
Adequate marketing and communication effort is
necessary to overcome fear of losing customers
Permission to use DP in parking as long as displayed
when entering a car park
6. Conclusion: A Road Map for Dynamic Parking Pricing
To actively implement DP at a larger scale, we suggest for private parking operators in Europe
a migration model from the current static, “one-size-fits-all”-pricing model to a more DP scheme
through three steps: (i) understand, (ii) evaluate, and (iii) communicate.
First, understanding customer behavior should be the initial step. On- and off-street PMS
virtually register every single parking transaction that involves a payment. This huge amount of
transactional data should be used to better understand (and predict) drivers’ behavior. At the
moment, the biggest challenge is that those who are or should be interested in better understanding
parking patterns (e.g., parking operators) usually do not have the right level of skills to thoroughly
analyze and understand the data; and those who have proper skills to make use of the richness of the
data (e.g., PMS providers and mobile pay applications providers) usually are not heavily interested
in using or sharing data insights to better understand customers’ behavior.
Second, more pilot tests with DP must be carried out, monitored, and evaluated. APCOA’s and
Interparking’s first attempts in Europe as well as SFpark’s venture in the US are rare but good
examples showing that the introduction of DP in parking yields benefits for everyone. Thanks to the
comprehensive and rigorous evaluation performed by the local authority, the SFpark scheme is now
enlarged to other areas of San Francisco  and other cities are seriously considering introducing
DP in parking as well (e.g., Milwaukee  in the U.S. and Basel  in Europe). A diligent evaluation
has to consider the effect of DP on revenue, demand (i.e., occupancy levels, turnover, length-of-stay)
traffic flows, competition between different parking locations, and public acceptance.
Third, internal and external communication is the most crucial aspect. Contrary to the airline
industry, the paid private parking sector has—as already described—a (relatively) bad image among
consumers. Most drivers still find it difficult to understand why they have to pay for a parking space.
This bad reputation is one of the key reasons for the fear from private parking operators in Europe
towards implementing DP in their off-street facilities. To overcome this anxiety, three aspects are
crucial from the authors’ point of view: (i) how the customer perceives and accepts the rules of pricing
(e.g., “the later you book the more expensive the ticket becomes”) , (ii) how exactly the mechanism
of DP works—according to Haws and Bearden ) the fairness perception is different for a scenario
where the supplier sets prices with customers participating in the price-setting process, e.g., “pay-
what you-want”, “name-your-own-price” or any kind of auctioning or bidding process., and (iii) how
the price discrimination is framed .
Finally, we provide some suggestions for further research. First of all, the few existing and recent
examples of DP in private parking should be further explored and analyzed. On the one hand, we
invite parking operators to share their data with the academic world; on the other hand, academics
could put emphasis on the analysis of DP schemes that were recently introduced by some private
operators to better understand drivers’ behavior and customers’ demand patterns. Experimental
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research could be used to test for (un)fairness perceptions among customers which together with
expert interviews could help us to better understand the reluctance of private operators in Europe to
implement RM and/or DP in parking.
Author Contributions: Conceptualization: M.F., G.M., Methodology: M.F., G.M., Acquisition and analysis of
data: M.F., Writing: M.F., G.M, Reviewing and editing: M.F., G.M. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
1. Kimes, S.E. A strategic approach to yield management. In Yield management; Ingold, A., McMahon-Beattie,
U., Yeoman, I., Eds.; Thomson Learning: Padstow, Cornwall, UK, 2000.
2. Krämer, A.; Friesen, M.; and Shelton, T. Are airline passengers ready for personalized dynamic pricing? A
study of German consumers. J. Revenue Pricing Manag. 2018, 17, 115–120.
3. Ruh, M. Yield management in Hamburg, Leipzig and Stuttgart. Parking Trend Intern. 2019,33, 10.
4. Mollaret, G. Un Parking de Nîmes Ajuste Ses Tarifs en Fonction de L’affluence. 2019. Available online:
(accessed on 12 November 2019).
5. Friesen, M. Parking still has room to grow. Parking Trend Intern. 2013, 27, 37.
6. Tian, Q.; Yang, L.; Wang, C.; Huang, H.J. Dynamic pricing for reservation-based parking system: A revenue
management method. Transp. Policy 2018, 71, 36–44.
7. Pierce, G. and Shoup, D. Getting the price right. J. Am. Plan. Assoc. 2013, 79, 67–81.
8. Available online: https://www.group-indigo.com/en/reference/controlled-on-street-parking-in-madrid/
(accessed on 12 November 2019).
9. Arnott, R.; Inci, E. An integrated model of downtown parking and traffic congestion. J. Urban Econ. 2006,
10. Inci, E. A review of the economics of parking. Econ. Transp. 2015, 4, 50–63.
11. Shoup, D. The High Cost of Free Parking; American Planning Association: Chicago, IL, USA, 2005.
12. Guadix, J. Practical Pricing for the Car Park Industry. In Revenue Management; Yeoman, I., McMahon-
Beattie, U., Eds.; Palgrave Macmillian: London, UK, 2011.
13. Teodorović, D.; Lučić, P. Intelligent parking systems. Eur. J. Oper. Res. 2006, 175, 1666–1681.
14. Guadix, J.; Cortés, P.; Muñuzuri, J.; Onieva, L. Parking revenue management. J. Revenue Pricing Manag.
2009, 8, 343–356.
15. Madsen, E.; Mulalic, I.; Pilegaard, N. A model for estimation of the demand for on-street parking. Available
online: https://mpra.ub.uni-muenchen.de/52361/1/MPRA_paper_52301.pdf (accessed on 24 August 2018).
16. Kelly, J.A.; Clinch, J.P. Temporal variance of revealed preference on-street parking price elasticity. Transp.
Policy 2009, 16, 193–199.
17. Qian, Z.; Rajagopal, R. Optimal dynamic parking pricing for morning commute considering expected
cruising time. Transp. Res. Part C Emerg. Technol. 2014, 48, 468–490.
18. Mackowski, D.; Bai, Y.; Ouyang, Y. Parking Space Management via Dynamic Performance-based Pricing.
Transp. Res. Procedia 2015, 7, 170–191.
19. Zheng, N.; Geroliminis, N. Modeling and optimization of multimodal urban networks with limited parking
and dynamic pricing. Transp. Res. Part B Methodol. 2016, 83, 36–58.
20. Fichman, M. An Evaluation of Pittsburgh’s Dynamically‐Priced Curb Parking Pilot; University of Pennsylvania:
Philadelphia, PA, USA, 2016.
21. Lieberman, W.H. Debunking the Myths of Yield Management. Cornell Hotel Restaur. Adm. Q. 1993, 34, 34–41.
22. Kimes, S.E. The Basics of Yield Management. Cornell Hotel Restaur. Adm. Q. 1989, 30, 14–19.
23. Friesen, M.; and Reinecke, S. Wahrgenommene Preisfairness bei Revenue Management im Luftverkehr.
Mark. Rev. St. Gallen 2007, 24, 34–39.
24. Botimer, T.C. Airline Pricing and Fare Product Differentiation; MIT: Cambridge, MA, USA, 1994.
25. Belobaba, P. Airline Yield Management: An Overview of Seat Inventory Control. Transp. Sci. 1987, 21, 63–73.
26. Talluri, K.T.; Van Ryzin, G.J. The Theory and Practice of Revenue Management; Springer: Boston, MA, USA;
Dordrecht, The Netherlands; London, UK, 2004.
Sustainability 2020, 12, 2732 11 of 11
27. Friesen, M. New customer-centric tariff models—Improved value-based parking for operators and
customers alike. Parking Trend Int. 2013, 27, 38–41.
28. Available online: http://sfpark.org/about-the-project/ (accessed on 24 August 2018).
29. San Francisco Municipal Transportation Agency (SFMTA). 2014. SFpark Pilot Project Evaluation. Available
online: http://direct.sfpark.org/wp-content/uploads/eval/SFpark_Pilot_Project_Evaluation.pdf (accessed
on 24 August 2018).
30. Available online: https://espaciocoches.com/tarifas-de-la-zona-azul-en-madrid/ and
b3128fb9458fe410VgnVCM1000000b205a0aRCRD (accessed on 24 August 2018).
31. Milosavljević, N.; Simićević, J. User response to parking policy change: A comparison of stated and
revealed preference data. Transp. Policy 2016, 46, 40–45.
32. Simićević, J., Milosavljević, N., Maletić, G.; Kaplanović, S. Defining parking price based on users’ attitudes.
Transp. Policy 2012, 23, 70–78.
33. Manville, M. Parking Pricing. In Parking—Issues and Policies; Ison, S., Mulley, C., Eds.; Emerald Group
Publishing Limited: Bingley, UK, 2014.
34. Mingardo, G.; van Wee, B.; Rye, T. Urban parking policy in Europe: A conceptualization of past and
possible future trends. Transp. Res. Part A Policy Pract. 2015, 74, 268–281.
35. Available online: http://sfpark.org (accessed on 24 August 2018).
36. Available online: https://urbanmilwaukee.com/2018/09/13/city-hall-demand-based-parking-meters-
coming/ (accessed on 7 September 2018).
37. Available online: http://www.badische-zeitung.de/basel/parkgebuehren-nach-nachfrage--153356514.html
(accessed on 24 August 2018).
38. Haws, K.L.; Bearden, W.O. Dynamic Pricing and Consumer Fairness Perceptions. J. Consum. Res. 2006, 33,
39. Weisstein, F.L.; Monroe, K.B.; Kukar-Kinney, M. Effects of price framing on consumers’ perceptions of
online dynamic pricing practices. J. Acad. Mark. Sci. 2013, 41, 501–514.
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