Originally published in: Research Collection Permanent ......Moreover, the constant growth in the...

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Journal Article

Carsharing demand estimationZurich, Switzerland, area case study

Author(s): Balać, Milos; Ciari, Francesco; Axhausen, Kay W.

Publication Date: 2015

Permanent Link: https://doi.org/10.3929/ethz-b-000087032

Originally published in: Transportation Research Record 2536, http://doi.org/10.3141/2536-02

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Carsharing demand estimation: Case study of Zurich area1

Date of submission: 2014-11-142

Milos BalacIVT, ETH Zürich, CH-8093 Zürichphone: +41-44-633 37 30fax: +41-44-633 10 [email protected]

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Francesco CiariIVT, ETH Zürich, CH-8093 Zürichphone: +41-44-633 71 65fax: +41-44-633 10 [email protected]

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Kay W. AxhausenIVT, ETH Zürich, CH-8093 Zürichphone: +41-44-633 39 43fax: +41-44-633 10 [email protected]

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Words: 4472 words + 8 figures + 4 tables = 7472 word equivalents9

Balac, M., Ciari, F. and Axhausen, K.W. 1

ABSTRACT1

Worldwide carsharing has grown significantly in recent years. The traditional round-trip model2

is no longer the only carsharing model offered. It is now being accompanied by more flexible3

options like one-way station based, free-floating and peer-to-peer carsharing. Moreover, it has4

become important to have tools that can estimate both the spatial and temporal demand for5

carsharing services, providing operators with a good instrument for planning their services.6

The work presented in this paper makes use of the multi-agent simulation tool (MATSim) to7

investigate the effects of supply on the demand of the existing round-trip service in the Zurich8

area. Additionally, the results provide guidance for the possible optimization of the carsharing9

service. We also present an implementation of a one-way station based model as a part of the10

MATSim framework and investigate the potentials of one-way carsharing service in the study11

area. Results show that there is still untapped potential of round-trip carsharing, but that service12

might need optimization. Furthermore, one-way carsharing, being more convenient, would13

generate a little less than three times more trips compared to the round-trip option.14


INTRODUCTION1

Carsharing first appeared as an alternative in Zurich, Switzerland in 1948 in a small housing2

cooperative (1). After ups and downs, with the development of the technology and growing3

awareness of the public it has risen to be a transportation mode worldwide. Carsharing programs4

today have nearly 2 million members on five continents (2). In recent years more flexible5

carsharing alternatives are accompanying traditional, round-trip carsharing, which require the6

car to be returned to the station where it was picked up. These include:7

1. One-way carsharing - where a vehicle can be returned at any station8

2. Free-floating carsharing - where a car can be picked and parked on (usually) any public9

parking spot within the service area10

3. Peer-to-peer carsharing - where privately owned vehicles are available for use by members.11

These new carsharing alternatives have been growing in the recent years because of their12

flexibility, but are also facing new problems that need to be addressed and that are limiting13

the operators. Among these problems are the possibility for the system to become unbalanced14

during the day and also during a bigger period of time, unavailability of vehicles for the return15

part of the tour, legal issues with peer-to-peer carsharing etc..16

In Switzerland, at the moment, there is only one carsharing operator (Mobility (3)) providing17

round-trip carsharing in the whole country, free-floating in Basel (4) and is partly involved18

in the peer-to-peer carsharing (5). Having 1,395 stations, with 2,650 cars, it serves nearly19

112,000 members. The paper focuses on the effects of the supply on the demand for the existing20

round-trip carsharing service and also gives first insights of the effects of replacing round-trip21

with one-way service, based on an implementation of the multi-agent transport simulation tool22

(MATSim (6)). The results are presented and discussed for the study area of Zurich, varying23

fleet sizes and the number of members.24

Even though the share of carsharing trips is marginal - estimated to be well below 1% in25

most places with existing carsharing service (see, e.g., (7, 8)), the modal share and number of26

members is constantly increasing (2). Therefore, it is very important to investigate carsharing27

with a model where supply actually affects the system and the demand. The MATSim framework28

is a suitable tool for this kind of analysis, as the proposed modeling framework described in the29

reminder of this paper can capture the relevant interactions. The results obtained on the potential30

demand and effects of the supply of the carsharing alternatives can be used by policy makers31

and carsharing operators.32

RELATED LITERATURE33

In the recent years, carsharing has attracted attention of many scientists around the globe. The34

main reasons are the potentially large impacts of carsharing on the environment, transportation,35

its social effects etc. Some of these impacts include: potential of carsharing to reduce the number36

of privately owned vehicles (up to 13 vehicles are replaced with one carsharing car (2, 9)), sold37

vehicles due to carsharing membership (up to 34% (2)), energy consumption (2), emission of38

pollutants (up to 56 % reduction (2)) and vehicle kilometers traveled ((10)).39

In short, one of the main advantages of carsharing is encouraging a more sustainable form40

of transport. Moreover, the constant growth in the number of operators, members and types of41

carsharing offered (2) and the growing presence of the "sharing economy" which is based on42

"sharing" rather than on "owning" (11), has opened many questions and issues that research is43

trying to tackle.44


From the operational research point of view, the largest attention was given to the possibility1

of one-way and free-floating carsharing fleets to become unbalanced. Various studies have dealt2

with this problem in the past (12–14) and a good categorization of various relocation strategies3

can be found in (14). However, all these strategies do not take into account that the demand4

would change with the change of the location of vehicles, which is very important to completely5

grasp the quality of the service. On the other hand, the literature on the supply optimization6

and supply-demand relationships is very scarce. An overview of the available literature on the7

carsharing demand estimation can be found in (15). They report that in the current literature,8

demand estimation has only been addressed for round-trip carsharing and that it is mostly context9

specific. Moreover, they report that literature on this topic for one-way and free-floating systems10

is non-existent. Recently, (16) has addressed this issue for free-floating carsharing in the Berlin11

area and it showed a large untapped potential of both round-trip and free-floating carsharing12

services in the simulated area. However, results are based on only one forecast for fleet size13

and number of members and does not investigate the relationship between supply and demand14

further.15

To the best knowledge of the authors only a small number of studies exists dealing with16

the optimization of the supply side of a car sharing service ((17, 18)). They, however deal only17

with one-way systems and try to satisfy the current demand with an optimal fleet size, thus not18

considering how the demand will adapt to the change of the supply.19

According to (19) and (20) vehicle availability is one of the most important factors in20

choosing to become a member of the carsharing program. Moreover, (21) dealing with one-way21

carsharing in Montreal area, shows that the sizes of the carsharing stations have a large impact22

on both availability and usage of vehicles in the area. Building on these results, the purpose of23

this paper is to fill in the gap in the literature on the demand-supply relationship for round-trip24

carsharing services, providing a general model that can be used for any situation and area, and25

to give first insights on the potentials of one-way service, using Zurich as the study area. The26

model presented is a useful tool for policy makers and carsharing operators to estimate the27

potential demand of new or changed services.28

METHODOLOGY29

MATSim (www.matsim.org) was used before to model the supply and demand for round-trip30

and one-way carsharing (16, 22). MATSim simulates a synthetic population in a virtual world.31

The synthetic population is generated using the census data and daily plan (activity chains32

and mobility tools) for each member of the population is derived from suitable diaries. The33

virtual world presents the road network - in this study it is a detailed navigation road network.34

During the iterative process of finding the stochastic user equilibrium each agent can adapt its35

plan according to its preferences (change transportation mode, departure time, route, location36

of his secondary activities (shopping and leisure)). The advantage of using an agent-based37

simulation tool over the traditional four-step models is the opportunity to answer complex38

scientific questions regarding carsharing user behavior. This comes from the fact that to correctly39

model carsharing both spatial and temporal location of vehicles is needed which aggregate40

four-step models can not provide.41

Both carsharing modes, used in this study, were integrated into the MATSim environment.42

Implementation of these carsharing alternatives builds on the previous implementations (16, 22)43

and provides a significant increase in detail, as will be explained below.44


Simulation Model1

Transportation alternatives used in the simulation, besides carsharing, are walk, bike, public2

transport (pt) and car. Walk and pt as modes can be used on a trip level (between any two3

consecutive activities), while car and bike can be used only on a subtour level (the start and the4

end location of a subtour need to be the same and a first trip with these modes need to start at5

home). In the case of round-trip carsharing, individuals are allowed to use it as a mode on the6

subtour level, meaning that they can pick-up a carsharing vehicle after finishing an activity at7

a given location and return it after coming back to the same location. The following steps are8

modeled and simulated:9

1. Agent finishes its activity, finds the closest available car and reserves it (making it unavail-10

able for other agents),11

2. Walks to the station where it has reserved the vehicle,12

3. Drives the car (interaction with other vehicles is modeled),13

4. Parks the car at the next activity,14

5. After finishing his activity the agent takes the car and drives to the next activity,..15

6. Before reaching the last activity in the subtour, agent ends the rental and leaves the vehicle16

at the starting station making it available to other agents,17

7. Walks to the activity,18

8. Carries out the rest of the daily plan.19

In the case of one-way carsharing (used on a trip level), the steps are similar, but with a few20

differences:21

1. Agent finishes its activity, finds the closest station with an available car and reserves the22

vehicle (making it unavailable for other agents),23

2. Walks to the station where it has reserved the car (takes the car and frees a parking spot at24

the station),25

3. Finds the closest station to his destination, with a free parking spot and reserves it (making26

it unavailable for others)27

4. Drives the car to the reserved parking spot (interacting with other vehicles on the network),28

5. Parks the car on the reserved parking spot and ends the rental,29

6. Walks to the next activity30

7. Carries out the rest of the daily plan.31

This new implementation of round-trip along with the newly introduced one-way carsharing32

model addresses previous implementation’s limitations by introducing:33

(a) station capacities including parking spaces for one-way service34

(b) reservation system35

(c) physical simulation of carsharing vehicles.36

Behavioral Model37

The behavior of agents is evaluated based on the utility function that evaluates each component38

of the agents daily plan (Equation (1)) generating a final score:39

Uplan =

m∑i=1

(Uact,i + Utravel,i) (1)40


where m is the number of activities that agent has in his daily plan. In general, performing1

activities increases the score (positive utility), while traveling decreases it (negative utility).2

The utility of an activity is defined as:3

Uact,i = Udur,i + Uwait,i + Ulate.ar,i + Uearly.dp,i + Ushort.dur,i (2)4

Udur,i is the utility of performing the activity, where the opening times of activity locations5

are taken into account. Uwait,i is the disutility for waiting (i.e. for the store to be opened) and6

Ulate.ar,i and Uearly.dp,i represent the disutility for being late and early respectively. Ushort.dur,i is7

the penalty for performing the activity too short.8

The specific components of carsharing (both round-trip and one-way) travel are:9

• Carsharing constant10

• Rental time fee11

• Time cost of walking (access and egress)12

• In-vehicle travel time13

• Distance cost14

The utility of traveling, using carsharing, between activities i − 1 and i therefore looks as15

follows:16

Utravel,cs = αcs + βcost,csCostt ∗ RT + βtt,walk ∗ (AT + ET )17

+βtt,cs ∗ TT + βcost,csCostd ∗ Dist (3)18

βcost,csCostt represents the disutility of time cost for rental. Access and egress walk stages19

are calculated separately using the underlying navigation network to calculate the shortest time20

paths and represented with βtt,walk ∗ (AT + ET ). βtt,cs presents the disutility of traveling while21

the distance cost is captured with βcost,csCostd. Constant αcs captures travel attributes not22

represented by other components.23

Equation 4 presents the utility of traveling for all other modes:24

Utravel,i = αmode + βtt,mode ∗ TT + βcost,modeCostd ∗ Dist (4)25

In this model for calculating the utility of traveling, access and egress times are not calculated26

but are captured in the constant parameter αmode. The other two components represent the dis-27

utility of traveling and the distance cost.28

These functions are used by the agents to determine, in the iterative process of the MATSim29

simulation, which mobility option suits them best. A detailed description of the carsharing30

utility function can be found in (23).31

SCENARIOS DESCRIPTION32

The study area is the ”Zurich-Greater area”, which is created by drawing a 30km radius circle33

around the Bellevue square in Zurich’s city-center. The area has a population of 1,622,164. The34

agent population is based on the 2000. census and 2005. national travel diary data. The different35

types of scenarios used to investigate carsharing alternatives were:36


1. Base Scenario: Only round-trip carsharing is available, with the current Mobility stations1

(492 stations in the simulated area with 911 cars) and membership assigned to agents,2

based on the logit model estimated previously by (24) resulting in 35,718 members.3

2. Round-trip scenarios (eight): The effects of different supply size on the round-trip carshar-4

ing is observed.5

3. One-way scenario: A complete switch from round-trip to one-way service and its effects6

are observed for the scenario with original fleet size and the number of members.7

All scenarios were run for 100 iterations, with a 100% population (meaning that all persons8

living in the simulated area are represented with a corresponding agent in the simulation). This9

is the first attempt to run 100% population in MATSim for the analysis of carsharing behavior.10

Using a smaller percentage of the population is usually done to reduce computation times, but it11

can produce undesired effects, especially for station-based carsharing as most of the stations12

have only 1 or 2 cars, which makes scaling down very difficult. Of course, this accuracy comes13

at the price of higher computation time, which on our servers (20 cores running on 3.33GHz and14

using 120GB RAM) was approximately 4 days, for each scenario. During simulations, each15

agent was allowed to change his transportation mode, route and departure time.16

RESULTS17

In this section simulation results of the current carsharing situation in the Zurich area is compared18

with the data obtained from the carsharing operator - Mobility. Next, effects of different supply19

sizes on the demand is presented and discussed. Finally, results of the switch from round-trip to20

one-way carsharing are shown and analyzed.21

Base Scenario22

Data obtained from the Mobility is from the year 2010 and is used to calibrate the simulation23

module and to determine the quality of the results. The data includes all stations and members24

in Switzerland, but rental data, within that year, is only for the Canton of Zurich. The rental data25

includes: id of the vehicle used, rental start and end times and when the vehicle was reserved.26

Actual simulated area, however, is larger than the Canton of Zurich, with more stations, so the27

booking data from the Mobility was scaled accordingly, to provide an approximation of the28

demand side in this area (Table 1). The scaling was done by multiplying the number of rentals29

and used cars with the ratio of the number of cars in the simulated area and the Zurich Canton.30

TABLE 1 Carsharing supply and demand.

Number of Mobility - Canton Zurich Mobility - sim. area MATSim - sim. area

Stations: 379 492 492Cars: 695 911 911Members: 26,814 34,897 35,718Rentals/work day: 730 ∼940 830Trips: - - 2,157Used cars: 475 ∼617 604Unique users: - - 814


Table 1 also shows the number of rentals obtained during the simulation with the current fee1

structure (0.60CHF/km and 2.8CHF/hour). A little underestimate of the number of rentals was2

expected since the Mobility data shows that almost 10 % of the rentals was with the vehicle type3

"transporter" used to move bulky loads. However, current day plans of the agent population do4

not differentiate between different types of shopping nor does it includes moving home.5

Figure 1(a) shows the comparison of the distribution of rental start times gathered from6

the Mobility data and observed in the MATSim simulation. Both distributions have three7

distinguishable peaks and are similar, except that MATSim simulation produces much larger8

peak in the morning and valleys between the peaks are lower than the ones from the real rentals.9

Differences between these distributions also arise because in reality users adapt their daily10

schedule (change order of their activities etc.), which is not modeled at the moment in the11

simulations (except the length of each activity), to match the availability of cars.12

Figure 1(b) shows the distribution of rental length for both data sets. Here the distributions13

clearly show that the most rentals are very short, however simulation results show a slight peak14

at 10 hour mark, suggesting that some rentals are used for work activities. The reasons for15

this could be that the public is not aware that some of them might benefit by using carsharing16

service for work and/or the calibration of the generalized cost of carsharing travel needs to be17

slightly adjusted to remove this peak. However, since the peak is not substantial, it was left to be18

addressed in the future work.19

(a) Distribution of rental start times.

(b) Distribution of rental length times

FIGURE 1 Comparison of the base simlation against observed demand.


These analyses show that the MATSim simulation (even though with mentioned limitations),1

produces results not very different from the actual behavior of the members of the carsharing2

scheme.3

Round-trip Scenarios4

Before increasing the carsharing fleet, we observed the number of rentals when allowing every5

agent with a driving license to use the carsharing service. There were 884 rentals, which is only6

slightly more than the number in the base scenario. This shows, that the demand is definitely7

limited by the current size of the supply. To see the effects of the fleet size on the number of8

users, we tested 4 additional fleet sizes - with 2 to 5 times larger fleets. Results for all four9

fleet sizes are summarized in Table 2. The increase in the number of rentals and trips is smaller10

than the increase of the supply and is reaching its limits with 4-5 times the original supply size.11

This was expected since not all members have a daily schedule that is suitable for round-trip12

carsharing, this finding is supported by the Mobility data which shows that only about 10% of13

users on each day use the service also on the next day. Moreover, it is noticeable that there are14

increasingly more unused cars during the day with growing supply. Looking at Figure 2 we can15

see that the distribution of trip distances (between two consecutive activities) for private car and16

carsharing are different. The distribution for private cars is left skewed with a very long tail17

with an average of 8.0 km per trip. However, carsharing trips have smaller shares for the first 318

km than car trips, but larger ones from 4-9 km, with no trips longer than 30 km, resulting in an19

average of 6.9 km. This is expected, since the high distance prices for carsharing is limiting that20

demand.21

FIGURE 2 Distribution of trips by distance.

Analyzing these results one must have in mind that the number of members was held constant22

and only current membership holders were affected by the increase of the fleet. However, these23

findings show that there is still space for the improvement of the current carsharing service,24

for instance by placing the unused cars into the areas of higher demand, and/or by increasing25

the number of available cars. On the other hand, these improvements might be limited by the26


constraints of the space available in a city for placing new or expanding the existing stations.1

Nevertheless, these findings can be used by operators to steer their development and efforts in2

the right way.3

TABLE 2 Round-trip carsharing simulation statistics with different fleet sizes for a con-stant membership.

Variable Original 2x fleet 3x fleet 4x fleet 5x fleet

Number of cars: 911 1,822 2.733 3,644 4,555Number of rentals/work day: 830 1,597 1,906 2,077 2,081Rentals increase over original[%]: - 192 229 250 250Number of trips: 2,157 4,088 4,993 5,409 5,414Trips increase over original[%]: - 189 231 250 250Average trip distance[km]: 6.9 7.0 6.9 6.6 7.0Number of used cars: 604 1,183 1,541 1,824 1,873Number of unique users: 814 1,553 1,856 2,013 2,029

The rental start times distributions are presented in Figure 3(a) for original, three times4

larger and five times larger fleet sizes (for the sake of readability other two were omitted here,5

since they do not differ from the others). Distributions for all three fleet sizes are very similar.6

Interestingly with the increased supply size the much larger morning peak disappeared and7

distributions are much more similar to the Mobility data. This can be explained by the fact8

that having more cars available compensates the previously mentioned non-modeling of users9

changing their daily schedule (removing one or more activities, changing the order of activities)10

to match the availability of cars. The larger fleet gives users more freedom, so they can keep their11

original daily schedule. Observing rental durations for increasing fleet sizes, it can be noticed12

that shorter rentals are increasing, the peak around 6 hours has disappeared and percentage of13

10 hours rentals is smaller (Figure 3(b)). These all can be accounted to the increased number of14

cars, availability and more freedom for users, thus allowing for more shorter rentals.15




FIGURE 3 Rental start times and durations for round-trip carsharing with differentfleet sizes.

Figure 4 shows the distribution of trips, done by round-trip carsharing, by purpose. Even1

though, return to home trips before ending the rental are usually not counted as a purpose, they2

are included here to have a better comparison later with one-way carsharing where home as3

purpose is regularly reported. The distributions are similar for all five fleet sizes, supporting the4

interpretation that with the larger fleet sizes more users with similar activity-chains are able to5

use carsharing. The only major change is the increase in the shopping trips. This is expected6

as there are more shorter rentals as the supply increases. Number of trips with a purpose to7

work might seem high, but taking into account that work activities reached by any mode, on8

average, last 5 hours and 36 minute and those reached by round-trip carsharing only 3 hours and9

36 minutes, this is not surprising.10


FIGURE 4 Trips by purpose for round-trip carsharing and different fleet sizes.

Round-trip Scenarios with an increased number of members1

As previously mentioned, one of the most important factors of becoming a member of the2

carsharing service is car availability. Therefore, the membership model mentioned earlier is used3

to estimate the number of members for the larger fleets. Statistics on the demand for each fleet is4

presented in Table 3. While additional cars bring an increase in the number of members, the fact5

that the cars are placed at the already existing stations is limiting this increase. Nevertheless,6

the statistics in Table 3 show that round-trip carsharing has still an untapped potential in the7

Zurich area. Doubling the fleet size of the current stations would more than double the number8

of rentals. On the other hand, with increasing the size of the fleet there is an increase in the9

unused cars which suggests that serious optimization is required to have an optimal service.10

This would also increase the profitability of the service for the operator.11

TABLE 3 Round-trip carsharing simulation statistics with different fleet sizes and in-creased number of members.

Variable Original 2x fleet 3x fleet 4x fleet 5x fleet

Number of cars: 911 1,822 2.733 3,644 4,555Number of members: 35,718 40,442 43,455 45,756 47,625Number of Rentals/work day: 830 1,696 2,259 2,646 2,901Rentals increase over original[%]: - 204 272 319 349Number of trips: 2,157 4,445 5,981 7,009 7,635Trips increase over original[%]: - 206 277 325 354Number of used cars: 604 1,202 1,683 2,097 2,365Number of unique users: 814 1,652 2,206 2,571 2,813

Figures 5 and 6 show that the rental start times and duration and distribution of trips by12

purpose, for each supply size is very similar to the base scenario. This suggests that new users13

are behaving and using carsharing in the same way as the ones in the base-scenario. This shows14


that by increasing the supply size, more people that have similar daily routines to the ones in the1

base-scenario, can make use of the round-trip carsharing service.2



FIGURE 5 Rental start times and durations for round-trip carsharing with differentfleet sizes and matching number of members.


FIGURE 6 Distribution of trips by purpose for round-trip carsharing with differentfleet sizes and matching number of members.

One-way Scenario1

To grasp the potential of one-way carsharing in Zurich a complete switch from round-trip to2

one-way was performed while keeping the current number of stations and cars, but adding one3

additional parking space per car at each station. The pricing structure for round-trip was also4

used for one-way carsharing for better comparability. Moreover, Kaspi et al. (25) shows that5

letting users to reserve the parking spot at the start of their rental increases the performance of6

the system. Therefore, in the simulations an available parking spot, closest to the destination7

location, is assigned to each user upon the start of the rental. In the future work, the focus will be8

on the development of a separate membership model for one-way service and also on analyzing9

the demand based on different fleet sizes. Table 4 shows the results.10

TABLE 4 One-way carsharing demand for original fleet and membership size comparedwith round-trip service.

Variable Round-trip One-way

Number of cars: 911 911Number of trips/work day: 2,157 5,778Average trip distance [km]: 6.9 6.0Number of cars used: 604 899Number of trips per used car: 3.6 6.4Number of unique users: 814 4,247

Table 4 shows that there is a large potential for one-way carsharing with almost three times11

more trips conducted than with round-based carsharing. Moreover, utilization of vehicles was12

close to two times larger, almost 6.5 trips per used vehicle were made with one-way carsharing13

service. This increase in the number of trips comes from the much bigger flexibility of one-way14


carsharing, since the person can return the vehicle at any station and not having to pay for the1

car during his activities.2

Looking at the Figure 7, it can be seen that the start times distribution is similar to the one for3

round-trip carsharing, but there are some rentals after 22:00 which corresponds to renting after,4

late night, leisure activities. This is an additional benefit of one-way service, since members5

can use this option at night when the public-transport is not very frequent. Schmöller and6

Bogenberger (26) in their analysis of the free-floating carsharing in Germany, which is a more7

flexible type of one-way service, observe exactly the same effect.8

FIGURE 7 Distribution of rental start times for different operating schemes.

As expected work and home dominate the distribution by purpose of trips made with one-way9

carsharing (Figure 8). Unlike for round-trip service, average length of work activities after10

the trip with one-way carsharing vehicle is higher than the average (6 hours and 30 minutes),11

implying that the service is convenient for people having longer work hours. Moreover, having12

higher percentage of work is expected, since with one-way carsharing, users do not have to keep13

the rented car during the duration of their activities in otder to have it for the return part of the14

trip.15


FIGURE 8 Distribution of trips by purpose by operating scheme.

CONCLUSION1

The results indicate that there is still untapped potential for round-trip carsharing. Increasing2

the fleet size would mobilize more users among the current members. However, this increase3

saturates when the supply reaches four times the original fleet size. This increase in the supply4

allows users more shorter rentals than before, because of the increased availability of the cars,5

but it comes at the price of more vehicles being unused during the day. Therefore, removal of6

the unused vehicles and possibly increasing their numbers at the stations with high demand is7

necessary. On the other hand, assuming that the increased supply would also increase the number8

of members, we observed that a larger number of users emerge, that have daily plans with a9

sequence of activities where using round-trip carsharing is suitable. It is also observable that10

the number of unused cars is also increasing which is undesirable for the operator. Therefore, it11

seems that better assignment of vehicles to the stations and also finding new spots for stations12

is necessary for the optimization of the service. However, one needs to have in mind that the13

simulations presented here simulate only one work-day and that it might be that some cars14

are used during other days or during the weekends when users have different activity-chains.15

Finally, it looks that increasing the fleet size by the factor of two brings more rentals/car when16

the number of members is increased accordingly, which is important for the cost recovery of the17

service provider.18

Furthermore, we see that by replacing the round-trip with one-way service has a big potential19

in Zurich area. The results show that one-way may provide a much better option, being much20

more convenient for users and generating a little less than three times more trips than round-trip21

service. Additionally, as expected it is more convenient for making work trips than round-trip22

service. However, this switch from round-trip to one-way service comes at the price of larger23

requirements for parking spaces and as the literature suggests of re-balancing the system during24

the day to maintain the desired performance.25

MATSim as a simulation tool, allows us, though currently with some limitations, to inves-26

tigate sophisticated policy measures and to give answers that the traditional four-step model27

cannot. Improvements, presented in this paper, over the previous version of the carsharing28

implementation and also by using 100% of the population, some of the limitations have been29

addressed. Having this in mind, all findings presented in this study, can serve as an insight for30

the operators of the potentials of carsharing service in the Zurich area. Moreover, they can serve31


as the basis for the fleet optimization (removing unused vehicles, increasing the number of cars1

where the users where not be able to reserve the car, etc.), which will be a part of our future2

work.3

ACKNOWLEDGMENTS4

The authors would like to thank Mobility, for providing the data used in this research.5

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