What works and what doesn’t: How to motivate people to walk the extra mile for better distributed free-floating carsharing cars?

Iljana Schubert, Michael Stiebe and Uros Tomic

Quelle: https://www.mobility.ch/de/medien/bildarchiv

Over the last years, we have witnessed a rapid growth in the number of free-floating carsharing schemes. These are mobility schemes where cars can be picked-up or dropped-off at any location within a city zone. Such a scheme exists, for example, in Basel with Mobility-Go. These mobility schemes reduce the need for people to own a car because they can quite quickly and easily book a shared vehicle.

The main challenge of free-floating carsharing schemes is that often cars are not where people need or want them. On the one hand, there are zones with high demand, where operators have to constantly make sure that there are enough cars for people to use. On the other hand, there are zones with low demand, where vehicles are standing around unused for a long time. To achieve a more balanced distribution of cars across zones, operators often have to move vehicles from a low to a high demand zone. Unfortunately, this “operator-based redistribution” as this redistribution is known is costly, energy-consuming and associated with higher CO2 emissions. Therefore, so-called user-based redistribution, which is not based on users driving the car around but on users walking further to more suitable pick-up and drop-off locations is preferable. To get people to walk the extra mile to less convenient locations operators try to use incentives.

Table 1: Table shows design aspects of the discrete choice experiment

In our project, we explored which incentives users of a Swiss free-floating carsharing service (Mobility Go) would prefer (see Table 1 for design). Through an online discrete choice experiment, we tested the preference for different incentives compared to a non-incentive (status quo) option. We asked 194 people to hypothetically choose different Mobility-Go cars from three different hypothetical locations (2 min-walk, 10 min-walk and 15 min-walk from the person’s hypothetical location). We compared four different types of incentives for picking-up the car at the three different locations. These were; 15 minutes free driving time (time), a discount of 7 Swiss Francs (CHF), collecting points (money) for social or environmental projects (social points) and collecting points to enter a lottery for a chance to win bigger prizes (prize draw). We also tested the same incentives plus an additional incentive (a guaranteed parking space) for dropping-off the car, again at three different locations. Examples of the choice tasks can be seen in figure 1- for picking-up – and figure 2 – for dropping-off cars. In both figures the status quo option, thus the option without incentives is displayed on the right. In figure 1 we also see an example of the prize draw incentive (in the middle) and the 15 minutes free driving time incentive (on the left). In figure 2, in addition to the status quo option, we see an example of the guaranteed parking space incentive (in the middle) and the social points incentives (on the left).

Figure 1: Example of a choice set from the picking experiment
Figure 2: Example of a choice set from the dropping experiment

The results showed that across picking-up and dropping-off scenarios users are most interested in receiving free driving time as an incentive, followed by a discount. In addition, users were equally interested in the guaranteed parking space in the dropping-off scenarios. Collecting points (money) for social and environmental projects (social points) and collecting points to qualify for a prize draw were not attractive to Mobility-Go users. Overall, the interest in incentives seems to decrease with increased age and level of education.

In this first step of our project, we tested different types of incentives within a hypothetical setting. In a second step, we will test a selection of incentives in a field experiment. Watch this space for more news!

Sustainable Supply Chain Management

Brick vs. Click – Analyse zur Abschätzung der Umweltbelastung des Einkaufs von Medikamenten im Versandhandel und im stationären Handel

Online-Shopping ist in den letzten Jahren zunehmend beliebter geworden. Auf globaler Ebene machte E-Commerce im Jahr 2020 19% des weltweiten Einzelhandelsvolumens aus und es wird erwartet, dass der Online-Handel mit einer durchschnittlichen jährlichen Wachstumsrate von rund 15% bis 2027 wächst. In der Schweiz belief sich der Wert der Online-Bestellungen im In- und Ausland auf rund 12% des gesamten Einzelhandelsvolumens im Jahr 2020. Dieser Trend erfuhr durch die COVID-19-Pandemie einen zusätzlichen Schub. In diesem Zusammenhang und aufgrund der Relevanz der Klimakrise wurde das Institut für Nachhaltige Entwicklung der ZHAW mit einer Nachhaltigkeitsstudie zum Handel mit Medikamenten über physische und Online-Verkaufskanäle beauftragt. Zu diesem Zweck wurde eine Ökobilanz durchgeführt, um die Treibhausgasemissionen über den gesamten Lebenszyklus einer Medikamentenpackung (ohne die Herstellung des Arzneimittels) für beide Handelskanäle in verschiedenen Regionen und Gebietstypen der Schweiz zu ermitteln.

Schematische Darstellung des Modell Versandhandel (oben) und des Modell stationärer Handel (unten)

Die Resultate der Studie zeigen, dass der Medikamentenversandhandel sich bezüglich der CO2 Emissionen besonders in intermediären und ländlichen Regionen lohnt. Dagegen erweist sich der Bezug von Medikamenten im stationären Handel in den städtischen Gebieten als die CO2-sparendere Option, wenn die Strecke zur Apotheke zu Fuss oder mit dem Fahrrad zurückgelegt wird. Bündelungseffekte können sowohl im Versandhandel als auch im stationären Handel einen relevanten Beitrag zur Senkung der Gesamtemissionen leisten. Durch eine optimalere Anpassung der Verpackungsgrösse an die Bestellmengen im Versandhandel und der Eruierung von alternativen und nachhaltigeren Verpackungsmaterialien (z.B. Mehrwegboxen, Kunststoff etc.) könnte die CO2-Belastung im Versandhandel zusätzlich reduziert werden. Darüber hinaus können durch die Wahl eines Verkehrsmittels mit tiefem CO2-Ausstoss pro km im Vergleich zu konventionellen Fahrzeugen wie z.B. ein E-Auto, der ÖV oder das Fahrrad die Emissionen im stationären Handel deutlich reduziert werden.

Der Medikamentenversandhandel lohnt sich bezüglich der CO2 Emissionen besonders in intermediären und ländlichen Regionen.

Resultate für den Versandhandel und den stationären Handel in der Region Zürich

Wenn man die Resultate aus der Studie auf die Strukturdaten der Schweiz überträgt, könnte sich für 35% der Wohnbevölkerung der Versandhandel in Bezug auf CO2 Emissionen lohnen, sofern der Einkauf in einer Apotheke nicht mit einer anderen Aktivität kombiniert werden kann. Für 63% der Wohnbevölkerung wäre der stationäre Medikamentenhandel zu bevorzugen, sofern der Einkaufsweg nicht motorisiert zurückgelegt wird.


Feichtinger, S., & Gronalt, M. (2021). The Environmental Impact of Transport Activities for Online and In-Store Shopping: A Systematic Literature Review to Identify Relevant Factors for Quantitative Assessments. Sustainability, 13(5), 2981.

Grand View Research (2020). E-commerce Market Size, Share & Trends Analysis Report By Model Type (B2B, B2C), By Region (North America, Europe, APAC, Latin America, Middle East & Africa), And Segment Forecasts, 2020 – 2027. https://www.grandviewresearch.com/industry-analysis/e-commerce-market

Reuters, (2021). Online share of retail sales jumps to 19% amid lockdowns – UN. https://www.reuters.com/technology/online-share-retail-sales-jumps-19-amid-lockdowns-un-2021-05-03/

Wölfle, R., & Leimstall, U. (2020). E-Commerce Report Schweiz 2020. Digitalisierung im Vertrieb an Konsumenten. Eine quantitative Studie aus Sicht der Anbieter.

Our activity

Costs of multimodal alternatives compared to the private fossil fuel car

The future of private car mobility might be predominantly electric and powered by batteries. However, the environmental impacts of electric vehicles increase significantly with larger battery sizes. When relying on car utilization, vehicles with smaller batteries (i.e. smaller car, shorter range) should generally be preferred. However, the adoption of small cars is exacerbated by misjudgments of customers: On the one hand, customers tend to misjudge their actual need for range in a car leading to range anxiety with regard to small EVs (Hao et al., 2020). Needel et al. (2016), for example, suggest, that carsharing could play an important role for increasing the diffusion of EVs, covering the rare cases when the range of an EV is not sufficient. Hoerler et al. (2021) empirically show a correlation between carsharing experience and openness to buy a small to mid-sized EV underlining the suggestion by Needel et al. (2016). As such, multimodal combinations of small EVs with carsharing or public transport could be a sustainable and comfortable alternative to owning a private fossil fuel driven car, without the need for strong behavior change. On the other hand, consumers commonly misjudge the total cost of ownership (TCO) of fossil fuel cars due to the suppressing of sunk and periodical costs, e.g. purchase price, maintenance, taxes, and insurance (Andor et al., 2020; Lane & Potter, 2007). While studies show that EVs could indeed lead to savings compared to owning a similar fossil fuel car, this is not yet considered by the general public (Bert et al., 2016). As such, awareness with regard to cost advantages of EVs as well as comparing the TCO of EVs with similar conventional vehicles might increase its uptake.

With our research, we addressed the various misjudgments and proposed alternative mobility lifestyles, both combining the use of a small electric vehicle (EV) for everyday trips up to 200 km and the use of alternatives for daytrips exceeding 200 km: the former in combination with public transport (alternative 1: EV + PT) and the latter in combination with carsharing/car-rental (alternative 2: EV + CS). We proposed a further lifestyle that combines public transport and carsharing/car-rental without car ownership, as this would result in an even more sustainable mobility lifestyle (alternative 3: PT + CS). Basing on data from the Swiss Household Energy Demand Survey (SHEDS), we calculated the TCO of the current mobility behaviour of Swiss car owners. Furthermore, we calculated the TCO of the respective alternatives and pose the following research question: Would Swiss conventional car users be financially better off, if they switched to one of the proposed alternative mobility lifestyles?

Results suggest that roughly 63 % of respondents would be financially better off switching to a combination of a small EV for everyday trips until 200 km and use public transport for the cases daytrips exceed 200 km. About 36 % of the respondents would be better off with a combination of a small EV and carsharing/car-rental and 96 % would be better off switching to a combination of public transport and carsharing/car-rental without car ownership. The following three figures show the percent of respondent that are better off by switching to the respective alternative differentiated by the size of the respondents’ conventional car.

To the best of the authors’ knowledge, this is the first study to investigate the TCO of multimodal mobility lifestyles compared to a lifestyle based on conventional private car use. Our results could be relevant for public policy, mobility planners as well as mobility service providers who could use our results for promoting the cost advantages of alternative mobility lifestyles.

The findings were presented at the European Transport Conference and the Mobility Research and Innovation in Switzerland Workshop at the Verkehrshaus in Lucerne:

Raphael Hoerler at the Verkehrshaus in Lucerne


Andor, M. A., Gerster, A., Gillingham, K. T., & Horvath, M. (2020). Running a car costs much more than people think—Stalling the uptake of green travel. Nature, 580(7804), 453–455. https://doi.org/10.1038/d41586-020-01118-w

Bert, J., Gerrits, M., Xu, G., & Collie, B. (2016). What’s Ahead for Car Sharing? The New Mobility and Its Impact on Vehicle Sales. The Boston Consulting Group.

Hao, X., Wang, H., Lin, Z., & Ouyang, M. (2020). Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles. Journal of Cleaner Production, 249, 119403. https://doi.org/10.1016/j.jclepro.2019.119403

Hoerler, R., van Dijk, J., Patt, A., & Del Duce, A. (2021). Carsharing experience fostering sustainable car purchasing? Investigating car size and powertrain choice. Transportation Research Part D: Transport and Environment, 96, 102861. https://doi.org/10.1016/j.trd.2021.102861

Lane, B., & Potter, S. (2007). The adoption of cleaner vehicles in the UK: Exploring the consumer attitude–action gap. Journal of Cleaner Production, 15(11), 1085–1092. https://doi.org/10.1016/j.jclepro.2006.05.026

Needell, Z. A., McNerney, J., Chang, M. T., & Trancik, J. E. (2016). Potential for widespread electrification of personal vehicle travel in the United States. Nature Energy, 1(9), 1–7. https://doi.org/10.1038/nenergy.2016.112


Parcel lockers to decouple customers from suppliers

In 2019, 84.4% of the Swiss population lived in urban regions. The urbanisation trend still increases. Due to this and the fact that online shopping has gained popularity, the traffic load on a city’s infrastructure has increased dramatically. Currently, 10% of the transport performance (in vehicle km) on Swiss roads is caused by freight transport, causing 21% of transport related CO2 emissions. It is expected that the parcel volume will increase by another 75% and freight transport by 37% until 2040, leading to an extended logistics fleet of 36%.

Consumers ask for a diversified assortment of goods and their instant availability, while logistics space has been pushed out of the city centres towards suburban or rural areas. The new legislation about inner-city densification increases this tendency even further, as the last available spaces have been reserved for housing and offices rather than for logistics space.

All this leads to challenges in different areas, as shown in Figure1.

Figure 1: Challenges due to increased freight traffic in cities

One challenge of logistics service providers (LSP) is the necessity of several delivery attempts if the customers are not at home to accept the parcel in person. This further increases the already dense traffic situation caused through LSP. To solve this problem, the customers need to be decoupled from the supplier without losing the prove that the customer has accepted the delivery and with guaranteed save storage of the deliveries. This can be achieved through the installation of parcel lockers. Parcel lockers are secure containers where parcels are held until the customer collects them. The parcel lockers are self-service pick-up stations where the right compartment opens after the customer has either entered a code or has scanned a provided QR code from the supplier. With this, parcel lockers decouple customers and suppliers in terms of necessity to be at the same time at the same spot.

Figure 2: Customer picking up delivery from a parcel locker (picture source: www.expertmarket.com)

To analyse how many parcel lockers would be needed within the city of Zurich and where to place them to have the highest possible convenience for customers, ZHAW-INE and ZHAW-IDP conducted a study and developed a model to simulate different scenarios. The unit of analysis were the northern districts of the city of Zurich (purple areas in Figure 3).

Figure 3: Northern districts of Zurich as unit of analysis

The goal of the modelling was to find a possibility where the residents of Zurich only lose a minimum of comfort compared to home delivery, while supporting to free the city from freight traffic.

Each day, approximately 7500 parcels need to be delivered to the northern districts of Zurich. As studies show, residents are not willing to walk more than 250m to a parcel locker to pick up or return a delivery. Subsequently, the developed model takes the parcel volume, the distance to apartment complexes, and the position of public transportation stops into consideration (see Figure 4), to naturally bind the parcel lockers into the walk to the next public transportation stop to work or back home for parcel pick-up or drop-off.

Figure 4: Apartment complexes (red squares) and public transportation stops with a 100m radius (green circles)

The simulation of different scenarios provided results that if 33% of the parcels are deposed in parcel lockers and each parcel locker has 38 compartments, 64 parcel lockers are necessary in the norther districts of Zurich (see Figure 5). If this scenario is chosen, more than 80% of the households arrive at the next parcel locker in less than 250m walking distance.

Figure 5: Zurich North with apartments (multiple colours), optimal parcel locker sites (red) and potential additional parcel locker sites (black)

The results of the study provide insights into the possibility to install parcel lockers to decouple customers and suppliers in the necessity to be at the same time at the same spot to hand over the parcel in person. Parcel lockers hold the supply safe and thanks to self-service, customers can pick up their delivery whenever it fits into their daily schedule, while reducing freight traffic within the city of Zurich, resulting in a higher liveability within the city.

Sustainable Supply Chain Management

A quantitative social network analysis approach to mitigate the ripple effect in supply chain networks

Supply chain network disruptions have become an increasingly relevant topic in literature and industry. Just recently the COVID-19 pandemic demonstrated the need for greater resilience in global supply chain networks. Disruptions lead to material shortages adversely impacting demand fulfilment and eventually resulting in operational shutdowns of nodes or edges in the network. Due to the interconnectedness of modern supply chain networks, such impacts may not be contained to one part of the network but cascade through the network, which is known as the ripple effect or risk propagation. The inherent complexity of today’s supply chain networks and the associated dynamics of the ripple effect increase the difficulty to predict and manage disruptions. The unavoidable and unpredictable nature of disruptions requires the ability of supply chain networks to prepare to, respond to, and recover from a disruption. Supply chain networks with such capabilities are called resilient. Current Supply Chain Network Resiliency (SCNR) literature is assuming homogeneous node risk capacities and is mainly focused around network types, proposing methods that are impractical for supply chain management (SCM) practitioners, such as rebuilding an entire network into a certain structure, which is not possible in practice. Further, it is too expensive for practitioners to simply enhance risk capacity of every node in the network.

The Sustainable Supply Chain Management team of the Institute of Sustainable Development fills this research gap by exploring a novel way to identify critical nodes in supply chain networks, which are then to be fortified to increase overall network resiliency. We present a quantitative social network analysis (SNA) based approach to selectively fortify nodes in complex supply chain networks, targeting ripple-effect mitigation and enhancing supply chain network resilience (SCNR). Our model can be used to analyze supply chain resilience and derive strategies to mitigate disruptions, specifically using betweenness centrality for fortification. The model can be applied by supply chain managers to existing supply chain networks.

To find and evaluate an efficient selection logic, we choose a quantitative approach using agent based simulation. First, an instance generator is programmed in Python, by which we generate random graphs and layout them in two-dimensional space. After graph generation and calculation of SNA KPIs (e.g. betweenness centrality) the data is exported to AnyLogic. In AnyLogic we deploy a Susceptible-Infected-Recovered (SIR)-model from epidemiology literature to mimic the disruption propagation of the ripple effect throughout the network. Initially, all nodes in the supply chain network are in susceptible state. By initially infecting one (or multiple) nodes, the propagation process is started and the “infection” spreads naturally throughout the network. Infected nodes recover after a certain time and thereby gain immunity against further infections.

For our simulation experiment we alternate the values of five parameters, thus creating 25 = 32 different scenarios, which are each run 30 times with a random seed. Consequently, we obtain 32 * 30 = 960 observations. This data is exported to Excel where we perform two regression analyses to quantify the impact of our parameter variation on network performance, measured by two different network performance indicators:

1) Healthy Nodes, representing how deep the dip in network performance is.

2) Recovery Time, representing the time duration until the network performance is back on its initial level.

Looking at the regression output tables we can draw the following conclusions:

With Healthy Nodes as dependent variable, we may explain quite a bit of its variation by the independent variables. We find that the interaction term fort_option * fort_metric is statistically significant at the 1% level and has a positive sign, meaning that if we fortify nodes at all, then selection of nodes based on betweenness centrality results in a better network performance compared to randomly fortifying nodes, making betweenness centrality a suitable node selection logic for supply chain managers to mitigate the ripple effect.

When we set Recovery Time as dependent variable, we find only low explanatory power in our regression model. We observe contradictory and implausible results. Obviously, mechanisms impacting Recovery Time are not well understood and most likely not adequately captured by the model.

Future research will focus on overcoming the limitations of our model:

  • Experiments were performed on the same, single graph. We plan to repeat experiments on other, different graphs with the same properties to validate our results.
  • Further, we plan to repeat experiments on graphs varying in size and structure, moving from random graphs towards small world and scale-free networks.
  • Next, we plan to explore the effectiveness of utilizing other SNA KPIs as selection criterion, such as node degree, degree centrality, closeness centrality or page rank.
  • Obviously, a deep dive on mechanisms affecting Recovery Time is required.

First results have already been presented at EurOMA. In a next step, simulation experiments will be extended as has been described above.