user incentives catalogue€¦ · annex 1 survey questionnaire (german) annex 2 survey evaluation...
TRANSCRIPT
User incentives catalogue
WP 7 – Report
Ruzica Cuic
Roland Hackl
Marlene Hawelka
Julia Schmid
Benjamin Biesinger
Bin Hu
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INDEX
1 Introduction ................................................................................................... 3
2 State of the Art .............................................................................................. 4
2.1 Market analysis: car-sharing operators ............................................................ 4
2.2 User incentives: general outline and rationale ................................................. 5
2.3 Existing incentives ........................................................................................... 6
2.3.1 Free-floating ................................................................................................................... 8
2.3.2 Station-based ............................................................................................................... 10
2.4 Classification schemes and their impact on system performance .................. 11
3 Potential Future Incentives ......................................................................... 13
3.1 Methodology and approach ........................................................................... 13
3.2 Potential new incentives ................................................................................ 15
3.3 User rating of existing and possible future incentives .................................... 15
3.4 Classification of (new) incentives .................................................................. 17
4 Implementation rules (Incentives Models) ............................................ 18
4.1 Task objectives and approach ....................................................................... 18
4.2 Deriving acceptance probabilities: ‚stated preference‘ approach ................... 18
4.3 Theory-based deduction of acceptance probability ........................................ 22
4.4 Implementation of incentives for the free-floating car-sharing system ............ 25
4.4.1 Offered incentives ........................................................................................................ 25
4.4.2 Acceptance of incentives ............................................................................................. 26
4.4.3 Results overview .......................................................................................................... 26
5 Bibliography ................................................................................................ 28
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ANNEX
Annex 1 Survey Questionnaire (German)
Annex 2 Survey Evaluation Document
TABLES
Table 1: Overview on existing car sharing companies, area of operation & classification (Sources:
Websites of operators, accessed in July, 2017) ................................................................................. 4 Table 2: Main issues related to design, management and operation of vehicle sharing systems (cf.
Vogel, et al., 2011) ............................................................................................................................. 5 Table 3: Overview on existing incentives at different operators (Sources: Websites of operators,
accessed in July, 2017) ...................................................................................................................... 7 Table 4: Mapping and classification of existing incentives regarding their ecological, economic,
efficiency- and electricity-impact and the different planning horizons (TBWR) ................................. 12 Table 5: Possible incentives and rating at the project-team workshop (TBWR, AIT, UV-DSOR) ................... 16 Table 6: Mapping and classification of potential future incentives regarding their ecological, economic,
efficiency- and electricity-impact and the different planning horizons (TBWR) ................................. 17 Table 7: Descriptives - shadow wages (TBWR) ............................................................................................. 19
FIGURES
Figure 1: Screenshot - car2go catch it! (car2go Österreich GmbH, 2015) ...................................................... 8 Figure 2: Screenshot DriveNow app – Drive’n Save ........................................................................................ 9 Figure 3: Invitation top the second user-workshop on possible future incentives (TBWR) .............................. 13 Figure 4: Location of participants regarding their car sharing usage / behavior (TBWR) ................................ 14 Figure 5: Possible incentives and rating in % for filling up a car-sharing car (survey results, TBWR) ............. 16 Figure 6: Predictor importance for cluster formation (TBWR).......................................................................... 20 Figure 7: Cluster sizes (TBWR) ....................................................................................................................... 20 Figure 8: Cluster 1 profile (TBWR) .................................................................................................................. 21 Figure 9: Cluster 2 profile (TBWR) .................................................................................................................. 22 Figure 10: Incentive amount vs probability of acceptance: imaginary example graph (TBWR) ......................... 23 Figure 11: Accessibility analysis of fuel stations in Vienna (TBWR) .................................................................. 24 Figure 12: Cumulative frequency of residential areas by fuel station accessibility (TBWR) .............................. 25 Figure 13: Four types of actions with monetary incentives (AIT) ....................................................................... 26 Figure 14: Chart for user behavior when picking up a car (AIT) ........................................................................ 27 Figure 15: Chart for user beviour when returning a car (AIT) ............................................................................ 27
This report was produced within the framework of the e4-share project (Models for Ecological, Economical, Efficient, Electric Car-Sharing) funded by FFG, INNOVIRIS and MIUR via Joint Programme Initiative Urban Europe. The conclusions and recommendations contained within this report are those of the project partner TBWR based on the desk- and self-conducted qualitative and quantitative research. For more information please contact TBWR at [email protected].
Last update: October 2017
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1 Introduction
Car sharing systems are becoming more and more popular and the number of operating
companies increased all over the world in the last years. On the other hand side, some
operators have already withdrawn from the market. Car sharing is part of the so called
“sharing economy” (The Economist, 2013) which means the sharing of goods and services
respectively renting instead of owning a car. Users have the choice between small and large
cars, fuel-driven or electric cars as well as free-floating and station-based cars.
DriveNow- and car2go1 charge every driven and parked minute, including costs for parking,
insurance, fuel, etc. – referring to an All-Inclusive-Package. (car2go Österreich GmbH,
2017), (DriveNow GmbH & Co. KG, 2017) As every car, refueling, maintenance, etc. have to
be done within the shared fleet as well – it can be assumed that big car sharing companies
hire staff to take over those necessary tasks as well as for redistribution within the area of
operation in cases of imbalance. The next logical thought – based on the existing systems –
is that car sharing operators try to reduce their management- and maintenance effort and
offer their users different incentives to do some of the operational work – and this balance
between customer-based- and operational tasks seems to be hard to find.
This report gives an overview on existing incentives to customers to reduce the operators’
maintenance- or management effort. It is tried to find possible future incentives and to define
their influence. The analysis includes station-based- and free-floating-systems in the different
forms of ad-hoc- and pre-booked-sharing and their influence on management efforts in
practice.
1 DriveNow and car2go are the most-popular free-floating operators in Austria.
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2 State of the Art
First step within the project as part of the stakeholder dialogue was the analysis of existing
car-sharing-companies and -systems. The following point gives an overview on existing
operators and systems, an explanation and rationale on the outline and on existing
incentives as well as an overview of the most popular ones. At the end of this state-of-the-art
analysis those incentives are tried to be put into classification schemes and evaluated
regarding their impact on system performance.
2.1 Market analysis: car-sharing operators
Table 1: Overview on existing car sharing companies, area of operation & classification (Sources: Websites of operators,
accessed in July, 2017)
X = applicable N = nationwide operation 1-100 = number of cities with operation St
atio
n-b
ase
d
Fre
e-f
loat
ing
Ad
-ho
c
Pre
-bo
oke
d
Au
stri
a
Ge
rman
y
Swit
zerl
and
oth
er
EU-
Stat
es
UK
USA
Can
ada
Asi
a
Au
stra
lia
oth
er
Co
un
trie
s
car2go X X Vienna 6 62 7 4
Chong-qing
DriveNow X X Vienna 5 53
Lon-don
Multicity Carsharing X X Berlin
Flinkster X X X 24 N
Zipcar X X X 95
Frank-furt
16 7 3336 11 Taipei
Istan-bul
Cambio X X X 21
Enjoy (ENI) X X 5 (IT)
ICS - Iniziativa Car Sharing
X X X 11 (IT)
Caruso7 X X 148
Ibiola/24/79 X X N
Greenwheels10 X X 25
Mobility Schweiz X X N
Mobiel rijden (6 CS / rental services)11
X X 9 / NL
Autolib Paris X X X Paris
blueindy X X X Indianapolis
2 Florence, Milan, Rome, Turin, Amsterdam, Madrid (car2go Österreich GmbH, 2015)
3 Copenhagen, Stockholm, Brussels, Milan and Helsinki (DriveNow GmbH & Co. KG, 2017)
4 Has withdrawn from the Austrian market on 1
st of April 2016. (futurezone.at, 2015)
5 Has withdrawn from the Austrian market on 6
th of August 2017. (Zipcar Austria GmbH, 2017)
6 With all campuses using ZipCar.
7 This is a self-organized service.
8 All cities are located in Vorarlberg / January 2017 (CARUSO Carsharing eGen, 2017)
9 This is a self-organized service.
10 Since January 2016 including the former Quicar-vehicles (VW) (brt/Reuters/dpa, 2016)
11 ConnectCar, StudentCar, WITKAR, VALK Autverhuur, DemoRijden, KAV Autoverhuur (Mobiel Rijden, 2017)
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X = applicable N = nationwide operation 1-100 = number of cities with operation St
atio
n-b
ase
d
Fre
e-f
loat
ing
Ad
-ho
c
Pre
-bo
oke
d
Au
stri
a
Ge
rman
y
Swit
zerl
and
oth
er
EU-
Stat
es
UK
USA
Can
ada
Asi
a
Au
stra
lia
oth
er
Co
un
trie
s
citycarclub X X X Helsinki
Zencar X X Brussels
Wheels4all X X Utrecht
Elfride Carsharing X X Vienna
2.2 User incentives: general outline and rationale
While policy incentives intend to increase the acceptance of the population, encourage the
change to use electric cars and facilitate specific parking spaces for car sharing vehicles,
user- or customer-incentives intend to reduce the management- and maintenance effort of
the fleet operators and car sharing companies. It is assumed that redistribution trip costs are
very high for the fleet companies, which causes their aim to minimize the relocation costs.
They also try to keep the users’ satisfaction at a high level, which amongst others ”increases
with the probability to find […] an available parking place in any station at any time” (Crisalli &
Mussone, 2011, p. 298). Providing users high flexibility and low waiting times is one of the
relocation targets. This strategy can be recognized as a “cost‐effective means of tackling the
problem of unbalanced car distribution among stations in car sharing systems” (Obaidat &
Nicopolitidis, 2016). The main costs for car sharing companies represent “key resources and
activities: acquisition of the vehicle fleet, maintaining, fueling and cleaning the vehicles,
personnel costs and customer services, system operation and maintenance, insurance
contracts, other expenses related to eventual improper use of the service and fees related to
the usage of parking lots and access to limited traffic areas” (Ferrero, et al., 2015, p. 10).
Most of the car sharing companies involve operator-based strategies, which includes also the
partially user-based where users “may be available in performing few of the required
relocations if motivated by a reduction in the transport price” (Cepolina, et al., 2014, p. 112).
To overcome common issues like imbalances in the spatial distribution (Vogel, et al., 2011)
identified three separate planning horizons: strategic, tactical and operational. Those three
horizons are processed by the following design and management measures.
Table 2: Main issues related to design, management and operation of vehicle sharing systems (cf. Vogel, et al., 2011)
Strategic horizon (long-
term): Design
Tactical horizon (mid-term):
Management
Operational horizon (short-
term): Operation
Network design with decisions
on fleet size, locations, station
allocation, etc.
Incentives for customers:
overtaking spatial distribution
or refueling
Relocation and maintenance
done by the operator’s staff
(operator-based)
Decisions on the first – the strategic level – have to be planned on a long-term horizon and
therefore show great impact on the mid- and short-term-levels as its flexibility is very low
(e.g. changing station allocation). Otherwise, management and operational measures have
to compensate unsuitable designs, but at the same time have to be considered on the
strategic horizon.
The sharing operators as car owners are responsible for the long-term design of the system
but as well for the operational horizon on maintenance tasks such as repairing or tire
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changes. Users can be made responsible for refueling or cleaning the car – mostly in return
they receive e.g. extra minutes for driving (which is a tactical management measure) in
comparison to car rental, where those tasks are mandatory in addition to the rental fee.
Another important part of a well-functioning fleet is the optimized distribution of cars within
the area of operation (spatial distribution) – also therefore some operators implemented (at
least temporarily) an incentive system (see 2.3 Existing incentives). “The company [e.g.
car2go] aims to minimize its costs with fleet management: instead of actively repositioning
the fleet, the operation is projected so that the fleet can be managed more passively, with the
customers being responsible for the majority of changes in vehicle positions.” (Ferrero, et al.,
2015, p. 9). Incentives seem to be a “[…] promising way to deal with the major problem of car
sharing systems, that is, the fleet redistribution issue or asymmetric demand/offer […] and
tackle the demand/offer asymmetry problem.” (Gavalas, et al., 2015, p. 30)
Thus, car sharing operators can profit from saving maintenance costs and possibly ensure
that costumers remain satisfied and can choose on how much they want to participate in
management tasks. Furthermore they can profit from free trials or free registration for new
costumers (recommended by existing ones): “[…] Incentives are intended to influence the
travel behavior of the users according to the system conditions, monitored in real time.”
(Gavalas, et al., 2015, p. 17)
2.3 Existing incentives
User incentives currently exist in various forms (e.g. free minutes for charging, refueling or
re-locating the cars). (car2go Österreich GmbH, 2017) (Zipcar Austria GmbH, 2017) The
creation of incentives based on a reward system and offering benefits to users seems to be
an effective way for car sharing companies (e.g. car2go and DriveNow) to
(1) integrate their costumers into a coordinated transportation demand management and
therefore reduce efforts and to
(2) maintain existing customer relationships or – even better – to attract new ones.
So far existing and known incentives are mostly offered as “in kind” or “in price” solutions
which can be on the one hand e.g. discount vouchers or special offers like the allowance to
specific high-class vehicles. On the other hand, financial incentives like e.g. price reduction,
credits or free minutes can possibly motivate customers to overtake fleet management tasks
e.g. the relocation in free floating systems. (Gavalas, et al., 2015)
The research on the existing incentives at currently running car-sharing systems showed,
that the free-floating car-sharing operators offer incentives to their customers for overtaking
operational tasks while the station-based ones are mostly working with penalties if necessary
tasks have not been done before handing back the vehicle (e.g. if it is not fully refueled). On
the other hand side station-based services offer incentives for bringing new customers which
also improves the operational system to certain degree, as more customers can produce
more traffic and therefore decrease idle times.
In this table a rather broad definition of incentives is applied, in that e.g. fees and penalties
are considered negative incentives or billing schemes are considered ‘implicit’ incentives as
they might be influencing user costs and thus user behavior and system performance. Also,
restrictions are interpreted as inverse incentives ‘given’ to users acting within the limits of
those restrictions.
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Table 3: Overview on existing incentives at different operators (Sources: Websites of operators, accessed in July, 2017)
Free-floating station-based12
self-organized
car2
go
Dri
veN
ow
Mu
ltic
ity
Flin
kste
r
Zip
Car
Cam
bio
Enjo
y (E
NI)
Gre
en
-
wh
ee
ls
Mo
bili
ty
Sch
we
iz
Car
uso
Ibio
la/2
4/7
1. FREE minutes or BONUS
free minutes for charging / refueling x x x - - - x13 -
free minutes for cleaning
free minutes/credit for new customers / inviting family & friends
x x
free registration & trial for attracting new customers
x x x x x x x x
free minutes or bonus for relocation of the car / a more distant pick-up or drop-off location
x x - - - - -
free min./km for taking care of a vehicle
2. SPECIAL offers/DISCOUNTS
daily / hourly packages x x x
vouchers x x x x x x x x - x
special offer/rates for attractive routes (e.g. to airport, train station, shopping center)
x x x - - - x - -
3.HIGH & LOW rates
billing per minute influences on the driven distance
x x x - - - x -
billing per hour influences on the driven distance
- - - x x x x x x x
fees for special cleaning, delay, re-parking if illegally parked, loss of card, charging cable, processing of penalties and damages, overdraft etc.
x x x x x x x x x x
higher rates for rides out of business area
x x x - - - X x x -
low rates for cars in lower frequented areas
(x) x -
low rates for a special day time (barely used cars)
x - x x
restricted driving distance x x x x
restricted parking area x x x x x x x x x
The following sub-chapters give a more detailed overview on the existing incentives of three
free-floating and two station-based car-sharing operators.
12
“Mobiel rijden” and “ICS - Iniziativa Car Sharing” are active in the Netherlands and in Italy. They combine several car-sharing companies in one organization. Due to the different offers by each car-sharing provider, it will not be attended further here.
13 In form of a 5 EUR voucher. It will be instantly credited to use for the current rental.
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2.3.1 Free-floating
car2go (car2go Österreich GmbH, 2017)
Regarding the Austrian car2go-system, the operator offers the following incentives to its
customers:
charging / refueling: 10 free-minutes
o If state of fuel < 25% (or rather SOC14 < 30%)
o At defined petrol stations
o Valid for the trip(s) in the home-city in the following month.
For a shorter period another regulation was in operation: “money on account” instead of free
minutes
Inviting friends as new car2go customers: 60 free-minutes per new customer and 30 free-
minutes and less validation costs for each of them
o Valid for the trip(s) in the home-city in the following month.
Pilot operation car2go catch it! from June to October 2015: free minutes for next trip(s)
o Finishing trip within special „catch it!“-area (free-minute-zones)
In those cases, the concept of “in price”-solutions is chosen to incentive users to overtake the
management tasks of refueling (or charging) and relocation, as the purpose of the pilot
operations can be assumed as follows:
The system of „money on account“ ensures that the value of the “service” stays the same
even if prices for use change.
car2go catch it! areas car2go-cars are available in highly frequented areas, therefore ensure
less parking times (idle time) or are easier available (more concentrated) for the operator for
the cases of maintenance.
Additionally there are high rates/penalties for special use cases and different “misuses” as
e.g. 9.9 euros for trips to the airport or 50 euros for re-parking cars if illegally parked.
car2go additionally offers some “in kind” ore mixed solutions as there are cheaper or even no
registration fees and free minutes for “Friends of Merkur”.
Figure 1: Screenshot - car2go catch it! (car2go Österreich GmbH, 2015)
14
State of charge of electric vehicles.
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DriveNow Vienna (DriveNow GmbH & Co. KG, 2017)
Regarding the Austrian DriveNow-system, the operator offers the following incentives to its
customers:
charging / refueling: 20 free-minutes
o If state of fuel < 25% before and > 90% after refueling (or rather SOC15 < 25%)
o At defined petrol stations or charging stations
o Valid for the trip(s) in the homeland in the following 3 months.
Inviting friends as new DriveNow customers: 30 free-minutes for the first new customer, 45
for the second and 60 free-minutes for the third and every more and 15 free-minutes and
less validation costs for each of them
o Valid for the trip(s) in the home-city in the following 3 months.
Drive’n Save: discount rates for the next trip (e.g. 25 ct/min instead of 35 ct/min)
o assumption: specific cars with discount rate in less frequented areas and those with
already long standing (reducing idle times)
In those cases, it’s as well the concept of “in price” solutions chosen to incentive users to
overtake the management tasks of refueling (or charging) and relocation.
Additionally there are high rates/penalties for special use cases and different “misuses” as
e.g. > 400 euros for return trips from other DN-cities or 50 euros for re-parking cars if illegally
parked.
Figure 2: Screenshot DriveNow app – Drive’n Save
15
State of charge of electric vehicles.
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CITROËN MULTICITY CARSHARING
In Berlin (Germany), the CITROËN MULTICITY CARSHARING-system offers similar
incentives to its customers:
charging / refueling: 15 free-minutes
o If at least 25 liters are fueled or rather SOC16 is below 50%
o At defined petrol stations or charging stations
o One time per user and day
Inviting friends as new Multicity customers: 60 free-minutes for the existing customer and 30
free-minutes extra (additional to the existing 30 free-minutes at the registration) for the new
ones
As car2go and DriveNow do, Multicity offers “in price” incentives and additionally there are
high rates/penalties for special use cases and different “misuses” as e.g. 750 euros when the
losing the charging cable.
Multicity additionally offers some “in kind” solutions as their customers can also use the
complete “Flinkster” and “e-Flinkster”-fleet as well as “Call a bike”. There are as well extra
goodies for BVG (Berlin transport companies)-customers (60 free-minutes).
2.3.2 Station-based
The station-based use case differs from the free-floating one as there are fixed stations and
– usually – users have to give back the cars partly or fully fueled. Therefore, the
management tasks are limited to changing wheels, maintenance if necessary, sticking on the
motorway vignette or similar.
ZipCar
The main concept of ZipCar is working as “fair use” principle: there are some rules which
have to be fulfilled (reporting damages, keeping the cars clean, handing back the car with at
least 25% fueled, be in time, etc.). If one customer doesn’t report any misuse – he or she can
be held responsible by the next customer.
Additionally, ZipCar refunds the costs for washing or refilling oil after handing in the receipt.
Fuel is included in the price and can be paid via fuel card at specific petrol stations. If the
rules are not followed – also at ZipCar, there are penalties foreseen.
Flinkster
The big Car-Sharing operator Flinkster in Germany is working in close collaboration with
Deutsche Bahn and also station based. Like ZipCar, Flinkster pursues the fair-use-principle
with having penalties foreseen in cases of misuse or non-compliance.
16
State of charge of electric vehicles.
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2.4 Classification schemes and their impact on system performance
The analysis of existing incentives showed a set of solutions for incentives for all of the
different operators. The next step was to classify the incentives by the 4 “e” of e4-share:
Mapping of existing user incentives onto ecological, economical, efficient and electric
performance objectives
For both existing and potential new incentives (see next chapter) it was tried to make an
attempt to create links between individual incentive categories (and corresponding user
actions) and optimization objectives and/or system performance: „How can incentives
promote and support the overall objectives of urban car sharing systems?“
This was done on a conceptual level based on theoretical considerations. It aims at creating
a link between WP7 and the optimization models.
The following matrix shows how existing user incentives (based on the list of the existing
incentives above) relate to various key parameters and performance objectives of the car-
sharing fleet performance. These parameters refer to the optimization objectives of e4-share.
Assigning incentives to performance objectives is done in a conceptual fashion based on
theoretical considerations:
‘x’ marks denote the assumption that a respective impact exists
‘(x)’ marks that the assumption about a respective impact is very unclear as there is no
detailed information if those incentives produce more traffic than the task would do in the task
area of the operator (e.g. it cannot be proven if additional trips for collecting free-minutes for
refueling or charging cause more negative environmental effects that the staff’s trips for the
same would do due to different organizational structures).
Doing this assignment, a link between the incentive work package (WP7) and the
optimization models is being established.
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Table 4: Mapping and classification of existing incentives regarding their ecological, economic, efficiency- and electricity-
impact and the different planning horizons (TBWR)
Impact Level
‘x’ = assumption that a respective impact exists ‘(x)’ = assumption about a respective impact is very unclear as there is no detailed information if those incentives produce more traffic than the task would do in the task area of the operator Ec
olo
gica
l …
Eco
no
mic
…
Effi
cie
ncy
…
Ele
ctri
city
…
Stra
tegi
c …
Tact
ical
…
Op
era
tio
nal
…
1. FREE minutes or BONUS
free minutes for charging / refueling (x) x x x x
free minutes for cleaning (x) x
free minutes/credit for new customers / inviting family & friends (x) x x x
free registration & trial for attracting new customers (x) x x
free minutes or bonus for relocation of the car / a more distant pick-up or drop-off location
(x) x x x x
free min./km for taking care of a vehicle (x) x x x
2. SPECIAL offers/DISCOUNTS
daily / hourly packages x x x x
vouchers x x x
special offer/rates for attractive routes (airport, train station, etc.) x x x
3.HIGH & LOW rates
billing per minute influences on the driven distance x x x x x
billing per hour influences on the driven distance x x x
fees for special cleaning, delay, re-parking if illegally parked, loss of card, charging cable, processing of penalties and damages, overdraft etc.
x x x x
higher rates for rides out of business area x x
low rates for cars in lower frequented areas x x x
low rates for a special day time (barely used cars) x x x
restricted driving distance x x x
restricted parking x x
The analysis and classification of incentives so far was done on the operator’s level.
Currently – at least in Austria – there are no incentives or even no general regulation from
politics regarding urban planning / spatial impact on mobility. Although this was not of prime
concern in this work, nevertheless some analyses of the current status in Vienna were made
indicating that car-sharing operation is completely market-oriented in a competitive way
without any regulation – even if it can be assumed that policies might have an impact on
users’ behavior.
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3 Potential Future Incentives
3.1 Methodology and approach
In this section the methods aiming at collecting and specifying potential future incentives are
described briefly. The original plan was to develop further user-incentives regarding the
requirements of (e-)car sharing systems, developing a set of possible incentives and
consider their expected maintenance effects – based on discussion and evaluation with
industrial partners. As it turned out hard to get in touch with car sharing operators and
particularly to get internal information it was jointly decided to change the perspective from
the operators’ to the users’ point of view to eventually obtain a rating of possible incentives.
The first step was a creative workshop in accordance with the focus group approach within
the Austrian project team on ‘Possible future incentives – an intellectual game’. The
participants were all part of the project, with some insight based on the stakeholder dialogue
so far and therefore able to play a role and think alternatively in a plausible manner.
The introduction within the workshop was followed by the explanation of the role play, the
presentation of results and their rating. In a role play, „car-sharing operators“ and „users“
discussed and negotiated about possible incentives each from their own point of view.
This first classification showed a gap between the different stakeholders within the car
sharing process. Nevertheless, the results were taken into a second workshop with car
sharing users in which some insight was given on the project and the topic, but then again
six users discussed the following questions:
Willingness to refuel / charge the car?
Willingness to relocate the car?
Which incentives are expected / desired?
Other tasks that could possibly be performed.
Figure 3: Invitation top the second user-workshop on possible future incentives (TBWR)
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The methods to get to know more about the participants and on the posed questions were:
Short project presentation
Location of persons regarding their current car sharing usage. Results were documented on a
paper (see picture below)
Open Discussion
Documentation of the results
Figure 4: Location of participants regarding their car sharing usage / behavior (TBWR)
The results of this workshop on potential future and also existing incentives were the basis
for the web-based user survey. As described above, it turned out inapplicable to obtain
information from CS operators which eventually resulted in a decision to change
perspectives: thus considering to the users’ point of view as the existing incentives somehow
represent the possible range / willingness of operators.
The survey was performed online using the Survey Monkey framework. It contained 11
general questions on the person (demographic, socioeconomic data), 6 items on mobility and
private vehicles, 9 questions on car sharing and 7 questions on incentives. It was online for
10 weeks starting on May 26th until August 6th 2017 and distributed via the project partners,
mobility networks (WIMEN, BieM, etc.) Universities, Facebook Elfride Carsharing, public
accounts, twitter, etc. to more than 1.000 people whereof 58 people took part.
Specific questions regarding incentives:
How many extra minutes would you spend to walk to your car-sharing car if you are offered
the following reductions from the minute price (currently 31-38 cents) for individual travel?
How many times have you already experienced a vehicle with a tank filling / load of less than
25%, which will entitle you to get a bonus?
How many times have you already fueled a car-sharing car yourself?
To what extent do you personally appreciate the following incentives (possible "goodies") and
would these make you fill up a car-sharing car for the first time / more frequently? (multiple
selection)
How often have you already returned a discounted car-sharing car in a given zone or picked it
up from a given zone, irrespective of whether a further walking distance from / to the car-
sharing car has resulted?
To what extent do you personally appreciate the following incentives (possible "goodies") and
would these make you pick up /return a car in a given zone for the first time / more frequently?
(multiple selection)
What further activities could you imagine to take over (for your preferential cost
reimbursement)? (multiple selection)
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3.2 Potential new incentives
The following (existing and potential future) incentives have been identified within the two
workshops and the survey to be overtaken by users:
Refueling / charging
Carwash / cleaning
Relocation
Buying the vignette
Submit parking tickets or funds to a centralized collection site
Bringing the car to a workshop
Tire changing
Interior cleaning
Recruiting of new customers
Rating for cleanliness: every decent users get incentives (free minutes, bonus)
Tax relief for using car sharing
Personal CO2 balance: collecting free minutes
Host (station-based) as watchdog of the car to take care (maintenance and general condition
of the car): free minutes/hours for driving
3.3 User rating of existing and possible future incentives
The original plan was the evaluation of existing & possible future incentives (influences on
management efforts in practice) in expert workshops and interviews. As the CS operators
that have been contacted in the course of this project are largely unwilling to share any
information on management efforts or on the impacts of any of the incentives they provide to
their customers the WP methodology was adapted to still ensure quality results and
feasibility of the research approach: rather than attempting to capture the operators
perspective on user incentives the approach was adapted in terms of taking on the
perspective of the users of the CS systems. Thus, the general acceptance of existing and –
later on - potential new incentives could be established. However, as a consequence exact
conclusions on the amount of saved fleet management/maintenance costs as a result of user
incentives cannot be made. Nevertheless, an attempt was made to identify impacts on
various system-performance-parameters based on theoretical considerations (see section
4 Implementation rules (Incentives Models)).
The results of the survey showed that users would overtake the following tasks (in % of those
likely to perform the respective action):
62% Refueling / charging
33% Carwash/ cleaning
31% Buying the vignette
18% Submit parking tickets or funds to a centralized collection site
8% Bringing the car to a workshop
5% Tire changing: pick up tires from station / handing them back
10% Interior cleaning
31% Recruiting of new customers
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The rating of existing and potential future incentives within the first workshop led to the
following result:
Table 5: Possible incentives and rating at the project-team workshop (TBWR, AIT, UV-DSOR)
Incentives: Rating: operators’ view
(Distributed points / total: 24) Rating: users’ view
(Distributed points / total: 21)
Loyalty points / "Miles & More" 4 / 24 17% 3 / 21 14%
Free-minutes (extra-) with expiration date 6 / 24 25% 0 / 21 0%
Free-minutes (extra-) without expiration date
0 / 24 0% 5 / 21 24%
"Eco-points" environmental points 6 / 24 25% 1 / 21 5%
Benefit via cooperation (e.g. DriveNow / Sixt)
3 / 24 13% 0 / 21 0%
Free membership for one year (Sixt) 2 / 24 8% 1 / 21 5%
Hours packages 1 / 24 4% 1 / 21 5%
Cheaper rates for parking times 1 / 24 4% 3 / 21 14%
Cash 0 / 24 0% 4 / 21 19%
Credit (€) 1 / 24 4% 3 / 21 24%
Comparing to that, the survey showed a little different result:
Figure 5: Possible incentives and rating in % for filling up a car-sharing car (survey results, TBWR)
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3.4 Classification of (new) incentives
For both existing and potential new incentives an attempt was made to create links between
individual incentive categories (and corresponding user actions) and optimization objectives
and/or system performance.
„How can incentives promote and support the overall objectives of urban car sharing
systems?“
The following matrix shows how potential future (and to some extent in a similar way
existing) user incentives (based on the workshops and the survey) relate to various key
parameters and performance objectives of the car-sharing fleet performance. These
parameters refer to the optimization objectives of e4-share. Assigning incentives to
performance objectives is done in a conceptual fashion based on theoretical considerations:
‘x’ marks denote the assumption that a respective impact exists
‘(x)’ marks that the assumption about a respective impact is very unclear as there is no
detailed information if those incentives produce more traffic than the task would do in the task
area of the operator (e.g. it cannot be proven if additional trips for collecting free-minutes for
refueling or charging cause more negative environmental effects that the staff’s trips for the
same would do due to different organizational structures).
Doing this assignment, a link between the incentive work package (WP7) and the
optimization models is being established.
Table 6: Mapping and classification of potential future incentives regarding their ecological, economic, efficiency- and
electricity-impact and the different planning horizons (TBWR)
Impact Level
‘x’ = assumption that a respective impact exists ‘(x)’ = assumption about a respective impact is very unclear as there is no detailed information if those incentives produce more traffic than the task would do in the task area of the operator Ec
olo
gica
l …
Eco
no
mic
…
Effi
cie
ncy
…
Ele
ctri
city
…
Stra
tegi
c …
Tact
ical
…
Op
era
tio
nal
…
4. Potential future incentives:
free minutes without expiration date (x) x x x x
"Eco-points" environmental points x x x x
Cheaper rates per minute in the following month (x) x x
Loyalty points / "Miles & More" x x
Benefit via cooperation (e.g. DriveNow / Sixt) x x
Free membership for one year (Sixt) X x
Cheaper Hours packages x x
Cheaper rates for parking times X x x
Longer pre-booking-time for free in the following month x x x
Cash X x
Credit (5€) X x
Cheaper extra-km at next trips x x
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4 Implementation rules (Incentives Models)
4.1 Task objectives and approach
As a vital part of incorporating incentives into the models numerical data reflecting the
probability of user acceptance of the most important incentives provided by the CS operators
needed to be derived.
There are three fundamental ways of accomplishing this:
1. ‘Stated preference’: asking CS users whether or not they would accept a certain
incentive under specific conditions (facilitated via creative workshop, RPG; user
workshop; online user survey with a sample size of N=58)
2. ‘Revealed preference’: elaborating the acceptance levels of existing incentives by
specifically analyzing CS fleet data (practically not applicable due to CS operators
unwillingness to share data)
3. Theory-based deduction of acceptance probability adopting a Value of Time (VoT)
approach, implying economically sound user behavior and using available data on
average income levels (statistical office)
4.2 Deriving acceptance probabilities: ‚stated preference‘ approach
Inputs: Creative workshop / RPG: ‘Possible future incentives – an intellectual game’, CS user
workshop and online survey (see section 3.1.)
RPG: ‘Possible future incentives – an intellectual game’
The participants were organized into groups of two for a RPG: each one person assuming
the role of a CS operator (fleet manager, etc.) while the other one acted as a CS user. Role
character profiles were provided. The workshop was held January 23st 2017 at TBWR’
premises with AIT, University of Vienna and tbwr people (9 in total) attending. The game
consisted of negotiating potential incentives and their respective size until a consensus
between CS operator and user could be reached (if at all); A sample question issued by the
‘CS operator’ would be: ‘How many free minutes would I have to give you in order to make
you accept a little detour to a filling station and to refuel the car?’ The individual negotiation
results were presented to all and incentives were discussed among the WS participants. This
discussion was followed up by a joint assessment of incentives both from operator and user
perspectives (ordinal scale). Key results: service/task categories ranked according to user
acceptance among workshop participants (top one enjoys widest acceptance):
Refueling / charging
Car wash / exterior cleaning
Vehicle re-location
Buying a vignette / toll sticker
Delivering lost & found stuff or parking tickets to CS HQ
Car service runs
Changing tires
Car-interior cleaning
Acquiring new customers
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On top of this a parallel assessment of attractiveness of incentives both from operators &
users points of view was made during the WS.
Car sharing user workshop and online survey
In the second workshop with car sharing users the willingness to accept incentives for
various tasks was examined more closely.
In order to derive probabilities of acceptance from these stated preferences one could
basically use the odds of acceptance among the workshop attendees. However, in order to
safeguard these results an online survey was held among car sharing users in order to find
out specific willingness to accept incentives and the determining factors of this willingness.
We used the data from the online survey (as described above in section 3.1.) in order to
compute users’ ‘shadow wages’ from their stated preferences on incentives. The calculation
of shadow wages (as the individual valuation of time) was based on stated willingness to
take a walking detour to the CS car when offered a certain discount on the minute rate: users
were asked how many minutes of detouring they would accept when offered a 5%, 10%,
20% or 25% discount; The average duration of a CS trip is 33 minutes (in the user survey
sample). Based on a minute rate of 0,38 EUR this results in a total discount net value
between 0,63 EUR (5% discount) and 3,16 EUR (25% discount) offered as a “reward” for
taking the detour.
Based on the stated walking detours (in time units) and using the total net value of the
discount it is possible to calculate how users value their time (i.e. an approximation of a
shadow wage or valuation of time);
Table 7: Descriptives - shadow wages (TBWR)
Assuming rational choice it is deduced that the higher an individual’s shadow wage the
lesser is their willingness to accept detours for picking up / returning the CS car (and vice
versa). In the user survey sample, shadow wages range from 5,7 EUR/h to 189,8 EUR/h with
an average of 44,9 EUR/h (see Table 7).
In order to identify (relatively) homogenous user groups that share similar willingness of
accepting incentives a series of cluster analyses was carried out. Based on theoretical
considerations a set of variables was used in order to identify the key determinants of
acceptance levels: the individual’s perceived availability of time (perceived stress in terms of
time and daily tasks), shadow wage related to walking to (from) the CS car, CS experience
(expressed as no. of years since first using CS services), the respondent’s monthly net
income and the education level.
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Figure 6: Predictor importance for cluster formation (TBWR)
Above Figure 6 displays the predictor variables’ importance for the cluster formation; the
perceived availability of time is the most important covariate whereas education level is least
important. The cluster solution comprises 2 clusters with roughly 40% and 60% of the
records.
Figure 7: Cluster sizes (TBWR)
Examining the cluster profiles it can be concluded that cluster 1 (see Figure 8) is made up of
individuals with little or no willingness of accepting incentives for taking detours when picking
up / dropping off the CS car. This group of users can be described by having little time
available, valuing their time highly, being more experience CS users, having relatively high
monthly net incomes (mean value is some 1.766 EUR) and high education levels (mainly
academics).
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Figure 8: Cluster 1 profile (TBWR)
Cluster 2 consists of users that are likely to accept incentives for taking extra time when
picking up / dropping off their CS car (see Figure 9). By and large they state to have enough
time available to perform their everyday tasks. Also, they can be described as being less
experienced CS users as well as having lower average monthly net incomes (1.250 EUR) as
well as lower educational levels.
Assuming representativeness of the survey sample it can be concluded that the general odds
of incentive acceptance are equal to the cluster size ratio. Put differently, 61,5% of typical
users are likely to accept the incentive and perform the walking detours whereas 38,5% of
typical users reject the incentives.
Pertaining to the determinants of the acceptance probability, the willingness to accept the
walking detour decreases with income, educational level and age as well as with number of
years as a CS user (‘CS experience’). It increases with availability of time (stated, i.e.
perceived pressure of time).
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Figure 9: Cluster 2 profile (TBWR)
4.3 Theory-based deduction of acceptance probability
This approach is based on the idea that an incentive is likely to be accepted when at least
compensates or – even better - overcompensates for the time (discomfort, or - in more
general terms - disutility) related to performing the given task. Compensation is usually given
monetary, in kind or in any other form of quasi monetary values. In order to check whether or
not they are sufficient for an individual to actually pick up the task we need to find the
exchange rate for time (VoT) in order to express time in monetary terms. (VoT is a concept
widely used in transport economics and project appraisal.) It is very likely that the unit value
of time changes from person to person or even from day to day (subject to ‘perceived time
availability’, roles, income levels, psychological factors, cost awareness, etc.). However, in
order to simplify things we could use average values ( to express ‘average’ valuation of
time on a societal level).
In a similar fashion to the approach outlined in section 4.2 the theory-based deduction of
acceptance probability uses the individual’s valuation of time as a guideline for acceptance
levels. However, unlike in the above approach based on stated preference it relies on official
statistics in terms of finding numerical values for VoT.
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Figure 10: Incentive amount vs probability of acceptance: imaginary example graph (TBWR)
Figure 10 shows the underlying rationale: users valuing their time highly will require relatively
high incentive values in order to actually accept them, whereas users attaching lower value
to their time will be likely to accept smaller incentives for a given task. In the absence of any
stated preference data the exact form of these curves is unknown. However, using statistics
on income quantiles and assuming utility-maximizing behavior approximations can be
developed.
A first simplistic formulation of the probability of accepting a certain incentive may be written
as:
𝑝(𝑎𝑐𝑐𝑒𝑝𝑡𝑖𝑗) =𝑢𝑁𝐵𝑝𝑖𝑗
𝑢𝑉𝑜𝑇𝑖 - 1 [p values cut off outside 0….1 range)
where
𝑝(𝑎𝑐𝑐𝑒𝑝𝑡𝑖𝑗) Probability of individual i accepting the incentive for performing task j
uNBpij Unit net benefit for individual i yielded by performing task j for CS provider p
(measured in money terms, e.g. EUR/min)
𝑢𝑉𝑜𝑡𝑖 Unit value of time for individual i. This is the money value that i assigns to one
unit of her/his time (measured in money terms)
𝑢𝑁𝐵𝑝𝑖𝑗 =𝐼𝑗𝑝 −𝑡∗𝑢𝑉𝑜𝑇𝑖
𝑡𝑗
𝐼𝑗 Total Incentive amount offered by CS operator p for performing task j (e.g.
refueling); (measured in monetary terms or converted into money
equivalents)
𝑡𝑗 Actual time spent performing task j (measured in time units)
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In order to calculate numerical values of probability of acceptance data on the below items
are required:
(1) Incentives amounts: illustrated by quoting Car2Go as an example: 10 free minutes are offered
for 1x refueling; gross monetary value to the user would be some 2,5 EUR (based on hourly
rate/6)
(2) Time spent performing refueling: the sum of times spent for making the detour to the gas
station /charging point and for filling up; the average time spent for filling up & checking out is
estimated to be 5 min. Determining the amount of time spent for detouring is based on
analyzing fuel station accessibility
(3) unit value of time: since the true individuals’ values assigned to one unit of time are unknown
we use net income levels as a proxy and assume economically sound behavior (utility
maximization)
With regards to above point (2), the time spent performing the detour (illustrated by using
Vienna gas stations as a test case): assuming that trips start and end in residential or
populated areas, the time needed for the detour is derived by calculating the shortest path
from each Viennese building block to the closest filling station: a mean driving speed of 20
km/h is assumed resembling a reasonable estimate for Vienna.
Figure 11: Accessibility analysis of fuel stations in Vienna (TBWR)
In total approx. 53% of all Viennese addresses have a fuel station in <= 5min driving distance
interpretation: 53% of all CS trips would require a refueling detour of under 5 minutes;
Hence 47% of the trips would require more than 5 minutes to reach the closest
station
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Figure 12: Cumulative frequency of residential areas by fuel station accessibility (TBWR)
Pertaining to above point (3), the unit value of time:
The median net income for 10 minutes in Austria amounts to 1,91 EUR (based on 1736
annual working hours, 2015 figures)
Approx. 70% of the Austrian workforce earns less than 2,5 EUR for 10 minutes’ worth of work
about 70% of the workforce are getting paid less than car2go values refueling a car.
Hence it is sound to assume that 70% of the typical working population value their time less
than 2,5 EUR per 10 minutes.
4.4 Implementation of incentives for the free-floating car-sharing system
In our optimization algorithm for the free-floating car-sharing system, we incorporated the
incentives model and results from 4.2 and 4.3.
4.4.1 Offered incentives
Following cases were considered where users would be offered an incentive for their action:
Pick up a fully charged car from a recharging station
Return a car with low state of charge (SOC) at a recharging station
Pick up a car from a location with low future demand (e.g., a stranded car)
Return a car at a location with high future demand
In the optimization algorithm it is necessary to set the incentives amount and under which
circumstances they are offered, see Figure 13. Based on the results of the online survey,
following actual values were implemented:
Case Incentive
Pick up a car with SOC=100% from a recharging station 10 free minutes
Return a car with SOC ≤ 30% at a recharging station
Pick up a car where the future demand is less or equal 10 for
the next 48 hours Depends on the future demand,
reduction of up to 25% of the trip cost Return a car at a location where future demand is at least 10
for the next 48 hours
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Figure 13: Four types of actions with monetary incentives (AIT)
4.4.2 Acceptance of incentives
Another significant component in the optimization algorithm is how to model the probability
for a user to whether or not accept the offered incentive. We base our probability model on
the results of the online survey and the Huff gravity model:
- The user behavior clustering in 4.2 indicates that “61,5% of users are likely to accept the
incentive and perform the walking detours whereas 38,5% of typical users reject the
incentives.” Therefore in 38,5% of the cases the user simply takes the nearest car or ends the
trip exactly at the desired location.
- In other cases the probability that a customer takes/returns a car at location is proportional to
the incentive he or she gets and inversed by the walking distance, using the following formula:
𝑃𝑖𝑗 =𝑆𝑗
𝑇𝑖𝑗𝛼 / (∑
𝑆𝑗
𝑇𝑖𝑗𝛼)
4.4.3 Results overview
In the results for the Viennese use-case, by applying the incentives model described above,
we obtain the following values shown in Figure 13 and 14. We observe that in around 60% of
the cases the users pick up the closest car or return the car at the desired destination. This
includes both cases – users who do not accept incentives in general and situations where
the incentive is not “appealing enough”. The percentage of returning a car at a recharging
station or picking up a fully charged car from a station is only 1% – 2%, but this is mainly
because the situation does not occur too often.
In general, incentives are accepted in 15% of the times when picking up a car, and 21% of
the times when returning a car.
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Figure 14: Chart for user behavior when picking up a car (AIT)
Figure 15: Chart for user beviour when returning a car (AIT)
24%
61%
1%
14%
76%
Pickup locations
Unfulfilled requests
Nearest car
Fully charged car from a station
Car with incentive
23%
56%
2% 19%
77%
Return locations
Unfulfilled requests
Desired destination
Charging station
Other incentivized location
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