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issn 0391-8440
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international journal of transport economics
vol. xli · no. 3 · november 2014
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CONTENTS
Eddy Van de Voorde, Patrick Verhoeven, The economics of port
authority reform a framework for ex-post evaluation
Romeo Danielis, Lucia Rotaris, Andrea Rusich, Eva Valeri, Understanding the demand for carsharing : lessons from Italian case studies
Christina P. Milioti, Matthew G. Karlaftis, Multimodal public
transport demand : a cointegration time-series approach
Pablo Coto-Millán, Gustavo García-Melero, Rubén SainzGonzález, Sensitivity of the subjective value of travel time for different
microeconomic models : empirical evidence for university students
Martin Koning, Pierre Kopp, Are bicycles good for Paris ?
Xavier Fageda, Juan Luis Jiménez, Ancor Suárez-Alemán, Assessing Airlines : quality as a competitive variable
Index to Volume XLI
297
327
361
383
399
425
439
international journal of transport economics
issn 0391-8440 · e-issn 1724-2185
vol. xli · no. 3 · november 2014
UNDERSTANDING THE DEMAND FOR CARSHARING :
LESSONS FROM ITALIAN CASE STUDIES
1
Romeo Danielis* · Lucia Rotaris*
Andrea Rusich* · Eva Valeri*
Abstract : The aim of this paper is to estimate the potential demand for carsharing, to this
aim a model which calculates the total generalized cost for a given mobility pattern and
transport mode mix is developed. The model considers : a) that a person sometimes travels with friends and family, and therefore shares the travel expenses and/or satisfies several
travel needs, and b) that uses in given time period more than one mode of transport. The
parameters of the model are derived by detailed, face-to-face, computer-assisted interviews.
A limited number of interviews have been so far completed. However, they hint to some
very interesting empirical evidence. It is found that car ownership is currently very high in
the Italian families and that the car is used extensively both for work\study and, especially,
for other-than-work\study trip purposes. Offering a carsharing service (CS) would enhance
the mode choice and could, in some cases, lower the total mobility costs. The respondents
assign quite a large value to the pleasure of owning a car, much more so than the pleasure
of being carsharing users, both for workers and for students. Consequently, the respondents
would dislike not owing a private car, while having the choice between the private and the
carsharing car is preferred especially by the students. The mobility cost indicators reflect,
but not perfectly, the preference-based choices of the sample. Three individual case studies
are further analyzed. They have been defined as : a low, a medium and a high mobility case
study. The low mobility case study shows that these persons would largely benefit from the
existence of a CS service, they would use it occasionally and they would probably be willing
to forgo the private car. The medium mobility case study shows that the variables parking
time, access time and CS fare can easily switch the balance between convenience and inconvenience of using CS. The higher mobility case study in a small town setting demonstrates
that in such circumstances the prospects for a viable CS service are rather bleak.
Keywords : carsharing, transport modeling, passenger transport, potential demand estimation.
1. Introduction
C
arsharing (CS) is rapidly growing in many countries and cities. According to an estimate of the Transportation Sustainability Research
* Dipartimento di Scienze Economiche, Aziendali, Matematiche e Statistiche, Università
degli Studi di Trieste.
Corresponding author : Romeo Danielis : danielis@units.it.
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Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
Center at U.C. Berkeley 1 in December 2012 there were an estimated 1.7 million car-sharing members in 27 countries, including so-called peer-to-peer
services. In Italy, carsharing represents the most interesting development in
urban traffic of the last years. Although several public companies and privatepublic companies offered carsharing services since at least a decade in 12 Italian cities, the market entrance of Car2Go in Milan in 2013 was a real “game
changer”. Car2Go entered with 600 Smart ForTwo introducing the one-way
system, competing with the existing companies such as GuidaMi, EqSharing and E-vai, already active with conventional and electric cars but offering
round-trip service only. 2 The consumers’ reaction was enthusiastic and other
firms followed : Enjoy (a joint company of Fiat, Trenitalia and Eni) deployed
600 Fiat 500 and 500L, and Twist VW with planned 500 Volkswagen Up. In
March 2014, Car2Go started offering its service also in Rome with 500 Smart
ForTwo and in Florence. In all cities the new CS companies reported rapidly
increasing membership and use.
As the market for CS develops, a number of questions become relevant
both to CS companies and to policy makers : what is the potential demand
for CS ? Which cities\towns are better suited for CS ? Which segments of
the population are most likely to use it ? Which business models have most
potential ?
Unfortunately, so far, the ability of transport economists and modelers to
answers such questions has been limited. The section on the literature review will further discuss this statement. In our view, providing good answers
to these questions is difficult for a number of reasons.
First of all, the demand for CS is highly dependent on the mobility patterns. These patterns are partly recurrent (such as work or study commuting) and partly non-recurrent. The existing literature shows that CS is often
linked to the non-recurrent, less predictable ones.
Second, CS is basically an alternative to owning and using a private car, but
the two options can also co-exist : one can own a car and use a CS service at
the same time. For instance, the car can be used by another member of the
family. Moreover, CS has some advantages over the private car because it can
be parked at no cost in the city center, it can ran over reserved lanes, or it
1
Shaheen and Cohen (2012) discuss the worldwide market developments and emerging
trends.
2
In a one-way system vehicles can be accessed\returned to a different location, whereas
in the round-trip system, the vehicles should be returned to a location where they were
accessed. The one-way system creates unbalances in the cars’ spatial distribution, but it is
definitely preferred by the users, given the higher flexibility. Car2go reports that 9 out of
10 trips are one-way.
Understanding the demand for carsharing
329
can be conveniently used to conclude a trip partially carried out with public
transport mean. 1
A further element of difficulty is that owning one or more cars is often a
group or family decision. The choice might then be between owing a second
or third family car and using CS. A person\group of people might use jointly
many modes of transport or vehicles : the car, the bicycle, the motorbike, the
bus, the train, the taxi, the CS vehicle, the rent car, the airplane or walk, depending on the trip distance, trip purpose, weather, physical status, and so on.
In predicting the potential demand for CS ideally all these issues should be
accounted for. Moreover, making the task even more difficult, the demand
and the supply for CS should be taken jointly into account, not only for the
obvious reason the two interact in the marketplace setting the price of the
service, but also because they interact from a technological point of view especially when the one-way CS is available. As Jorge and Correia (2013) discuss
at length, in a one-way system the user determines where the CS vehicles are
parked, unless a costly repositioning system is operated. Consequently, the
availability of a CS car and the time needed to access a CS vehicle is not predicable, thus influencing the individual’s demand for the CS.
Moreover, the existing studies have shown that various, quite different
CS market segments exists : residents, students, tourists, customers making
short-term trips or out-of-town trips, each segment having specific demand
characteristics.
Finally, the CS systems are quite new, there are many types of firm offering the service (nonprofit organizations, commercial firms, subsidiaries of
car manufactures, car renting firms, public transport firms), who have little
experience and are still in search for a viable and profitable business model.
For all these reasons, modelling demand for CS is a quite challenging topic.
In this paper, Section 2 will review the growing literature with specific attention on demand modelling ; Section 3 presents our modelling effort based on
total mobility costs ; Section 4 reports some results from an initial set of interviews ; Section 5 presents three in-depth analysis of three interesting case
studies and Section 6 concludes.
2. Review of the literature
As CS develops and evolves, the academic and nonacademic research grows,
covering many aspects such as : tracking the growth and expansion of CS
1
In Italy, CS vehicles are located at train or metro stations. The joint use of the public
transport mean and the CS vehicle can be faster or cheaper than using the private car.
330
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
(Shaheen et al. 1998, 2004, 2006, 2009 ; Shaheen and Novick, 2005 ; Shaheen
and Cohen, 2007 ; Shaheen and Cohen, 2012) ; illustrating the administrative
or logistical aspects of running a CS organization (Kek et al., 2009 ; Fan et al.,
2008 ; Shaheen et al., 2003 ; Barth et al., 2003, 2004 ; Barth and Shaheen, 2002) ;
analyzing the actual usage of CS vehicles (Morency et al., 2008) and the
characteristics of the users (Burkhardt and Millard-Ball, 2006, TCRP, 2005 ;
Schaefers, 2013 ; Efthymiou et al., 2012) ; identifying the market segments
(Celsor and Millard-Ball, 2007 ; Musso et al., 2012 ; Le Vine, 2012 ; Schmoeller and Bogenberger, 2014) ; examining empirically how the adoption of CS
impacts vehicle kilometers, car ownership (Shaheen et al., 2006, Cervero
et al., 2007 ; Lane, 2005 ; Cervero and Tsai, 2004 ; Cervero, 2003) and health
(Kent, 2014) ; analyzing the energy, environmental and mobility impacts of
CS (Baptista, 2014) ; researching the familiarity with the CS system and the
willingness to accept it (Nobis, 2006 ; Loose et al., 2006) ; and, more recently,
estimating the possibility of using electric cars for carsharing in an urban or
a rural area (Wappelhorst et al., 2014).
Many and diverse shared-use vehicle business models have been launched
worldwide. Shaheen and Cohen (2012) list the following : neighborhood residential, business, governmental and institutional fleets, and college and university. The “neighborhood residential” one, apparently the most common,
focuses on mixed-use, urban, and residential neighborhoods. It can be either
open door or closed door. The “business” one provides exclusive-use vehicles
to clients that are shared among employees or departments. The “college\
university” model provides vehicle access at colleges and universities or is adjacent to campuses. The “government and institutional” one provides vehicles in place of governmental or institutional fleets. The “public transit” one
provides car at a public transit station or multi-modal nodes for ‘‘first and last
mile’ connectivity. The “one-way” carsharing enables a carsharing member
to return a shared vehicle to a different location from where the vehicle was
picked up. The “personal vehicle sharing” model involves privately-owned
autos employed in shared-use vehicle services. There are four sub-models
of personal vehicle sharing : 1) fractional ownership, 2) hybrid peer-to-peer
(P2P)-traditional carsharing, 3) P2P carsharing, and 4) P2P marketplace. The
“vacation\resort” provides hourly, shared-vehicle access at vacation resorts
and other tourist locations. Of course, these models are not mutually exclusive. The most successful carsharing companies in Italy nowadays, Car2go
and Enjoy, focus, in three large cities, simultaneously on the following market segments : i.e. business, college\university, government and institutional,
public transit, one-way, and urban tourism ones.
A specific concern of this review is that the literature on analyzing, mod-
Understanding the demand for carsharing
331
eling, simulating and forecasting the potential demand for CS is not abundant.
A pioneering paper is the one by Schuster et al. (2005). They set up a Monte
Carlo simulation of the economic decision to own or share a car on the basis
of major cost components and past car use. Many variables enter into the
model such as : CS membership cost, own car cost, mileage cost, depreciation, car age, purchase price, insurance, registration, financing, repairs, CS
time cost, hourly fee, travel time, number of trips, time at destination, route
time, and per mile cost/fee. For Baltimore, Maryland, they estimate that
the percentage of cars that would be cheaper to share rather than to own,
range from 4.2%, for the traditional neighborhood CS model, to 14.8%, for a
commuter-based CS model. In a city of a million people, assuming a 50% car
ownership ratio, it would amount to between 21 to 74 thousand shared cars,
which is a very large number compared with the current empirical evidence.
The main limitations of Schuster et al.’s model are, in our view, the choice
of focusing on cars instead of individuals, the almost exclusive consideration
of the monetary costs disregarding the role played by the non-monetary factors, the limited detail of the data on travel patterns, the lack of consideration of socio-economic characteristics of the decision makers.
Another major research effort it the one provided by Duncan (2010) who
seeks to quantify the market potential of CS in the San Francisco Bay Area,
defined as its ability to provide cost savings to those who adopt it in favor of
car ownership. Two research questions are investigated : a) what kind of car
usage patterns can CS accommodate in a cost effective manner ; and b) how
many cars have driving patterns that meet the threshold at which CS becomes
less expensive than auto ownership and how many households own such a
car. The 2000 Bay Area Travel Survey is used to address these questions. The
methodology comprises the following steps. Initially, the cost assumptions
for CS and car ownership are discussed. Then, the driving dimensions are
identified regarding the distance traveled, the length of time spent driving
and dwelling, and the driving frequency both for work and non-work travels.
Finally, based on the observed car ownership levels and travel patterns in
the San Francisco Bay Area, a rough estimated of the number of ownership or households who could financially benefit from CS is estimated. Nonmonetary considerations are then added to further filter out the people who,
based on socio-economic considerations (availability of more than a car per
driver or in the household, children in the household, availability of a SUV or
a collector’s car), are not “inclined” for CS. The figures that Duncan obtains
are also huge. A third of Bay Area households, that is more than 800,000
households, are estimated to have at least one car with a usage pattern that
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Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
is economical compatible with CS. This, combined with the quarter million
Bay Area households that do not own a car, makes an impressive number
(about a million households) of potential CS users. To put this in context, the
number of CS members across the entire US in 2009 was less than 300,000.
Since Duncan (2010) does not illustrate in detail the methodology used, nor
presents the data, it is not possible to exactly understand why the estimated
potential market is so different from the actual CS usage. Is it a matter of
lack of CS vehicle supply or a lack of knowledge of its saving potential in the
population due to insufficient promotion ? Were these explanations are valid,
the diffusion of CS would be just a matter of time, and the current growth
rates could demonstrate that the catching up process between supply and demand is actually taking place. A further explanation could be that the methodology used by Duncan (2010) is not yet complete or correctly applied, for
example because some relevant variables, such as the psychological ones or
the transaction costs, are not accounted for.
Ciari et al. (2012, 2013, 2014) use a completely different approach : they
use an activity-based microsimulation technique for the modeling of CS demand. The model is implemented via an existing multi-agent, activity-based,
travel demand simulator called MATSim. The simulator allows a synthetic
population which reflects census data to act as in a virtual word which reflects the existing infrastructures, the available transport services and activity opportunities. Each agent has its daily activity plan which s\he tries to
perform optimally according to a utility function that defines what is useful
for an agent. The utility function representing the generalized cost of travel
for car sharing includes five variables : 1) a constant intended to mimic the
minimum cost of CS ; 2) the walk path to and from the station ; 3) the time
dependent part of the fee ; 4) the distance cost and 5) the travel time cost. The
travel decisions are iteratively simulated as long as the overall score increases.
The equilibrium point of the simulated system is supposed to be a plausible
approximation of the real world behavior of the individual. The model is
applied to part of the Zurich city center. The aggregate results match what
is happening in reality. Defining the modal split as the percentage of trips
travelled with a certain mode disregarding the distance, the share of agents
using CS in the simulation is 0.6%. An estimate for the simulated area can be
obtained from a national study on CS usage leads to an estimate of 0.5% of
the trips. The implementation presented focuses on two particular features
of CS systems : the access to CS cars and the time dependent fee. Among
the many improvements possible, the authors quote the introduction of the
physical simulation of CS cars and of a reservation system with a limited
number of cars available at each station. Their long term goal is to build a
Understanding the demand for carsharing
333
predictive and policy sensitive model that can be used by practitioners and
policy makers in order to test different CS scenarios. Although they deem
that the model is still not quite ready for that use, it is, in their opinion, a
very promising advance in the search for reliable predictive models of CS
demand.
So far, we have discussed the contribution to estimating the demand for CS
via simulation models. They provide an estimate of the percentage of cars
which will be shared in a population, of the number of households that have
at least one car with a usage pattern that is economical conducive to CS, or
of the number of trips which can conveniently be made by CS. An alternative approach is to use discrete choice modelling, based on stated or revealed
preference data, to estimate the choice probability that CS is used among the
alternative mode available.
Catalano et al. (2008) take such an approach and apply it to the city of Palermo, Italy. The respondents could choose between private car, public transport, CS, and carpooling. The model is estimated using stated preference
data. The authors conclude that the CS market could vary from 5% to 10%,
depending from CS fare. Zheng et al. (2009) studied the potential CS market
at the University of Wisconsin-Madison by using a stated preference survey.
The results show that a respondent’s status at the university (e.g., faculty,
student, or staff ) has a strong influence, even more so than socioeconomic
variables such as income or car ownership, and that people’s attitudes play
an important role in their decision making. Furthermore, the ease of accessing a car is a critical factor. Kato et al. (2011) analyze membership choice
with a nested logit model. They find that car owners are more aware of CS
than non-owners but, as compared to non-owners, fewer car owners have
considered using CS services. This is judged to be a sign that the individuals have difficulty rearranging their lives to be car-free once they own a car.
They find a rational preference for lower membership fees, cheaper CS-use
charges, and better accessibility to the nearest CS. Furthermore, they note
that the comparative analysis of the four cities included in the study show
that the availability of CS services, public transportation service quality, trip
distance, and household income all influence CS membership. A limitation
of the discrete choice modelling approach is that, although very valuable to
understand the decision making process and to evaluate the role played by
the socio-economic characteristics and by the characteristics of the CS service in determining the demand, it does not lead to an exact quantification
of the potential demand for a given area, unless it is coupled with a simulation model that uses the discrete choice model coefficients and combines
334
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
them with a detailed description of the population, including the mobility
patterns, and of the CS supply characteristics.
A third approach is to use geographic information systems to assess CS
market potential. Celsor and Millard-Ball (2007) use this approach to analyze
the geographic market segments in urban areas. They find that neighborhood and transportation characteristics are more important indicators for CS
success than individual demographics of CS members. Their results indicate
that low car ownership has the strongest, most consistent correlation to the
amount of CS service in a neighborhood. By applying this tool to Austin,
Texas, they find that several Austin neighborhoods have the characteristics to
support CS (e.g., low car ownership rates and high percentages of one-person
households), but few Austin neighborhoods could support a high level of CS
service.
Building on the above-mentioned literature, the objective of this paper is
to contribute as follows.
Firstly, we develop a model able to estimate the demand for CS. The demand for CS can be expressed in various ways, for instance by : a) the number
of people who would become members because they would benefit from its
use ; b) the number of rentals in a given period ; c) the kilometers made by
the CS cars. All these indicators have been used in the literature and are used
in the public debate. Indicators b) and c) are probably the most relevant for
the CS companies ; indicator a) is the most common in the models because
it is closer to the economic approach to the decision making process. The
model we develop will produce a demand forecast in terms of indicator a),
although other outputs could be envisaged. The model described in Section
3 is based on the estimation of the total mobility costs for individual, considering his\her available transport mode mix. The status quo transport mode
mix is compare with CS-enhanced transport mode mix and with a hypothetical transport mode mix with the CS but without the private car. The model
is estimated by face-to-face interviews and the respondent is also asked to
choose the preferred transport mode mix.
Secondly, the paper contributes by identifying which are the most relevant
parameters in determining the potential demand for CS. In this paper, we
will focus on the role played by the parking searching time for the private car
(when no private garage is available), the time needed to get to or to come
from the CS car, the CS fare, and the number of trips made by car. Other variables might also play a relevant role such as the existence and the extensiveness of a public transport supply. These issues could be studied empirically
by comparing the evidence between different cities where CS is available or
Understanding the demand for carsharing
335
mathematically by simulating how the estimations performed via the model
depend on the parameters used. The latter approach will be used in this paper. The analysis is illustrated in Section 5 with the help of three explicative
case studies.
3. A total mobility cost model
The model (Figure 1) we develop evaluates the total costs of a mobility pattern given the transport mode mix. A large set of monetary and non-monetary costs are considered, including the value of travel time. The assumption
is that the total mobility costs are a relevant factor affecting the mode choice
of individuals. If, for an individual, adding CS to one’s mix or substituting CS
to the private car in one’s mix, decreases his\her total mobility costs, than
that individual is a potential CS user.
Socio-demographic variable: sex, age, occupa�on, income,
family members, auto ownership, etc.
Mobility pa�erns
Mode use: car, bus, train, taxi, motorcycle, bicycle, walking, carsharing
Cost and �me structure for each transport mode
Total generalize costs: monetary, nonmonetary, �me costs
Figure 1. Graphical illustration of the model.
The starting point is to collect information about the socio-economic characteristics of an individual such as gender, age, occupation, income, number of
family members, and car ownership. This information is both useful for market segmentation analysis and for estimating the total mobility costs (e.g., in
case of shared used of a private car). Next, two types of mobility patterns are
taken into account :
• Weekly mobility patterns for work\study purposes per type of transport
mode : number of trips, average distance per trip, number of trips with other
persons (family, and friends), and number of accompanying persons ;
• Weekly mobility patterns for other-than-work\study purposes (recrea-
336
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
tional, shopping, sport, ...) per type of transport mode : number of trips,
average distance per trip, number of trips with other persons (family, and
friends), number of accompanying persons.
The following modes of transport are considered : private car, carsharing,
bus, bicycle, motorbike, train, taxi. Finally, the generalized cost of performing both types of mobility patterns for each transport mode is estimated.
Each mode of transport entails monetary and nonmonetary costs. They are
the following.
• Car costs : car purchase price, number of years before the car value goes
to zero, fuel cost, road tax, insurance cost, estimated value of the uninsured
risks that the car is stolen or damaged, maintenance cost, estimated nuisance
cost of maintaining and refueling the car, opportunity cost of owning a garage, weekly parking expenses, amount of time needed to search for parking
at destination, estimated value of parking searching time, estimated value of
owning a car, value of having a car at own disposal. These costs can be summarized in 4 categories : annual monetary expenses, vehicle depreciation, opportunity cost of the garage, net nonmonetary costs and benefits. The fixed
costs are divided by the number of car users in the family. The total sum
represents the monetary and nonmonetary costs of having and using a car.
• Bus costs : single ticket, 10 ticket carnet, monthly pass or annual pass.
Depending on the number of rides made by the respondent the lowest figure
is used, assuming rational choice decisions.
• Train costs : single ticket, by-weekly pass, monthly pass or annual pass.
Depending on the number of rides made by the respondent the lowest figure
is used, assuming rational choice decisions.
• Taxi costs : computed on the basis of the prevailing Italian taxi fares and
on the number of taxi rides and ride distance reported by the respondent.
• Walking costs : estimated nonmonetary costs or benefits of walking.
• Bicycle costs : bicycle purchase price, number of years before the bicycle
value goes to zero, estimated nonmonetary costs or benefits of cycling.
• Motorbike costs : motorbike purchase price, number of years before the
motorbike value goes to zero, estimated nonmonetary costs or benefits of
using the motorbike.
• CS costs : membership fee, time fee, time needed to search and reach a
shared car (access time), estimated value of time for searching and reaching
a car, estimated nuisance value of booking a car, estimated nuisance value of
not finding a car when needed, estimated value of not having to take care of
a private car, estimated value of being a CS user. These costs can be aggregated in the total monetary cost of CS and in the net nonmonetary benefit
of CS.
337
Understanding the demand for carsharing
For each mode, the in-vehicle time costs are added. The sum of the financial and time costs provides us with an estimate of the individual’s total
generalized costs of his\her mobility pattern, given the mode choices made.
The model is operationalized with Excel and with Mathematica.
An Excel file is used to collect detailed, face-to-face, interviews. The questions asked for each mode are reported in the Appendix. The respondent’s
mobility patterns are collected under three scenarios :
1. The current scenario A_SQ (Status Quo), given that a CS service is not
available (Table 1). Such case currently applies to the citizens of Trieste and
of the Friuli Venezia Giulia Region.
2. A hypothetical scenario B where the respondent is requested to illustrate
which would be her\his mode choices if both the private car and the CS service were available, holding the mobility pattern constant.
3. A hypothetical scenario C where the respondent is requested to illustrate
which would be her\his mode choices if s\he had no private car and the CS
service were available, holding the mobility pattern constant (Table 2).
Table 1. Scenario A_SQ : current mobility patterns.
Current mobility patterns home-college during a weekly
Mode of
transport
Average
N° of round
distance per
trip journeys
journey
N° of journeys
made with other
people
n° of
accompanying
persons
Car
Motor bike
do not fill in
do not fill in
Bus
do not fill in
do not fill in
Train
do not fill in
do not fill in
Taxi
On foot
do not fill in
do not fill in
Bicycle
do not fill in
do not fill in
TOTAL
do not fill in
do not fill in
do not fill in
Current mobility patterns other than home-college during a weekly
Car
Motor bike
do not fill in
do not fill in
Bus
do not fill in
do not fill in
Train
do not fill in
do not fill in
Taxi
On foot
do not fill in
do not fill in
Bicycle
do not fill in
do not fill in
TOTAL
do not fill in
do not fill in
do not fill in
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Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
Table 2. Scenario C : most likely mobility patterns without a car and with the availability of a CS service.
Current mobility patterns home-college during a weekly
Mode of
transport
Average
N° of round
distance per
trip journeys
journey
N° of journeys
made with other
people
do not fill in
do not fill in
do not fill in
n° of
accompanying
persons
do not fill in
do not fill in
do not fill in
Motor bike
Bus
Train
Taxi
On foot
do not fill in
do not fill in
Bicycle
do not fill in
do not fill in
Carsharing
do not fill in
do not fill in
do not fill in
TOTAL
Current mobility patterns other than home-college during a weekly
Motor bike
do not fill in
do not fill in
Bus
do not fill in
do not fill in
Train
do not fill in
do not fill in
Taxi
On foot
do not fill in
do not fill in
Bicycle
do not fill in
do not fill in
Carsharing
do not fill in
do not fill in
do not fill in
TOTAL
The information about the individual’s current and hypothetical mobility patterns, together with all the other information on the vehicle costs,
the time costs and the non-monetary costs and benefits is used to estimate
the individual’s generalized costs in the three scenarios. The respondent is
then presented with the results and asked to rank the three scenarios, since
his\her choice might depend on factors other than those considered in the
model. For the purpose of this model, the information about the stated
preference is, however, not used. An interview lasts between 45 minutes
and 1 hour. Given the length of the interview, only 20 interviews have been
so far carried out. The aim is not to get an adequate statistical coverage to
draw general conclusions on the population, but to learn from each case
study which are the crucial factors that determine the choice of whether or
not to use CS.
The model is also operationalized with Mathematica in other to perform a
simulation at the individual level with the aim of testing how the generalized
Understanding the demand for carsharing
339
costs implied by the individual mobility choices would change by varying
the data or the model parameters. Some of these simulations are reported
in Section 5.
As already described, important characteristics of the model are that : a) it
keeps into account that each individual, or group of individuals, uses many
modes of transport to satisfy his\her mobility needs ; and that b) although
basically built on the decisions taken by a single person, it takes into account
that an individual sometimes travels with friends and family and, therefore,
shares the travel expenses or satisfies larger mobility needs.
However, some important factors could not be included in the model because highly person-, mode- and trip-specific. For instance, the use of the
family car by a young person implies a cost that is shared by all the members of the family. We attribute a fixed percentage share to each member,
although other specific family factors might determine that the cost is born
only, e.g. by the father. 1 Travel time reliability is an important mode-specific
attribute which is not included because it is not easy to have information, or
to economically evaluate this characteristic of the mode. Modes of transport
such as carpooling and urban rail transport were also not considered, the latter because available only in major cities.
Finally, one must admit that, as with many other transport choices, many
rational and irrational factors affect the choice among transport modes, including monetary, regulatory, technological, psychological, ideological ones.
Consequently, the choices might not only be based on a rational cost analysis. On the contrary, the actual decisions and the actual demand might differ
from the one predicted by the simulation model. Information, habits and
peer-imitation might play a role.
4. Results
Table 3 reports the main results from the 20 in-depth interviews. For each
selected indicator, the sample averages and the averages for the workers and
for the students are reported.
1
Another difficult situation to take into account is when in a family there is only one
driving license so that the driver is actually satisfying not only his own but all the family
mobility needs (children or even the wife).
340
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
Table 3. Results of the main results.
39
83%
Workers’
averarge
48
91%
Students’
average
27
74%
56%
105
64%
79%
106
89%
31%
81
40%
182
117
220
44%
58%
70%
74%
20%
39%
12%
10%
9%
16%
11%
3%
35%
28%
46%
32%
22%
22%
229
3506
368
3436
294
3978
192
202
161
2144
-4636
-4891
1848
7290
1.5
-5371
9404
1.2
-4536
2339
1.9
1.6
2.0
1.2
2.8
2.8
2.9
Indicators
Average
Age
N° of cars/n° of driving licenses in a family
Scenario A_SQ : without CS
N° of car trips/n° total trips for work\study
Weekly km for work\study
N° of car trips/n° total trips other than for
work\study purposes
Weekly km for other than for work\study purposes
Scenario B : A_SQ plus CS
N° of car trips/n° total trips for work\study
N° of car trips/n° total trips for other-thanwork\study purposes
N° of CS trips/n° total trips for work\study
N° of CS trips/n° total trips for other-thanwork\study purposes
Scenario C : CS and no private car
N° of CS trips/n° total trips for work\study
N° of CS trips/n° total trips for other-thanwork\study purposes
N° of CS trips/n° total trips for work\study
Monetary value of the pleasure of owning a
car
Monetary value of the pleasure of being a CS
user
Total costs - Scenario A_SQ : Status Quo without CS
Total costs - Scenario B : Status Quo plus CS
Total costs - Scenario C : CS, no private car
Preference (1=best, 3=worst) - Scenario A_
SQ : Status Quo without CS
Preference (1=best, 3=worst) - Scenario B :
Status Quo plus CS
Preference (1=best, 3=worst) - Scenario C : CS,
no private car
Understanding the demand for carsharing
341
Given the low number of respondents the results presented in this section
have no statistical representativeness. However, they are useful to identify
the factors which might play a role in explaining the CS demand. It appears
that :
- car ownership is currently very high ;
- the car is used extensively to go to work\study, more so for work trips ;
- the average distance travelled for work\study purposes is not very long :
something more than 20 km per day (round trip) ;
- the car is used even more extensively for other-than-work\study trips and
for longer distances ;
- if the CS were available, the number of car trips would be lowered and
substituted by CS about 10% of the times.
- in the hypothetical scenario that only CS, and not the private car, were
available 62.5% of the work\study trips currently made with the private car
and 43.8% of the other-than-work\study trips would be substituted by CS.
The proportions are relatively larger for students than for workers.
- a quite large value is assigned to the pleasure of owning a private car,
much more so than the pleasure of being a carsharing users, both for workers and students.
- with respect to the total cost of the three scenarios, on average, the respondents are slightly better off in Scenario B “Status Quo plus CS”, except
for the students who are slightly better off in Scenario 1 “Status Quo without
CS” ;
- when asked to rank the three scenarios, having looked at the estimated
total costs, the respondents judge scenario 1 as the best scenario. More so by
the workers. The students deemed Scenario 2 as preferred.
- it is worth noting that the hypothetical scenario 3 “CS, no private car” is
mostly ranked as the worse among the three.
The total mobility costs of the three scenarios and the preference rankings
are generally consistent. Scenario 3 is always more expensive because of its
lower flexibility and higher monetary and time costs. It is most of the times
ranked third (17 times of out 20, in the remaining ones is ranked second On
average, Scenario A_SQ “Status Quo without CS” costs more, but is ranked
higher than Scenario B “Status Quo with CS”, meaning that although the
availability of a CS service would lower the mobility costs of the individuals, it would not be preferred. Considering only the workers, the private car
preference is even stronger (possibly linked with psychological factors not
captured in the model). Considering only the students, the opposite applies :
the availability of a CS service is preferred to the current situation, although
342
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
no mobility costs advantages apply, probability reflecting an interest for innovative and more flexible modes of transport.
5. Three individual case studies
Each interview provided us with a set of individual values which represents
the parameters of the model. The model is then used to simulate how the
results depend on some parameters of interest for our analysis such as parking searching time, the time needed to get to a sharing car, the CS fare, and
the number of trips made. In the next subsections, three different individual
case studies are presented and discussed.
5. 1. Case study 1 – A person with low mobility needs
Although the respondent was asked to describe his mobility patterns under
the three scenarios described above, only two scenarios will be analyzed in
this paper : the Status Quo Scenario (Scenario A_SQ), with the person owning
a private car and having no access to a CS service, and an hypothetical alternative Scenario (Scenario C), with the person do not owning a private car and
having access to a CS service. The respondent’s answers were the following.
Scenario A_SQ : Current mobility pattern - the private car is available while
CS is not.
• Commuting trips during a typical week : 5 (round) trips by bus of 1 km
each ; for a total of 5 km per week.
• Non-commuting trips during a typical week : 1 trips of 2 km by car with
1 accompanying person ; 4 bus trips of 2 km ; 1 train trip of 120 km ; 1 by taxi
of 2 km with 1 accompanying person ; 3 trips by foot of 1 km ; for a total of
135 km per week.
Scenario C : Hypothetical mobility pattern - the CS is available, while the
private car is not.
• Commuting trips during a typical week : as above ; no CS use.
• Non-commuting trips during a typical week : 1 trips of 2 km by CS with
1 accompanying person ; 4 bus trips of 2 km ; 1 train trip of 120 km ; 1 by taxi
of 2 km with 1 accompanying person ; 3 trips by foot of 1 km ; for a total of
135 km per week.
The other parameters of the model and data provided by the interviewee
are available from the authors on request (the same applies for the other
case studies). This is a person who lives very close to his working place and,
hence, does very short-distance commuting to work by bus. The non-work
mobility is somewhat more complicated. More than one transport mode is
used, including a car. In Scenario C, CS substitutes the car. What would be
Understanding the demand for carsharing
343
the implications in terms of his generalized total costs ? The model is used to
simulate his generalized total costs.
6000
A_SQ total generalized costs
5000
4000
C total generalized costs
A_SQ Monetary Costs
2000
A_SQ\C Travel Time Costs
Euro
3000
C Monetary Costs
1000
C nonmonetary net costs
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61
1
A_SQ nonmonetary
net costs
-1000
Parking search �me\access �me to a CS car
Figure 2. Case study 1 : Total annual generalized cost depending on the parking
searching time\time to access a CS vehicle (minutes).
In this and the following Figures, the generalized cost under the two scenarios A_SQ and C are broken down into the following components : the
monetary costs, the travel time costs, and the non-monetary net costs. 1 The
monetary costs include the direct expenses, the amortizations costs and the
opportunity costs. 2 The travel time costs are estimated assuming a given
speed for each mode and multiplying it by an assumed value of time.
1
The nonmonetary costs and benefits (net costs) include : the estimated value of the uninsured risks that the vehicle (car, bicycle, motorbike) is stolen or damaged, the estimated
nuisance cost of maintaining and refueling the vehicle, the amount of time need to search
for parking at destination, the estimated value of park searching time, the estimated value
of owning a vehicle, the value of having a vehicle at own disposal, the estimated nonmonetary costs or benefits of walking, the estimated nonmonetary costs or benefits of cycling,
the time needed to search and reach a car, the estimated value of time for searching and
reaching a car, the estimated nuisance value of booking a CS car, the estimated nuisance
value of not finding a CS car when needed, the estimated value of not having to take care
of a private car, the estimated value of being a CS user.
2
More specifically, they include : the annual depreciation of the vehicle (car, bicycle,
344
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
Figure 2 describes how the total annual generalized cost would vary when
in Scenario A_SQ the parking searching time is allowed to vary from 0 to 60
minutes, and in Scenario C the time to access a CS vehicle is allowed to vary
from 0 to 60 minutes. It results that the low mobility person would reduce his
generalized costs by more than 1,000 Euros if the CS were available even if the
time to access a CS vehicle to collect the CS car were one hour. The main advantage of Scenario C over Scenario A_SQ is that the monetary costs are much
lower, because there are no fixed costs of owning a car. The travel time costs
are identical and the non-monetary costs in Scenario C are just slightly higher
than in the Scenario A_SQ. A similar result is reached also when the CS fare is
allowed to increase by a large amount compared with the currently prevailing
0.29 Eurocents per km. Even when, the time to access a CS vehicle is equal to
30 minutes and the per km fare is equal to 2 Euro per km, the total generalized
cost would be only 3,511 Euros, implying that this person is still better off using the CS than owning a car. Most probably the ideal solution for this person
would be to use the taxi for urban trips and the CS for suburban trips.
7000
C total generalized costs
6000
A_SQ total generalized costs
5000
Euro
4000
A_SQ\C Monetary Costs
3000
A_SQ\C Travel Time Costs
C Monetary Costs
2000
C nonmonetary costs
1000
0
1
-1000
2
3
4
5
6
7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 A_SQ non monetary costs
Car trips
Figure 3. Case study 1 : Total annual generalized cost by number of car trips.
motorbike), road tax, insurance cost, maintenance cost, the opportunity cost of the owned
garage, weekly parking expenses, the bus ticket or pass costs, the train ticket or pass costs,
the taxi fare, the annual depreciation of the vehicle, CS membership fee, the CS fare.
Understanding the demand for carsharing
345
Let us now increase the number of car trips that the respondent makes in
Scenario A_SQ by private car and in Scenario C by CS. How would his generalized costs increase ? The results simulated by the model are presented in
Figure 3. Note that a 10 minutes time to access a CS vehicle is assumed for
CS, whereas a zero minutes parking searching time is assumed for the private
car. This person would find CS convenient over owning a private car if less
than 14 trips are made. The increase in the Scenario C generalized costs are
due to the increase of both the monetary and the nonmonetary (time to
reach a CS car) costs.
This case study is representative of the people who either do not work
or study, e.g. because they are retired, or commute very short distances and
have relatively short non-working trips. In the city of Trieste, Italy, our guess
is that probably more than 60% of the population has this kind of mobility
pattern. Our model estimates that these persons would largely benefit from
the existence of a CS service, they would use it occasionally and they would
probably be willing to give up the private car.
5. 2. Case study 2 – A person with medium mobility needs
Case Study 2 analyses the case of a person with a medium mobility level. The
mobility pattern under the two Scenarios, A_SQ and C, are the following :
Scenario A_SQ : Current mobility pattern - the private car is available while
the CS is not.
• Commuting trips during a typical week : 4 trips by car of 3 km each ; 6
trips by foot of 3 km ; for a total of 30 km per week.
• Non-commuting trips during a typical week : 5 trips of 20 km by car, of
which 1 trip with 1 accompanying person ; 1 bus trip of 6 km ; 2 trips by foot
of 5 km ; 1 trip by bicycle of 10 km ; for a total of 126 km per week.
Scenario C : Hypothetical mobility pattern - the CS is available, while the
private car is not.
• Commuting trips during a typical week : 6 trips by foot of 3 km ; 4 trips
of 3 km by CS.
• Non-commuting trips during a typical week : 2 bus trips of 9 km ; 3 trips
by foot of 6 km ; 1 trip by bicycle of 10 km ; 4 trips of 20 km by CS with 1 accompanying person ; for a total of 126 km per week.
This person is able to reach his working place by foot, but needs also to use
the car 4 days a week. The non-work mobility includes the car, the bus, the
bicycle and walking. If this person would hypothetically not own a private
car, such trips would be made both by bus and by CS.
346
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
8000
A_SQ total generalized costs
7000
C total generalized costs
6000
5000
Euro
4000
A_SQ\C travel �me costs
3000
A_SQ monetary costs
2000
C monetary costs
1000
A_SQ nonmonetarycosts
C non monetary costs
0
1 3 5 7 9 1113151719212325272931333537394143454749515355575961
1113151719212325272931333537394143454749515355575961
1
-1000
-2000
Parching searching �me\access �me to a CS car
Figure 4. Case study 2 : Total annual generalized cost depending on the parking
searching time\time to access a CS vehicle
If the parking searching time is allowed to vary from 0 to 60 minutes, as it
can be seen in Figure 4 the generalized costs in Scenario C are always lower
than Scenarios A_SQ, when the parking seraching time is equal to the time
to access a CS vehicle. But whenever the difference between access time to a
CS car and parking time is larger than 19 minutes, Scenario A_SQ becomes
preferable. Contrary to case study 1, this case study shows that the parking
conditions and the number and location of CS cars can determine whether
a person would find avantageous or not to give up the private car in favour
of a CS service.
The fact that the difference between the two generalized cost curves is not
so large allows us to test a further aspect : the break-even of CS fare.
Understanding the demand for carsharing
347
Table 4. Case study 2 : the break-even of CS fare.
Parking searching
time
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total cost
CS fare
Scenario A_SQ
5224
5261
5297
5334
5371
5407
5444
5481
5517
5554
5591
5627
5664
5701
5737
5774
5811
5847
5884
5921
5957
5994
6031
6067
6104
6141
6177
6214
6251
6287
6324
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.4
0.41
0.42
0.43
0.44
0.45
0.46
0.47
0.48
0.49
0.5
Total cost
Scenario C
15 min time to access a CS
vehicle
4056
4119
4181
4244
4307
4369
4432
4495
4558
4620
4683
4746
4808
4871
4934
4996
5059
5122
5185
5247
5310
5373
5435
5498
5561
5623
5686
5749
5812
5874
5937
Let us examine Table 4. Column 2 lists the total generalized of Scenario
A_SQ corresponding to the parking searching time depicted in column 1.
Column 4 lists the total generalized of Scenario C, (assuming the time to ac-
348
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
cess a CS vehicle equal to 15 minutes) correspending to the fare cost per km
depicted in column 3. It can be seen that when the parking searching time
is equal to 0 (that is, parking is avaible in a private garage or to very close
to house), the break-even fare, that is the fare according to which the total
cost of scenario A_SQ, €5224, is close to the total cost of scenario C, € 5247,
is equal to 0.38 Euro per km, which is above the current prevailing levels in
Italy. This implies that this person is potentially a CS users, were it possible to
supply him with a CS service at current Italian cost conditions.
18000
C total generalized costs
16000
14000
12000
A_SQ
_ Q total generalized
g eralize
generalize
ized
d costs
co
8000
Monetary
C Mon
Mone
eta
tarry Costs
Euro
10000
A
Monetary Costs
A_SQ
6000
A_SQ\C
A
Travel
Time
T e Costs
Tim
4000
2000
C non monetary costs
0
-2000
-4000
1
2
3
4
5
6
7
8
9 1
10
11
12
13
14
15
16
17
18
19
20
21
0 1
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 2
0 2
1
SQ
S non monetary costs
Car trips
Figure 5. Case study 2 : Total annual generalized cost depending on the number of
car trips.
Figure 5 shows that when the number of car trips (of 20 km) increases,
Scenario C becomes increasingly more expensive than Scenario A_SQ. This
is mostly due to the higher slope of the C monetary cost curve relative to the
A_SQ monetary cost curve, that is, the variable cost of CS are higher than
the variable cost of the private car (in our case 0.29 Eurocents\km for CS vs
0.14 for the private car). The travel time costs are identical, the nonmonetary
costs are slightly higher in Scenario C.
In a city such as Trieste, it is not easy to determine how representative this
person is with respect to the current population. Our guess is 10% of the
total inhabitants.
Understanding the demand for carsharing
349
5. 3. Case study 3 – A person with high mobility needs and living in a small town setting
Case Study 3 considers the case of a person with a high mobility level in a
small town. The mobility patterns under the two Scenarios, A_SQ and C, are
the following :
Scenario A_SQ : Current mobility pattern - the private car is availability
with no CS.
• Commuting trips during a typical week : 11 round trips by car of 4 km
each. Total 44 km.
• Non-commuting trips during a typical week : 5 round trips by car of 20
km each. Total 100 km.
Scenario C : Hypothetical mobility pattern - the CS is available, but not the
private car.
• Commuting trips during a typical week : 9 round trips by bicycles of 4 km
each ; 2 round trips by CS of 4 km each. Total 44 km.
• Non-commuting trips during a typical week : 2 round trips by bus of 30
km each ; 2 round trips by bicycles of 10 km each ; 1 round trip by CS of 20
km. Total 100 km.
This person, living in a small town, with low density and scattered settlements, currently relies on car both for work and non-work trips. No other
modes of transport are used. The car is an essential mode of transport for
this person. In the interview setting, being forced to imagine how he would
have traveled without a car, this person sad he would have turned to the bicycle for the short trips, and to the bus (when available) and to CS for longer
trips. The simulation model is used to evaluate the generalized total cost
(including the time cost) of the two scenarios.
350
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
6000
A_SQ total generalized
costs
5000
4000
C total generalized
costs
C travel �me costs
A_SQ monetary costs
Euro
3000
2000
A_SQ nonmonetary
costs
A_SQ travel �me costs
C monetary costs
C non monetary costs
1000
0
11 1315171921232
25 27 29 31333537394143454749515355575961
1
1 3 5 7 9 1113151719212325272931333537394143454749515355575961
-1000
-2000
-3000
Park searching �me\access �me to a CS car
Figure 6. Case study 3 : Total annual generalized cost depending on the parking
searching time\time to access a CS vehicle.
When the parking searching time is zero or small the respondent is better off
using a private car. Although the A_SQ monetary costs are much higher than
the C monetary costs, this difference is more than compensated by the travel
time gains (the C travel time costs are much larger than the A_SQ travel time
costs) and by the lower nonmonetary net costs in Scenario A_SQ vs. Scenario
C. Among the latter, the respondent assigned a large at having a car at own
disposal. In other words, in a small town environment with scattered settlements, having a car – although being a financial burden – is convenient, since
it permits flexible and fast mobility. In a scenario without a car, the respondent would incur in lower monetary costs, but higher time costs and lower
flexibility. The two scenarios converge when parking the car or reaching a CS
vehicle requires, unrealistically, more than 37 minute walk.
As in the previous case, we can increase the CS fare to see what would
be the break-even fare for this respondents. The answer depends on the assumed parking searching time. When there is no o very little parking searching time, as it is the case in most small town or low density areas, Scenario
C is not competitive even when the CS is offered at no or very small fares
(the value of owning a private cars prevails). Only when the private car is 10
minutes further away of the CS car (25 minutes for the private car vs. the assumed 15 minutes for the CS car), the CS service becomes advantageous.
Understanding the demand for carsharing
351
Table 5. Case study 2 : the break-even of CS fare.
Parking searching
time
Total cost
Scenario A_SQ
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1371
1434
1497
1560
1623
1685
1748
1811
1874
1937
2000
2063
2126
2189
2252
2315
2378
2440
2503
2566
2629
2692
2755
2818
2881
2944
3007
3070
3133
3195
3258
Total costs
Scenario C
CS fare
15min time to access a CS
vehicle
0
2906
0.01
2919
0.02
2933
0.03
2946
0.04
2959
0.05
2972
0.06
2985
0.07
2999
0.08
3012
0.09
3025
0.1
3038
0.11
3051
0.12
3065
0.13
3078
0.14
3091
0.15
3104
0.16
3117
0.17
3131
0.18
3144
0.19
3157
0.2
3170
0.21
3183
0.22
3197
0.23
3210
0.24
3223
0.25
3236
0.26
3249
0.27
3263
0.28
3276
0.29
3289
0.3
3302
Simulating an increasing the number of trips is not meaningful in this case,
since the result is obvious : it would reinforce the advantages of owning a
private car.
Case study 3 sheds some light on the factors that play a role in a small
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Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
town setting with scattered settlements. Owing a car is extremely relevant in
terms of flexibility and time savings, although costly in monetary terms. The
potential for a viable CS service in such circumstances are rather bleak.
6. Conclusions
This paper presented a model which calculates the total generalized cost for
a given mobility pattern and transport mode mix. The model considers that
a person sometimes travels with friends and family, and therefore shares the
travel expenses or satisfies several travel needs, and that uses in a given time
period more than one mode of transport. The presence of a CS service increases a person’s mode of transport availability, allowing him\her to eventually redistribute his\her travel mode choice differently. Each mode entails
monetary, time and other nonmonetary costs. In order to consider them all,
a complex and detailed model should be specified and estimated with enormous data requirements. In our case, the parameters of the model are derived by detailed, one-hour long, face-to-face, computer-assisted interviews.
A limited number of interviews have been so far completed. However,
they hint to some very interesting empirical evidence. It is found that car
ownership is currently very high and that the car is used extensively both for
work\study and, even more, for other-than-work\study trip purposes. Offering a CS would enhance the mode choice and could, in some cases, lower
the total mobility costs. The respondents assign quite a large value to the
pleasure of owning a private car, much more so than the pleasure of being
a carsharing users. Consequently, the respondents would dislike not owing
a private car, while having the choice between the private and the carsharing car is preferred especially by the students. The mobility cost indicators
reflect, but not perfectly, the preference-based choices of the sample.
These interviews allowed us to examine many different case studies, three
of which, the most representative ones, are described and analyzed in this
paper. They have been defined as : a low, a medium and a high mobility case
study. The low mobility case study is representative of the people who either
do not work or study, e.g. because they are retired, or commute very short
distances. Our model estimates that these persons would largely benefit
from the existence of a CS service, they would use it occasionally and they
would probably be willing to forgo the private car. The medium mobility
case study shows that the variables parking time, access time and CS fare can
easily switch the balance between convenience and inconvenience of using
CS. As the number of trips increase, owning and using a private car becomes
the preferred option. The higher mobility case study in a small town setting
Understanding the demand for carsharing
353
demonstrates that owing a car is extremely convenient in terms of flexibility
and time saving, although costly in monetary terms, and that in such circumstances the prospects for a viable CS service are rather bleak. A larger set of
interviews and the knowledge of how representative these case studies are of
a city or a region could lead to a reliable estimate of the potential demand for
the CS service. This will be the aim of our next research effort.
Although the model is complex and requires a large set of data, it is still a
simplified and partial representation of the real world decision choice process. As mentioned earlier, estimating the potential demand for CS is fraught
with difficulties, mainly because it involves both individual and family decisions, it depends on economic and ideological, rational and irrational motivations, it requires learning and adaptation (being a relatively newly available
mode of transport) and it is strongly affected by contextual factors (owning a
private garage, the availability and performance of public transport, specific
origin-destination conditions). A model could not account for all these complexities. A simplified process is needed. The model presented in the paper
represents an initial effort. Further experimentation and data collection is
certainly needed.
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Appendix
Excel interface for data collection for the detailed, face-to-face, computer-assisted
interview. The rows in italics are generated by the software based on the previous
information on the mobility patterns and on assumptions and information about
the regional fares :
If you own a car, please answer the following questions
€
How much does your car cost ?
After how many years do you think that the market value of your n°
car be zero ?
How much do you pay as a road tax ?
€
How much do you pay as insurance ?
€
€
How much do you the risk of uninsured theft or damage to your
car ?
€
How much do you pay for the annual ordinary and extraordinary
maintenance ?
€
How much would you pay annually for avoiding the nuisance of having to care about maintain and refuelling your car ?
What is the opportunity cost of your private garage ?
€
How much do you pay weekly for parking ?
€
How much time do you spend weekly to search for a parking place ? min.
€
How much would you pay for avoiding the nuisance of searching for
10 minutes for a parking place ?
How much do you rate in monetary terms the pleasure of owning
€
a car ?
€
What is the sum that you would be willing to accept to give up your
private car ?
Annual monetary expenses :
€
€
Annual depreciation costs :
Annual opportunity costs :
€
Net nonmonetary costs (excluding the cost for the time spent in the vehicle) :
€
€
Total monetary and nonmonetary costs (excluding the cost for the time spent
in the vehicle) :
If you own a motorcycle, please answer the following questions
How much does your motorcycle cost ?
After how many years do you think that the market value of your
motorcycle be zero ?
€
n°
357
Understanding the demand for carsharing
How much do you pay as a road tax ?
How much do you pay as insurance ?
How much do you the risk of uninsured theft or damage to your
motorcycle ?
How much do you pay for the annual ordinary and extraordinary
maintenance ?
How much would you pay annually for avoiding the nuisance of having to care about maintain and refuelling your motorcycle ?
How much do you rate in monetary terms the pleasure of owning a
motorcycle ?
What is the sum that you would be willing to accept to give up your
private motorcycle ?
Annual monetary expenses :
Annual depreciation costs :
Annual opportunity costs :
Net nonmonetary costs (excluding the cost for the time spent in the vehicle) :
Total monetary and nonmonetary costs (excluding the cost for the time spent
in the vehicle) :
If you own a bicycle, please answer the following questions
How much does your bicycle cost ?
After how many years do you think that the market value of your
bicycle be zero ?
How much do you rate in monetary terms the pleasure of cycling ?
How much do you rate in monetary terms the nuisance of cycling ?
Annual monetary expenses :
Annual depreciation costs :
Annual opportunity costs :
Net nonmonetary costs (excluding the cost for the time spent in the vehicle) :
Total monetary and nonmonetary costs (excluding the cost for the time spent
in the vehicle) :
If you use the bus, please answer the following questions
Do you buy single tickets ?
Do you buy the 10 tickets card ?
Do you buy the monthly pass ?
Do you buy the annual pass ?
Total annual costs (excluding the cost for the time spent in the vehicle) :
€
€
€
€
€
€
€
€
€
€
€
€
€
n°
€
€
€
€
€
€
€
€
358
Romeo Danielis · Lucia Rotaris · Andrea Rusich · Eva Valeri
If you use the train, please answer the following questions
Do you buy single tickets?
Do you buy the bi-weekly pass ?
Do you buy the monthly pass ?
Do you buy the annual pass ?
Total annual costs (excluding the cost for the time spent in the vehicle) :
€
If you use the taxi, please answer the following questions
Total annual costs (excluding the cost for the time spent in the vehicle) :
€
If you walk to reach your work\non work destinations, please answer the
following questions
€
How much do you rate in monetary terms the pleasure of walking ?
How much do you rate in monetary terms the nuisance of walking ?
€
Net nonmonetary costs (excluding the cost for the time spent in the vehicle) :
€
If you use the Carsharing, please answer the following questions
What is the annual membership fee
€
Carsharing fee per minute
€
(Round) Trips per year
n°
n°
How many minutes do\would it take to you to reach a CS car ?
€
How much would you pay for avoiding the nuisance of searching for
10 minutes for a CS car ?
€
How much would you pay annually for avoiding the nuisance of having to book a CS car ?
How much would you pay annually for avoiding the risk of founding
€
no CS car available when you need it ?
How much do you rate in monetary terms the satisfaction of being
€
a CS user ?
Annual monetary expenses :
€
€
Net nonmonetary costs (excluding the cost for the time spent in the vehicle) :
€
Total monetary and nonmonetary costs (excluding the cost for the time spent
in the vehicle) :
Overall total
Total annual monetary expenses :
€
€
Total annual depreciation costs :
359
Understanding the demand for carsharing
Total annual opportunity costs :
Total net nonmonetary costs (excluding the cost for the time spent in the
vehicle) :
Overall total monetary and nonmonetary costs (excluding the cost for the
time spent in the vehicle) :
€
€
€
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Direttore responsabile: Fabrizio Serra · Autorizzazione del Tribunale Civile di Pisa n. 12/1997