INVESTIGATING DYNAMIC PRICING TO SOLVE THE FLEET REBALANCING PROBLEM IN BIKE SHARING SYSTEMS

Word count: 24,996

Amaury Hellebuyck Student number : 000140581086

Supervisor: Prof. Dr. Dries Benoit

Master’s Dissertation submitted to obtain the degree of:

Master in Business Engineering: Data Analytics

Academic year: 2018-2019

INVESTIGATING DYNAMIC PRICING TO SOLVE THE FLEET REBALANCING PROBLEM IN BIKE SHARING SYSTEMS

Word count: 24,996

Amaury Hellebuyck Student number : 000140581086

Supervisor: Prof. Dr. Dries Benoit

Master’s Dissertation submitted to obtain the degree of:

Master in Business Engineering: Data Analytics

Academic year: 2018-2019

Confidentiality Agreement

Permission,

I declare that the content of this Masters Dissertation may be consulted and/or reproduced, provided that the source is referenced.

Name student: Hellebuyck, Amaury Student ID: 01405810 University: Ghent University, Belgium

Signature: Abstract

Hellebuyck, Amaury1

1) Ghent University, Belgium

Bike Sharing is increasing in popularity and bike sharing systems are popping up in large cities all over the world. People tend to move away from the polluting transportation industry and welcome smart mobility concepts with open arms. One major problem all bike sharing operators are faced with is the rebalancing of the fleet. Currently, most operators are using a fleet of trucks to pick up unused bikes and redistribute them throughout the city. A new, experimental way of solving this problem is using incentive schemes, as a means of dynamic pricing, in order to motivate users to rebalance the system themselves. This research conducted a case study that focused on Mobit, a large Belgian bike sharing operator, and investigated whether or not using incentive schemes could lead to less trucks being used. The case study is two-fold. First, a qualitative in-depth interview was conducted to explore the topic and postulate a a-priori hypothesis. Secondly, a quantitative part was done to reject or accept this hypothesis. In this part a dataset provided by Mobit is being analysed. The results show that incentive schemes are an effective way to reduce the amount of trucks being used for the redistribution of the fleet. In the conclusion part, some managerial implications to implement a more dynamic pricing strategy are given.

Keywords— Bike Sharing, Dynamic Pricing, Incentive Scheme, Fleet Rebalancing, Smart Mobility Preface

Dear reader,

First of all, thank you for taking the time to read my master dissertation. If you still have any questions or remarks, do not hesitate to contact me and I will be happy to reply.

Since I was a kid, I have always had an entrepreneurial mindset. From selling candy on the play- ground to organizing prom in the final year. Going to university did not extinguish but invigorated this entrepreneurial drive. During these five beautiful years, I had the honour of working on small projects with some of my best friends as well as organize the graduation party for my fellow students, future colleagues and good friends. Writing this master dissertation was the final challenge before getting the degree of Master of Science Data Analytics in Business Engineering.

My entrepreneurial drive together with a profound interest in new technologies and modern urban issues was the perfect inspiration source for this research paper. One day I came across a new phenomenon in China called bike sharing. Start-Ups offering shared bikes to local inhabitants bringing a solution to a lot of societal and environmental problems large cities encounter. I started wondering why this did not exist in Ghent, the city of bike riding students. What started as a small idea for a possible Start-Up soon turned into late night readings on Smart Mobility and Bike Sharing Systems. Instead of working out the idea, I decided to dive deeper into one specific problem of the bike sharing industry and that is how the research question of this thesis came to life.

Through this way, I would like to thank everyone who made this research possible. First of all, my supervisor for giving me guidance and clarity where needed and always leaving the door open for questions of any kind. Secondly, Alexander De Bi`evre,co-founder of Mobit, for the collaboration in this research. Without the help of Mobit, this would not have been possible. And last but not least I would like to thank my family and in particular my parents. Not only for the unconditional love and support during these intense months of writing this research, but for making me into who I am today. I always tried to live up to quote of theirs: ’After all, it is not what you study that counts, but the person you’ve become when you finish those studies.’ Thank you.

Ghent, 4th of June 2019 Amo out.

i Contents

List of Figures iv

List of Tables v

1 Situating 1 1.1 Problem statement ...... 1 1.1.1 Research Question ...... 2 1.2 Relevance of the paper ...... 2 1.3 Composition ...... 3

2 Introduction 4 2.1 Sharing Economies ...... 4 2.2 Vehicle Sharing Systems ...... 6 2.2.1 Car Sharing ...... 8 2.2.2 Scooter Sharing ...... 13 2.2.3 Bike Sharing ...... 14 2.3 Advantages of Bike Sharing Systems ...... 17 2.3.1 Individual Benefits ...... 18 2.3.2 Public Benefits ...... 18 2.4 Disadvantages of Bike Sharing Systems ...... 19 2.4.1 Vandalism and Theft ...... 19 2.4.2 Bad Parking Behaviour ...... 20 2.4.3 Lack of Infrastructure ...... 22 2.4.4 Load Balancing ...... 22 2.5 Pricing ...... 25 2.5.1 Static Pricing ...... 26 2.5.2 Dynamic Pricing ...... 26 2.6 Hypothesis ...... 31

ii Contents iii

3 Methodology 32 3.1 Research Design ...... 32 3.1.1 Mobit ...... 33 3.2 Research Methods ...... 35 3.2.1 Qualitative Research ...... 35 3.2.2 Quantitative Research ...... 39 3.3 Reliability and validity ...... 54 3.3.1 Reliability ...... 54 3.3.2 Validity ...... 55

4 Results 56 4.1 Qualitative Research ...... 56 4.1.1 Competition in Belgium ...... 57 4.1.2 Pricing Strategy ...... 60 4.1.3 Motives for using Bike Sharing Systems ...... 61 4.1.4 Fleet Rebalancing Problem ...... 61 4.2 Quantitative Research ...... 62 4.2.1 Descriptive statistics ...... 62 4.2.2 Conclusive results ...... 69

5 Discussion 74 5.1 Brief Summary of Results ...... 74 5.2 Conclusion ...... 75 5.2.1 Managerial Implications ...... 76 5.3 Discussion ...... 76 5.4 Limitations ...... 77 5.5 Suggestions for future research ...... 78

A Appendix 79 A.1 In-depth Interview ...... 79

Bibliography 85 List of Figures

2.1 Global two-yearly evolution (2006-2016)...... 8 2.2 Global overview: Members (2016)...... 10 2.3 Global overview: Fleet (2016)...... 10 2.4 China: Pile of Bikes...... 21 2.5 Map: Typically full or empty stations in Washington D.C...... 29

3.1 First 10 lines of the raw data from the .txt-file...... 41 3.2 First 10 lines of the structured dataset...... 41 3.3 Density plot of gps deviation...... 49 3.4 Partial density plot of gps deviation...... 50 3.5 Example of ordered dataset...... 52

4.1 Plot of amount of trips per day of the week ordered from most to least. . . 63 4.2 Proportion of trips during weekday and weekend...... 64 4.3 Proportion of trips picked up by user or truck...... 65 4.4 Map: Operational cities of Mobit in Belgium...... 66 4.5 Map: Centre of Kortrijk...... 72 4.6 Map: Outskirt of Kortrijk...... 72

iv List of Tables

2.1 Global market in terms of members and fleet size (2016)...... 9

3.1 Tariffs per 20 min...... 34 3.2 Number of unique counts...... 43 3.3 Original data types of different variables...... 44 3.4 Number of missing values per variable...... 45 3.5 Modified data types of different variables...... 46

4.1 Overview of Belgian Bike Sharing Market...... 59 4.2 Descriptive statistics of Full dataset...... 63 4.3 Descriptive statistics of subset Before Bonus Bikes...... 67 4.4 Descriptive statistics of subset After Bonus Bikes...... 67 4.5 Descriptive statistics of subset from Weekdays...... 68 4.6 Descriptive statistics of subset from Weekends...... 68 4.7 Descriptive statistics of subset from Centre Data...... 69 4.8 Descriptive statistics of subset from Outskirt Data...... 69 4.9 P-value per city...... 71

v Chapter 1

Situating

This first chapter covers the definition of the problem statement and the according research question of this dissertation followed by a view on the relevance this research. The chapter ends by giving an overview of the composition of this dissertation.

1.1 Problem statement

Bike sharing is seen as a solution to many problems of modern society. Because of rapid urbanization, there is an overload of cars in the cities. People drive to work with their cars on a daily basis causing low air quality, excessive air pollution, congestion etc. One reason why people do not shift to public transportation is called the last mile problem. Even when they use bus, tram, train, metro or other means of transportation, there is always a certain distance to cover from the final destination of the transportation method to the final destination of the user. This is not the case when using a car. A bike sharing system offers the perfect solution to cover this last mile.

Another recent trend within society is that people become more and more conscious about their ecological footprint. They tend to move away from everything that is polluting the earth. Off course, emissions of transport have a big, negative impact on the environment. Riding a bike is an emission-free means of transportation and when integrated well in the public transport it offers an efficient and convenient alternative to the car. People are shifting more towards this solution.

But the truth has to be said, bike sharing systems are not fully emission-free. When the system needs to be rebalanced because of full or empty stations or bikes being left at places

1 Chapter 1. Situating 2 where they are not likely to be picked up again, trucks drive around to collect these bikes and rebalance the fleet. This is disastrous for the green and sustainable image of the bike sharing operator.

1.1.1 Research Question

Using trucks to rebalance the fleet is not the only option. The bike sharing operator could integrate a dynamic pricing system in order to keep the bikes on places where they are likely to be used and optimise revenues in the meantime. Off course, it is difficult as a bike sharing operator to write a dynamic pricing algorithm when you do not know the exact value a customer assigns to certain characteristics of a trip. For example, when the bike sharing operator knows people do not want to pick up a bike outside of the city centre, he could charge a high fee to park the bike in the outskirt of the city. It is important that the dynamic pricing reflects the true values of the customer and is transparent to the user.

A good, intermediate solution to figure out what the user values is using incentive schemes. This makes the pricing system less static and makes it possible to see in what situation people accept the incentive and in what situation they do not. This is also a perfect way to decrease the usage of trucks and rebalance the fleet by giving users free trips or discounts on future trips when using bikes that are left unused for a long time.

This research investigates whether or not dynamic pricing and more specifically incentive schemes could work as a means to lower the usage of trucks and help solving the fleet rebalancing problem.

1.2 Relevance of the paper

Smart Mobility is building the transportation network of tomorrow. It is using Internet of Things and other new technologies to shape the way we move in cities and make transport cleaner, safer and more convenient. Bike sharing is one application of this smart mobility and could really revolutionise the transportation industry. This research could help shape the future of bike sharing by investigating whether or not these incentive schemes work.

If they prove to be effective, it will have a lot of positive effects on society, the environment and the bike sharing operators. These reflect the three P’s, Profit, Planet and People. There is an interaction between these three factors which will be explained in the next paragraph. Chapter 1. Situating 3

If the incentive schemes work. The bike Sharing operator would be able to operator more efficiently and at lowers costs because less trucks would be used for the rebalancing. The effect of this would be two-fold. A first consequence is for the environment. Less trucks used equals less emission gasses which is beneficial for the environment. A second consequence is on societal level. If the bike sharing operator can operate more efficiently, this will improve user convenience and satisfaction levels. This will result back in a positive impact for the bike sharing operator and result in more users and trips being sold. The fact that less emission gasses will be emitted also influences the society. Better air quality in the cities is a pleasant consequence. Another benefit for society when the use of bike sharing systems increases is less traffic in the city and thus less congestion on the roads. It is obvious that a positive outcome of this research could be beneficial on a lot of levels.

1.3 Composition

This thesis starts with a thorough introduction to the existing literature. The concepts sharing economy and vehicle sharing system will be explained more in detail followed by a detailed description of bike sharing and the advantages and disadvantages. One part of the introduction will focus on dynamic pricing and incentive schemes and this section ends with stating the hypothesis of this research. Secondly, the methodology of the research is revealed. This section is subdivided into a part about the research design, a part about the research method which elaborates further on the qualitative and quantitative research and ends with a section on the reliability and validity of the conducted research. This is followed by a description of the results of both the qualitative and quantitative research. Finally a discussion part will cover the conclusion and limitations of this research and give suggestions for future research. Chapter 2

Introduction

On the 28th of July 1965, at 3 ’o clock in the afternoon, the first white-painted bicycle is left unlocked in the streets of Amsterdam, ready to be used free-of-charge. It was Luud Schimmelpennink, member of the Provo, who announced the White Bicycle Plan in his pamphlet Provokatie nr. 5. The idea was simple. A certain amount of white-painted, unlocked bikes would be distributed throughout the city. People could freely use them and leave them wherever they pleased. But, the idea was more than that. It was an anarchistic, communist move to stop capitalistic, polluting industries. Not only was it a move to stop the auto-authority, as Schimmelpennink called it. It was also an environmental friendly solution to reduce the emission gasses, such as carbon monoxide, emitted by cars. This whole concept was against the will and vision of the government and politicians. Shortly after the launch, the police began impounding the bicycles because it was in violation of a 1928 law to leave bikes unlocked in the streets. As fast as it arose, the White Bicycle Plan disappeared.

Although it was only of short duration, it was the first example of a Bike Sharing System, a special kind of Sharing Economy.

2.1 Sharing Economies

The term Sharing Economy was introduced in 2008 by Lawrence Lessig. According to Lessig, a sharing economy is characterised by the collaborative consumption of goods through exchanging, sharing or renting out these goods, without having the ownership. (Lessig, 2008) This Collaborative Consumption is one of the most important new concepts within sharing economies. Collaborative Consumption is sharing goods and service in real

4 Chapter 2. Introduction 5 life or online with other people. (Hamari et al., 2016) In the literature plenty of different definitions for Sharing Economy and Collaborative Consumption can be found. Russell W. Belk (2014), leading authority on Sharing Economies defines Collaborative Consump- tion as people coordinating the acquisition and distribution of a resource for a fee or other compensation. This fee could be of monetary nature but this is not a necessity. Another definition of Sharing Economy by world-leading sharing economy expert and consultant, Benita Matofska, is given. She defines it as a socio-economic ecosystem built around the sharing of human and physical resources. It includes the shared creation, production, distri- bution, trade and consumption of goods and services by different people and organisations. (Matofska, 2016)

Sharing economies are nothing new. Since time immemorial, sharing has been one of the means to maintain and build social relationships with peers. Starting in the prehistory, people shared everything they had because living as nomads, wandering around from one place to another preying for food, was not easy carrying around too many belongings. This behaviour continued. During the time of the Roman Empire, soldiers walked all the way through Europe. They did not carry all their belongs and had to share with each other and the people of the cities they were staying in. Later, revolution and industrialisation caused people to move from the country side to the cities, where they stopped living a nomad life. People started collecting and building assets, they started trading more than sharing. The general tendency of individuals owning more goods privately, led to less frequent usage of these goods. Producing more than actually needed and wasting and depleting precious resources are unintended consequences. Collective purchase and on-demand use of goods could solve these new problems. The economy should evolve to a situation in which access is more important than ownership as Rifkin mentioned in his book Ages of Access. (Rifkin, 2001)

In the past, assets were shared mostly with relatives or friends. Sharing with known peers gave more security. As of today, those securities can be granted by legal frameworks, making it possible to share with total strangers. This trend is called Stranger Sharing. (Frenken & Schor, 2017) A good example is time-sharing, where people co-own a property with others they never met before. All owners get a certain time-frame in which they have the right to visit the property. Other well-known companies whose business plans are based on Stranger Sharing are AirBnB, Uber, CoachSurfing etc.

There are a couple of different business models that companies use in the sharing economy industry. Most businesses within the Sharing Economy act as P2P-platforms matching Chapter 2. Introduction 6 supply and demand of assets and make sure there is a legal framework in which all actors can operate. Another option is for companies to own the assets and make it possible for individuals to use them on-demand, for free or in exchange of a fee. This model takes away the risk and cost for individuals to invest in expensive assets. A perfect example is buying a car. For most people this is a big investment while they only need it at certain moments in time to cover small distances. On top of this, the fixed costs of owning a car are relatively high compared to the variable costs per trip, giving owners an incentive to drive more than needed. This has a negative impact on the environment as well as road occupation and quality of the infrastructure. (Shaheen et al., 1998) This same study showed that people use their car maximum one hour or less every day in a normal 24h-day. This is highly inefficient, costly and a waste of valuable resources. Some start-ups saw the potential and created the first Vehicle Sharing Systems.

2.2 Vehicle Sharing Systems

’All ways lead to Rome’ is a commonly used Latin proverb but if it was not because of the outstanding and efficient road infrastructure of the Roman Empire, nobody would use it today. Transport has been the engine of growth within societies and economies. It creates prosperity and wealth in regions. It connects people and makes operating businesses more efficient. A perfect example is trade over the Silk Road, connecting the West and East making it possible to transport unknown spices and goods to the other side of the world.

Following in the footsteps of Luud Schimmelpennink, people realised the evolution of trans- port did not only bring a freedom to move, but also a lot of negative aftereffects. Traffic congestion, air pollution, traffic noise, exhaustion of fossil fuels to name a few. (Katzev, 2003) These problems mostly arise because of the fact that almost every individual owns a car, and drives it whenever they need to go somewhere. Although a lot of possible solutions have been tried such as congestion pricing, employee parking cash-out incentives, transit passes etc. evidence shows that they only had little effect on discouraging individuals to use their car. (Katzev, 2003) If people do not want to change their mode of transportation, we have to change the way they can access it. Vehicle Sharing Systems could offer a solution.

Three different types of Vehicle Sharing Systems can be distinguished. Technically speaking, the first option is renting a vehicle which can be seen as a sharing concept. A rental company invests in a fleet of vehicles which can be rented for a certain amount of time. Those rentals are mostly not as flexible as the other two systems. The vehicle has to be Chapter 2. Introduction 7 returned to a branch of the rental company and rental companies mostly charge the full daily rate regardless of the duration and distance of the trip. (Katzev, 2003) The second system is individuals sharing the same vehicle at the same time. Think of Carpooling, where individuals going in the same direction at the same time, agree on sharing a privately owned car of one of these individuals. A good example is BlaBlaCar. BlaBlaCar offers a digital app that functions as a platform on which people offering a trip get matched with people needing one. A study on the environmental impacts of carpooling in the United States proved that carpooling significantly reduces both the emission gasses contributing to climate change as well as those contributing to air pollution. (Javid et al., 2016) Other advantages of carpooling include reduced parking demand, reduced travel demand, reduced pollution, lower travelling expenses and natural resource conservation. (Dewan & Ahmad, 2007) However, some authors argue that carpooling is not a form of vehicle sharing but rather Passenger Ride Sharing. This is open for interpretation. Thirdly, a system exists in which the vehicle is not owned by an individual but by a government or an enterprise. The individual can use it for a certain fee over a certain time-window. Mostly, the car has to be left in a dedicated parking spot or parking area, generally in the cities. This gives the driver more flexibility to plan a small trip than with rental companies since rental companies demand their vehicles to be returned to one of the offices. Another aspect that makes the third option more flexible is the possibility to rent the car for the exact duration of the trip. You can rent a car from start to end destination while rental companies mostly charge a flat fee per day.

There is also a difference in flexibility between the last two systems. In Carpooling, the flexibility is limited because there is a need for collaboration between two or more individuals going in the same direction at roughly the same time. The last system is fully flexible and only dependent on the availability of a vehicle at the preferred location and time of use. This system provides a vehicle at the right time, on the right place. In this thesis, when referring to a Vehicle Sharing System, the last option is meant.

The next paragraphs will zoom in on the different types of Vehicle Sharing being Car sharing, Electric Scooter sharing and lastly Bike Sharing. Chapter 2. Introduction 8

2.2.1 Car Sharing

History

Similar to the White Bicycles, Luud Schimmelpink introduced Witkar in 1973 but this concept also failed to deliver. The first successful Car Sharing Systems popped up in the late 1980s in Switzerland and Germany and spread throughout Europe. Later, in 1998, the concept crossed the Atlantic Ocean and emerged for the first time in the United States. (Katzev, 2003)

Shaheen et al. (2018) showed that in 2016, the combined amount of vehicles worldwide of all car sharing initiatives was estimated at around 157,000 vehicles and approximately 15,000,000 members used the services. Compared to 2014, this is a compound annual member growth rate of 76% and a compound annual fleet growth rate of 23%. Asia is the largest participating continent with 57.95% of the total amount of global members and 42.77% of the fleet. The second largest continent is Europe and accounts for 29% of the members and 36.75% of the fleet. On the third place comes North-America with 12.21% of the members and 16.95% of the fleet. The other continents account for the remaining 0.84% of members and 3.98% of the fleet. These numbers (Cf. Table. 2.1 and Figure 2.1) show the increasing popularity of Vehicle Sharing Systems. More and more vehicle sharing systems are popping up in large cities and the fleet of existing initiatives is growing.

Figure 2.1: Global two-yearly evolution (2006-2016). Chapter 2. Introduction 9

Continent Members Vehicles Asia 8,722,138 67,329 Europe 4,371,151 57,857 North-America 1,837,854 26,691 Oceania 96,600 5,040 Africa 15,100 379 South-America 7,350 120 Total 15,050,193 157,416

Table 2.1: Global market in terms of members and fleet size (2016).

Round-trip vs One-way trip Carsharing

A big difference among the car sharing systems is in the flexibility of the returning point of the vehicle. In Round-trips, the user has to return the vehicle to the point where he picked it up. In One-way trips, there are some different alternatives. In some systems, the user can leave the vehicle at any location he/she wants. This gives the user the highest flexibility and convenience. Other systems are working with fixed stations but leave the user free to choose at which stations he/she leaves the vehicle. That way, a certain amount of flexibility is guaranteed and the car sharing operator has lower operational costs of collecting the vehicles for check-up or redistribution. A disadvantage of this system is the risk of driving to a full station and not having a spot to leave the vehicle behind.

In the same study of Shaheen et al. (2018), they found that in 2016 the round-trip car sharing accounts for 69,03% of the members and 73,39% of the fleet, while one-way trips account for the remaining 30,97% of the members and 26,61% of the fleet. (Cf.figure 2.2 and figure 2.3. Note that n is the number of vehicle sharing initiatives.) (Shaheen et al., 2018) Chapter 2. Introduction 10

Figure 2.2: Global overview: Members (2016).

Figure 2.3: Global overview: Fleet (2016). Chapter 2. Introduction 11

Reasons of use

There are several reasons why individuals opt for a car sharing system instead of other modes of transportation such as a privately owned car, public transport, taxi services etc.1. A first set of reasons is linked to the costs. Studies showed that there is a significant positive effect of the cost gap between original mode of transport and the car sharing alternative, on using the car sharing alternative. The bigger the gap, the more likely an individual will use a shared car. (Yoon et al., 2017) This shows the importance of having a competitive pricing strategy. This same study revealed that people choose for a car sharing vehicle when costs for taxis are too high. Uber and Lyft are two perfect examples that prove this point. In the most recent years, the use of those ride-hailing apps only increased in popularity and they take around 16 million trips every single day. Those are trips that presumably would have been carried out by regular taxis but since Uber and Lyft have a more competitive price than taxis, those taxis are losing the trips.

Meijkamp (1998) found that the flexibility of a car sharing system offers a perfect alter- native for two less flexible systems namely, car rental or taxi services. On the one hand, a car rental service is perfect when it comes to long, round trips. On the other hand, a taxi service is useful for short, one-way trips. The arrival of car sharing systems provides the perfect alternative for anything in between. Be it a long, one-way trip or a short, round- trip. Another reason why people make use of car sharing systems is because they find it more convenient to find parking spots. In most cases, a car sharing company has fixed parking spots in certain areas of the city making it very easy to plan your trip, knowing up front where you can park your car. (Yoon et al., 2017) It is also possible that cars from car sharing organization do not have to pay any parking fee because they collaborate with cities to promote these green intiatives.

Impact

Modern transportation of individuals and goods has a lot of negative economical, ecological and societal consequences. Sharing modes of transportation can have a positive influence on these effects. A division can be made between impact on individual mobility patterns and impact for the society in general.

The most important impacts on Individual Mobility are the following. Loose (2010)

1The other modes of transportation will be referred to as original mode of transport in the rest of this thesis. Chapter 2. Introduction 12 shows that the use of a car sharing system replaces in general four to eight cars on the roads. This means less individuals buy a car and some even sell their car because they use car sharing systems. Two studies confirm this and show that approximately 30% of the households sold their car or waited with the purchase of a new car. (Cervero & Tsai, 2004; Group et al., 2009) The use of public transport increased because of the integration of car sharing in the total transportation network. Katzev (2003) showed that individuals use more public transportation and alternative transportation modes, such as bicycling and walking, when they have the option to integrate car sharing in to their route planning. Shaheen et al. (1998) found the same conclusion and added the fact that individuals reduce their kilometers travelled by car with 33% to 50%. In the Netherlands, Meijkamp (1998) found that the amount of kilometers travelled was indeed reduced by 33% and noticed a more frequent use of train and bike with respectively 37% and 14%. Lastly, the findings of Duncan (2011), are consistent with other literature and stipulate that there is a reduction in mileage between 42% and 50%. All of this reduces the ecological footprint of individuals by reducing their emission gasses significantly.

Of course, car sharing does not only have an impact on individual level. There are societal, environmental and economical benefits of vehicle sharing systems that effect the General Society. The study of Loose (2010) concluded that the decrease of individuals owning a vehicle results in lower demand for public parking space, increased road safety, lower energy consumption, decreased local pollutant emissions and less congestion in the cities. A car- sharing fleet can account for 15-25% lower CO2 emissions, compared to private car fleets. (Group et al., 2009) Car sharing offers a perfect complement to the existing public transport, walking and bicycling making it more convenient for society to use public transport. This helps to make urban mobility more sustainable. (Duncan, 2011) Because individuals are less likely to own a private vehicle and are less incentivised to use the car for making trips and rather use public transport in combination with car sharing, they drove 15-20% fewer kilometers than before the vehicle sharing systems came on the market. This decrease in car usage and ownership led to a decrease of 240 to 390 kilograms CO2 emitted per person per year. This is the equivalent of 13-18% CO2 emissions related to car use and ownership. (Nijland & van Meerkerk, 2017)

Car sharing is a good step towards a more sustainable mobility within urban areas and is the ideal solution for making trips of reasonable distances. However, it is not a perfect solution for trips covering small distances. In the next paragraphs, solutions that are more suitable for smaller trips are covered. Chapter 2. Introduction 13

2.2.2 Scooter Sharing

Another, more recent, alternative within the vehicle sharing systems is the sharing of elec- tric steps or kick scooters, also called Scooter-Sharing Systems. In 2012, Scoot Networks, launched the first version of a Scooter-sharing system in San Francisco, USA. Since 2017, they are popping up in every European City and later in 2018 the growth in USA also picked up. There are a lot of different players in this market segment. For example, in Brussels alone there are more than 5 operators all competing within the same operational area. This is beneficial for the user because there is a large supply within their preferred area and they have the flexibility to choose according to their scooter and price preference.

While a car is more convenient to cover longer distances, a kick scooter has some other advantages. First advantage is their compact, agile design which makes them flexible enough to move around in busy, dense, urban areas. The steps are electrical and have a maximum speed of around 25km/h, which is enough to cover small distances in a short time while driving safely. The fact that they are electrical gives them a green and sustainable image since they are carbon-neutral and have zero emissions. Another advantage is their size. Since they are rather small, they are easy to park almost anywhere in the city. This makes it possible for users to use the step until the desired, final destination. This system is called free floating and is the opposite of a station-based system, in which you have to park in a immovable station dock. Everything happens through a mobile app in which the user can find a charged step in the area, use the step and the payment happens automatically via a linked credit card. This all to increase the convenience of the user.

Of course there are some disadvantages with these steps as well. The flexibility to leave them wherever the user pleases causes the steps to end up literally everywhere. Some users are careless and drop the bikes wherever they want causing chaos in the streets and on the sidewalks. This problem is being treated more in detail in subsection 2.4.2. Another disadvantage is that electrical devices have to be charged. Picking up all the steps and charging them is a demanding activity causing high operational costs for the operators. A possible solution, applied by many operators, is working with individuals and rewarding them for picking up, charging the steps overnight and distributing them throughout the city the next morning. These individuals are called Chargers. For example charging a step overnight in Brussels yields between e3.50 and e12 minus the electricity costs and the costs of picking up and delivering the steps in the city. (Nuyts & Mertens, 2019)

The main advantage of these kick scooters is the ability to cover small distances at a Chapter 2. Introduction 14 reasonable speed in dense areas where agility is key. But sometimes distances are too large to conveniently cover them while standing up on a kick scooter. For these trips, bikes are more advantageous.

2.2.3 Bike Sharing

As stated in the introduction, bike sharing has been around since the mid 1960’s. Since then, their growth has been overwhelming. It is hard to picture the shared bikes out of the city image. The bike sharing industry has known a fascinating evolution. What once started as a social initiative to provoke the auto-authority is now a fully accepted and integrated means of transportation applied by a large part of inhabitants in major cities. The next paragraphs will walk through the evolution and different generations of bike sharing and their advantages and disadvantages.

First generation Bike Sharing Systems

The first attempt to create a bike sharing system was in Amsterdam. Luud Schimmelpen- nink, member of the communist party ’Provo’, distributed 50 white painted bicycles through- out the city. The bikes were left unlocked. People could freely pick them up, drive them around and leave them at their final destination where the bikes were waiting to be picked up by the next user. At least, that was the plan. In reality, the bikes were thrown in the canal, stolen and moved to nearby villages or picked up by the police. The police started impounding the bikes because they got the message that this was a move to provoke the establishment and make a statement against the growing number of cars in the city centre of Amsterdam.

This first generation was very simple in its nature. No technology, no locking/unlocking the bikes and no fee was being charged. Simply a bike, free to use. This was not an attempt to create a business but rather a symbolic move of the Provo. Even though Mr. Schimmelpenninck made the calculation that it would only cost the city of Amsterdam 10% per person per kilometer of what public transport costs the city per person per kilometer, the city rejected the initiative. (Van der Zee, 2016) The city believed that bicycles belonged to the past and cars had a promising future.

Second generation Bike Sharing Systems

Early in the 1990’s, new bike sharing initiatives popped up in Scandinavian cities. First, small initiatives of around 25 bikes got enrolled in Denmark and later in 1995 a large- Chapter 2. Introduction 15 scaled bike sharing system got launched in Copenhagen, called Bycyklen. Where the first generation were just ordinary bikes used for sharing purposes, this generation had some big modifications to improve convenience and operational efficiency. One of the most important modifications to the bikes were the airless, solid, rubber tires. This cut down the operational costs of changing flat tires significantly. Another modification was in the way the bikes operated. Before, people could freely pick them up. In this system, a coin deposit was needed. Furthermore, the bikes had to be picked up and left behind in fixed stations, spread throughout the city. The first type of Station-Based System was born. In this system there are several stations spread throughout the city and a fixed amount of parking spots are available in every station. There are two different subdivisions. Back-to-one is the first option. The user can pick up a bike at any given station, but needs to return the bike to this same station. This system is the least flexible of the two. The second, more flexible, option is referred to as back-to-many. In this system a customer can pick up a bike in a station and leave it in the station closest to his final destination. Problems and inconveniences arise when the preferred final station is full. There have to be enough stations to meet the demand of the user and make it possible to drive close to the final destination. It is also important to have enough parking spots in every station to ensure a parking spot for every user.

While this system was more thought through and defined than the previous generation, there were still some major drawbacks. The biggest problem this generation faced was theft since the unlocking and use of the bikes was anonymous.

Third generation Bike Sharing Systems

Because of the high rates of bicycle theft in previous generations, an improved way to track customers was needed. The major modification in these third generations is the use of technology to improve convenience and security for the operator. The first, third generation bike sharing system was launched in 1996 at the university of Portsmouth, England. In this campus based initiative, the anonymity disappeared because students needed to swipe a magnetic stripe card to unlock the bike. Technology contributes to the smartening of the bikes providing telecommunications systems, electronically-locking racks and bike locks, mobile phone access and on-board computers. (DeMaio, 2009) Another characteristic of this generation is the advertisements on the wheels. Bike sharing operators added a new dimension to the existing business model by adding revenues of advertisements.

More initiatives popped-up. Every year, one or two programs were introduced but the first Chapter 2. Introduction 16 large-scale project was launched in 2005 in Lyon. V´elo’v, an initiative by JCDecaux, spread 1,500 bikes across Lyon. These bikes were being used on average 6.5 times per day by the 15,000 members of the program. (Henley, 2005) It was only until after the success of V´elib, the Bike Sharing initiative from Paris with 20,000 bikes, that the third generation bike sharing spread in other continents.

Even though technology caused a lot of improvements. There is still one major disadvantage. These third generation systems are also station-based systems, which is disadvantageous for the convenience of the user.

Fourth generation Bike Sharing Systems

This fourth generation added new improvements to increase the convenience of the user. One of the biggest improvements is the fact that they are free floating, not station-based. This means the user can leave the bike practically anywhere. Most companies work with an operational area in which you can leave the bike behind free of charge. When you park the bike outside of this operational zone, you pay a large fee. Free floating systems give the user the highest convenience since it is possible to park at the user’s final destination. Because the bikes are equipped with GPS trackers, the user is able to find nearby bikes on a map in the mobile app. This GPS tracker makes it difficult for thieves to steal the bikes since the location is shared and known at all times.

The first three generations were operating mainly in Europe, contrary to the fourth gener- ation which is primarily booming in Asia. In China alone, there are around 80 domestic bike sharing operators putting approximately 16-18 million bikes on the streets. (Ibold & Nedopil, 2018) Ofo and Mobike are two relatively young start-ups, dominating this Chinese market with combined 50 million rides per day in 2017. The potential of the free float- ing bike sharing systems is reflected by their fast growth and the fact that large Venture Capital funds invest large amounts in to these start-ups. Mobike’s latest valuation is $2.7 Billion and Ofo’s $2 Billion. The dense cities and relatively poor population are a perfect combination for bike sharing systems to boom.

Since this generation is even smarter than the third generation, new opportunities come to light. The business model shifted from generating revenues through advertisement and fees from trips to using and selling the data of the trips. This data is highly valuable for governments and local businesses. Of course, with regard to the new privacy regulations, companies have to be very meticulous and careful with handling this data. Unfortunately, Chapter 2. Introduction 17 there are less rules about privacy in Asian countries such as China than there are in Europe. But it is a general trend that more and more people tend to move away from companies that keep, use or sell their personal data in an non-transparent way. This creates new opportunities for future generations of bike sharing systems.

Future generation Bike Sharing Systems

As a counter move to the fourth generation bike sharing systems that keep and use per- sonal data of the users, a new initiative originated called Fairbike. Dutch designer Marcel Schouwenaar, creative director of The Incredible Machine, invented this new generation of bikes. The idea is to have a decentralised bike sharing system where bikes are not owned by a company, government or an individual. The bikes operate autonomously in a self-sustaining manner. The next paragraph explains how this system would work.

In this system, every bike runs on its own, generating its own income. Every time a user makes a trip, he pays through the app and the bike collects the money. Once a bike needs maintenance, they can be brought to a local repair shop or the local bicycle maker can pick it up. The bikes get repaired in local repair shops supporting the local economy and the repair shops receive the money from the bikes. When a bike earned enough money through collecting fees, a new bike can be bought to participate in the system. This way, the bike sharing system grows organically according to the demand for trips in a city. The higher the demand and the more trips, the more bikes become available in the system. All of this happens transparently and autonomously with the use of an integrated blockchain system. No company would have access to the data and no company would need to overlook the system. The only need is a city investing in the first fleet of bikes as a green public transportation initiative.

This is a move against big capitalistic, non-transparent companies disrespecting the privacy of individuals by abusing their data. In essence, this is the same plan as the white bicycle plan. Maybe this time, it could work because of the technology backing it up. At this moment, this is only a concept and no such a system is operational yet.

2.3 Advantages of Bike Sharing Systems

Bike sharing systems have a lot of advantages and off course also some disadvantages. The next two subsections will cover the most common advantages and disadvantages of modern bike sharing systems. Chapter 2. Introduction 18

2.3.1 Individual Benefits

It is commonly known that riding a bike on a regular basis contributes to an individuals overall health. This is no different when riding a bike in a bike sharing context. The most important health consequences of riding a bike regularly are lower risk of type-2 diabetes, improved fitness, a lower risk of cardiovascular events, a lower risk of abnormal high blood pressure and a lower risk of obesity. (Oja et al., 2011) One could argue that riding a bike more regularly increases the exposure to dangerous emission gasses which are harmful for ones health. A study conducted by De Hartog et al. (2010) proved that the health benefits of riding a bike are greater than the risk of exposure to this air pollution and the increased risk of ending up in a traffic accident.

Today’s public transportation network is very comprehensive. Almost every destination is accessible through a combination of different public transportation methods. However, it is often the case that people need to cover a small distance to get to their final destination or to get to public transport to start their journey. This problem is called The Last Mile Problem, or first mile problem when it concerns the first mile to get to their mode of transportation. This last mile is often seen as a burden to take public transportation and is a reason why people choose a car for their convenience. Public bike sharing systems offer an addition to current public transportation modes enhancing the connectivity between home and work. (Shaheen et al., 2013)

Another advantage is the shorter journey time for individuals. The first or last mile of the journey is covered faster with a bike than by walking. Sener et al. (2009) studied the comparison of journey times of bike sharing systems with journey times of other forms of transport and found that journey times of bike sharing trips are shorter because they are not subject to congestion.

2.3.2 Public Benefits

Public bike sharing does not only benefit individuals but also the society as a whole. The presence of bike sharing systems in society increase the modal share of biking with 1-1.5% after the first year of being operational according to a study by DeMaio (2009).

The arrival of public bike sharing initiatives has a positive impact on the local economy. A study of Buehler et al. (2014) found that 23% of the users reported spending more money in local businesses because they use bike sharing systems. In addition, 20% of the Chapter 2. Introduction 19 local business mentioned a noticeable increase in revenue since the arrival of a bike sharing station nearby their shop. Furthermore, 61% of the businesses reported that they would like to replace a car parking with bike sharing stations in front of their shop.

Bike sharing also has a big impact on the environment. Sælensminde (2004) and Shaheen et al. (2013) found that because of bike sharing, the modal share of vehicles dropped causing a decrease in cost of congestion, cost of pollution and cost of CO2 emission for the society. These are important findings since impact on the environment is one of the motivators for people to use bike sharing. It is also a valuable argument bike sharing operators can use to convince cities to implement more initiatives and people to make more use of those initiatives.

Because of the fact that people tend to cycle more and the air is less polluted since the arrival of bike sharing, the overall public health improved. This makes it possible to reduce the expenditure on public health care without compromising the public health level. (Van Amelsvoort et al., 2006)

A last advantage is found in a study of Boland & Murphy (2012). They investigated the relation between physical activity2 in leisure time and absenteeism at work due to sickness. One of the conclusions was that workers who spend leisure time doing physical activity twice or more a week are less likely to call in sick than workers who do not. They are, on average, 5 days less absent because of sickness. Another finding is the fact that workers who spend time doing physical activities are more productive at work.

2.4 Disadvantages of Bike Sharing Systems

Although Vehicle Sharing Systems could be the solution to pollution, traffic congestion and lead to a healthier lifestyle, it also causes problems and new urban issues. Since China is the biggest player in the world with the fastest growth in terms of members and fleet, they also encounter the most problems. This subsection will cover the most relevant and most prominent problems.

2.4.1 Vandalism and Theft

This was mainly a problem in the first generations of the bike sharing systems. Anonymity of the user led to a lower barrier to exhibit unethical behaviour. Since users only had to

2Riding a bike of a bike sharing system can be considered physical activity during leisure time. Chapter 2. Introduction 20 put a coin in a bike in order to use it, they could easily steal the bike and keep it for private purposes.

The use of smart bikes and smart technologies significantly reduced this risk. Users have to register and make a profile on a mobile app and link their credit card. This personal profile made it a lot more difficult to steal bikes anonymously. Vandalism became more difficult since users can report any damage to the bikes or cases of violated rules3. The reporting of vandalism gives the reporter a reward and the offender of the misconduct penalty points. Too many penalty points will lead to exclusion from the service.

Even though smarter bikes are less likely to be stolen, a Parisian bike sharing operator with a fleet of 20,000 bikes had to replace thousands of bikes every year, due to theft and vandalism, at a cost of 3 to 6 million euros per year. (Midgley, 2009) This same operator reported in a survey that in the first two years after the launch, 7,800 bicycles got stolen and another 11,600 got vandalised. (Shaheen & Guzman, 2011)

It just so happens to be one of the reasons why people tend to use bike sharing over using a private bike. People who are trying to avoid getting their bike stolen make use of bike sharing alternatives. (Fuller et al., 2011)

2.4.2 Bad Parking Behaviour

While theft and vandalism occurred more with the first generations of bike sharing, this problem is specific to the last generation. Since this generation is dock-less, users are free to park wherever they want to. Operators set guidelines and parking rules and hope users will obey. But practice does not always follow the theory. Bikes are dumped literally everywhere. The streets of China are flooded with bikes, metro stations are hard to enter because of bikes blocking the entrance and sidewalks are often turned in to parking spaces for bikes. In some public parks in China, where it is forbidden to park bikes, users leave their shared bike. Security throws the bikes over the fences of the park causing huge piles of bikes from different operators to form next to the park. (Cf. Figure 2.4.2) This leads to operational costs to disentangle, collect and redistribute the bikes throughout the city.

3Violated rules concerns violated parking rules, leaving bikes unlocked, locking bikes with private lock, moving bikes illegally and unethical behaviour. Chapter 2. Introduction 21

Figure 2.4: China: Pile of Bikes.

A solution to this problem could be the introduction of Electric Fences. These are geo- fences incorporated in the app that show you where you are able to park the bike. When the GPS location of the bike is not within an electric fence, it is impossible to lock the bike and you will be charged as long as the bike is not locked properly. Or you can lock the bike outside of these fences at a higher cost. These fences can be set anywhere in the city according to the preference of the operator or the city. A case study in Shanghai proved these geo-fences to be efficient. When the number of electric fences in Shanghai is set to 7,500 it can cover 91.8% of total parking demand and is it possible to accurately determine the best and most efficient locations to set up these fences. (Zhang et al., 2019)

This issue goes hand in hand with the issue of Overcapacity. Chinese start-ups backed by large venture capital funds know that the key is to be able to meet the demand by providing enough bikes at the users disposal. If a user needs a bike but cannot find one within a convenient range, they might loose a client to a competitor. This leads to an overcapacity of bikes in the large Chinese cities because of the oversized fleet of the operators. Streets are packed with bikes, parking lots are flooded with unused or broken bikes and the streets become an unorganised mess because of this overcapacity. Chapter 2. Introduction 22

2.4.3 Lack of Infrastructure

China, once referred to as the Bicycle Kingdom, is trying to honor its name and is investing on a large scale in bike sharing initiatives. However, the Chinese biking infrastructure is lagging behind on two areas.

The first issue China has with its biking infrastructure is the shortage of safe Bike Lanes. One might argue that in cities where public bike sharing initiatives are implemented, bike infrastructure is significantly better than in cities where this is not the case. Assuming bike sharing initiatives increase the quality of biking infrastructure in these cities is simply a wrong cause-effect interpretation. Cities where bike infrastructure is better than average are usually chosen to start a public bike sharing initiative. Public bike programs do not improve the condition of existing bike lanes and neither do they increase the amount of bike lanes in the city. (Yang et al., 2015)

A second issue China has is the lack of sufficient Parking Facilities. A perfect example is the Xicheng district in China’s capital Bejing. Only 24% of the streets have bike parking facilities. (PAN et al., 2010) It is very important for cities to have proper bike parking facilities. A survey conducted in Shenzen in 2010 revealed that the lack of biking facilities is for 60% of the respondents a reason to not use biking as a transportation method. (Zheng & Planningamp, 2014)

2.4.4 Load Balancing

The most prominent problem bike sharing systems have is the rebalancing of their fleet. This problem is mostly caused by unpredictable, asymmetric demand, differences in altitude throughout the city, weather conditions, time of the day and day of the week and events in the city. (Singla et al., 2015; Fricker & Gast, 2016) A good example of the differences in altitude is Montmartre Hill in Paris. Tourists use the bikes to go downhill but rarely do the efforts to drive uphill. In most of the cases, the solution to this problem is using a fleet of trucks or vans to pick up the scattered fleet and redistribute them all over the city.

Using trucks to rebalance fleet is biggest contributor to CO2 emissions of a bike sharing system. (Council, 2013) This is not beneficial for the image of bike sharing systems which are praised for their sustainability.

Rebalancing problems in Station-Based Systems occur because of the unbalanced supply and demand at the fixed parking stations. For the system to be convenient, a user has to be able to pick up or park a bike at the preferred station. Because it is hard to predict customer Chapter 2. Introduction 23 behaviour and driving patterns, there can be either Starvation4 making it impossible for a user to pick up a bike at the preferred station, or Congestion5 making it impossible for users to leave their bike. Customer satisfaction decreases when there is starvation or congestion at the station resulting in a loss of customer demand and revenues. (Ghosh et al., 2017) A bike sharing operator needs to make sure that the supply of a station can at least meet peak demand during peak times. V´elib,the Parisian bike sharing system, has 23 trucks with a capacity of 20 bikes to redistribute their fleet of 20,000 bikes over 1,200 stations. (Nair & Miller-Hooks, 2011) This usage of trucks is a large operational costs as well as a large environmental cost. Most of the time, these trucks operate during the night in the static case. Static repositioning means repositioning when there is almost no demand for the bikes. This is mostly at night. When the repositioning happens during this time, the bikes are not moving. This makes it a lot easier since they do not have to take into account the movement of bikes during this repositioning. The opposite of static repositioning is dynamic repositioning in which the movement of bikes during the repositioning process is taken in to account. This makes the process a lot harder because the following scenario could happen. The operator picks up some bikes and maps a route with different stations to drop these bikes but while they drive to the empty station, bikes also drive to this station making it full before the truck arrives.

Even when the station gets restocked during the night there are cases of congestion and starvation during the day. Ghosh et al. (2017) found that in 2014, in around 40% of the instances this was the case for more than 30 minutes. A lot of studies focus on the strategic planning6 and operational planning7 both in a static or dynamic context. The same Ghosh et al. (2017) conducted a study in which they compared three different rebalancing systems. They compared a dynamic repositioning system, a static repositioning system and an online approach using myopic heuristics. In the online case, sensors in the bikes and stations keep track of the fill rate of a station and based on these signals, trucks refill the stations. Two different cases were distinguished. The first cased took one day as a time range for the investigation, the other case considered only peak moments. Both cases were conducted on two different bike sharing systems. In all cases the dynamic approach was the most efficient one in terms of revenue increase and decrease of lost customer demand. Compared to a static approach, the average profit gain over a full day is 2.81% and during peak period

4Starvation is the case in which there is a lack of bike supply at the station. 5Congestion is the opposite of starvation and indicates an oversupply at a certain station. 6Strategic planning refers to optimal fleet size, location of the stations etc. 7Operational planning refers to fleet repositioning etc. Chapter 2. Introduction 24

6.48% while the decrease in lost demand for a full day is 34.07% and during peak period 36.30%. If the results are compared with the online heuristic approach they found that the gain in profit during a full day is on average 2.21% and in the peak period 4.01%. The decrease in lost demand for a full day is on average 25.54% and during peak hours 24.94%. Those results show that a dynamic approach is a lot better both in terms of revenue and customer satisfaction.

The problem is slightly different when talking about Free Floating Systems. There is no possible case of starvation or congestion since there are no fixed stations but similar problems arise. The problems these systems have is bikes being parked in places where they are unlikely to be picked up by a new user. In these cases, trucks drive through the city to pick up and redistribute the abandoned bikes. The process is more labour-intense than in fixed station systems since the bikes are more scattered throughout the city. In most cases, free floating systems work with an operational zone in which the bikes have to be parked. If a user parks the bike out of this operational zone an extra fee is charged. This is to keep the bikes in the area where they are most likely to be picked up again or where it is easier to collect with them a truck.

A recent trend within bike sharing are so-called Hybrid Bike sharing Systems. Those systems are a combination of free floating and station-based. The user can park the bike anywhere within the operational zone at the cost of a fee. The difference with free floating is that there are also parking zones within the operational zone. It is free to leave the bike in one of those parking zones. These zones are strategically placed at places where demand is usually higher.

The redistribution of bikes comes at large operational costs and efforts. A report of the New York City Department of City Planning shows that the average operational cost for a bike per year is around $1,600. System operations account for the largest part of this cost and include rebalancing of the fleet, maintenance, IT and customer service operations. (Haider et al., 2018) For the French station-based operator V´elib,the cost to reposition one bike from one station to another costs on average $3 according to a study of DeMaio (2009). A more precise and effective way of forecasting bike demand and supply at certain stations could mean a significant cost reduction. A lot of studies focus on optimizing the number of bikes and stations and their locations as well as their static pricing systems. (Katzev, 2003; Shaheen & Cohen, 2007; DeMaio & Gifford, 2004; DeMaio, 2009) Other papers focus more on building optimization algorithms to improve the redistribution during the day or night and minimise the distance the trucks traveled. (Nair & Miller-Hooks, 2011) Besides Chapter 2. Introduction 25 solving this problem by optimizing the system, a solution could be to incentivise the users and implement a dynamic pricing system to lower operational cost and emission gasses and increase revenues. The next section focuses on different pricing systems in bike sharing systems.

2.5 Pricing

A good pricing strategy is essential for every business to be profitable. But just as in other businesses, it is very hard and difficult to set the right prices in the bike sharing industry. In general there are three different possible ways of setting your price, in theory those are referred to as the 3 C’s. The first possibility is Cost-Plus Pricing. In this technique, you calculate the total cost of the products or services you offer based on a forecasted volume. This determines the cost per unit on which a certain profit margin is added to get the target price. The second possibility is Customer Driven Pricing. This strategy tries to find out what the exact perceived value is in the minds of the customer. Once the willingness to pay of the customer is determined, this price can be asked as long as this price exceeds the costs. The third option is Competition Based Pricing. This is the simplest form of all three strategies. You simply look at what the competition is charging and ask a price roughly the same as theirs. All three strategies have their advantages and disadvantages.

It is very important for a good pricing strategy to be value driven. Only when the price reflects the value a customer assigns to a product, the customer will be happy to pay and a healthy relationship with the customer is built. Off course, this entails some difficulties. Firstly, it is very hard to figure out exactly what your customers value and what the willingness to pay of these customer is. There are several techniques that help with findings this willingness to pay. Secondly, once you know the willingness to pay, it is still difficult to charge different prices to different customers. One possibility for bike sharing systems is to figure out how the willingness to pay changes at different moments in time or on different places and charge prices accordingly. This way, the bike sharing operator can capture more value than with a regular pricing strategy.

The most common price metric in bike sharing systems is a price per unit. In this case, the unit is a fixed time period. For example, Vilo! from Brussels charges per thirty minutes, Mobit from Belgium charges per twenty minutes, Ofo from China charges per fifteen minutes and Mobike from China charges per twenty minutes. This price structure is perfect for commodity products. It is really difficult to differentiate the price and ask different prices Chapter 2. Introduction 26 to different customers with different willingness to pay. Put different prices could be charged when the value of a bike is lower in general. For example when a bike is left behind in a very obsolete area, this bike is in general less valuable than a bike in a crowded place. It would be possible to lower the price of these bikes in order to compensate for the lower value they offer.

The aspect of fluctuating values accompanied by fluctuating prices raises the topic of static and dynamic pricing systems. These two systems will be discussed in the following para- graphs.

2.5.1 Static Pricing

A static pricing system means charging a fixed rate or fee per unit. This is mostly the case in bike sharing systems. An extra dimension is added by leveling the static prices. The longer the user uses a bike, the more expensive per unit it gets. This is in contrast with normal volume discounts. Almost every product, especially commodities, have volume discounts. This means the more you buy of a product or service the cheaper it gets per unit. A possible explanation could be that this is to increase the flexibility of the fleet. If people use the bike only for short distances, the bike remains in the system. When people occupy the bikes for a longer duration, less bikes are available in the system. Thus increasing the price to demotivate users to take long trip can increase the availability of the bikes and thus the flexibility of the system.

2.5.2 Dynamic Pricing

Dynamic pricing is different from static pricing in the sense that it does change over time. According to the demand in the system, prices decrease or increase in order to maximise revenues. The lower the demand, the lower the price to stimulate this demand. The higher the demand, the higher the price to maximise the revenues. Dynamic pricing could also be used to rebalance the fleet over the system. If some locations suffer from congestion, prices of trips to these locations should increase while prices from these locations should decrease. On the contrary, where there is starvation, prices should decrease for trips going to this location and increase for trips leaving this location until the system is more or less balanced to meet future demands. Algorithms could be developed taking in to account all different variables and future demand. Not only could a dynamic pricing system react to changes in the occupation at different stations or locations. It could also change the prices according to what time of the day it is or what day of the week. If bikes are more used during morning Chapter 2. Introduction 27 peak hours, the bike sharing operators could charge higher rates to optimise revenue and spread demand more evenly over the day.

The last decades there has been an increase in the importance of technology in different business models. These new technologies facilitate the implementation of dynamic pricing systems for commodity products. Complex algorithms made to increase the revenue and manage demand and supply through dynamic pricing can easily be implemented. These technologies not only help companies but also makes it possible for customers to get real- time information on the status of a service, for example the exact price they have to pay. Uber, the ride hailing app, is a perfect example of how the use of technology makes it possible to implement a dynamic pricing system in to the business model. Uber for example implements route pricing8 as well as search pricing9. Drivers get a map and see where demand for trips is high. The prices are the highest in these areas and thus drivers can earn the most per trip while working in these areas. Bike sharing systems should strive towards the implementation of dynamic pricing systems in their models.

A more comprehensible and transparent way to implement this dynamic component in the price is using incentive schemes.

Incentive Schemes

A lot of companies have been using incentive schemes as means to incentivise users to relocate bikes that have been left unused for a long period of time. The aim is to make bike sharing systems operate fully autonomous and self-sustainable in terms of re-disribution. Incentives could be discounts on trips, lines of credit for future trips, money rewards etc.

For the user, there is always a trade-off between the offered incentive and the additional travel time and distance the user has to cover to receive the incentive. Question is if the incentive is large enough to persuade the users to drive the additional kilometers. Pfrommer et al. (2014) investigated how a public bike sharing system could integrate both an incentive scheme for users together with a classic system for the repositioning trucks. In this case, the incentive is a monetary payment which can be used for later trips, a sort of credit. The study showed that for the London Public Bike Sharing System, incentives alone were enough to keep the service level at 87% during the weekends without using additional staff to help with the redistribution. However, during the weekdays, additional staff was required

8Route pricing takes in to account the predicted route a driver has to take to determine the price. 9Uber defines search pricing as the cost to match supply and demand. Chapter 2. Introduction 28 since most people use the bike sharing system to commute to work and incentives are not enough to persuade users to drive to a further location.

Capital Bikeshare, the regional bike sharing system of Washington D.C., used a different kind of incentive. They organised a competition called Reverse Rider Rewards which was active under the slogan ”Don’t go with the flow this summer.” Although they partner up with a company that helps with the constant redistribution of bikes over the different stations, there are still full or empty stations during morning rush hour on weekdays. They tried to incentivise users to help redistribute the bikes alongside the partner company. When participants drive a bike from stations where there is usually congestion10 to stations where there is usually starvation11 during rush hour, they earn one point and one entry in a lottery. At the end of the competition, the user with the most points gets a free one year extension of his/her membership. Users who end in the top ten get a one month extension and five lucky participants of the lottery also get a one month extension of the membership. (Lisle & Eatough, 2010) It is impossible to find any data on the effectiveness of this initiative but Capital Bikeshare is still working with the reward system and even added some new dimensions. Currently they work with an incentive program in which the user can choose to become a Bike Angel. On the website they say bike angels are users that help with the improvement of the availability of bikes for other users and earn points for this. These points can be exchanged for different rewards. The fact that their old Reverse Reward System is still in place and even more comprehensive, probably means that it is an effective way of dealing with the rebalancing problem.

10Black station in figure 2.5 11Yellow station in figure 2.5 Chapter 2. Introduction 29

Figure 2.5: Map: Typically full or empty stations in Washington D.C.

Patel et al. (2018) studied the effect of offered discounts for trips on the level of balance of the system. The better the system is balanced the more customer there can be served and the higher customer satisfaction is. Therefore, customer churn will be lower and total revenues will increase. Thus, the goal is to maximise this level of balance. However, there is always a trade-off between increase in customers served and decrease in marginal profit per trip due to offered discounts. Offering too many incentives decreases total profits. The study also showed that the higher the discount rate, the lower the probability of accepting the incentives has to be to maximise total profits. Chapter 2. Introduction 30

Because the use of incentive schemes as motivation for users to relocate bikes is a rather recent trend, not a lot of literature is available. The literature that is available mostly focuses on station-based systems. This dissertation will research an incentive scheme in a free floating system. Chapter 2. Introduction 31

2.6 Hypothesis

The focus of this master dissertation is mostly on the effect of incentive schemes, as a form of dynamic pricing, on the fleet rebalancing problem of free floating operators. The main hypothesis of this master dissertation is:

1. The use of incentive schemes leads to less trucks being used for the redistri- bution of bikes throughout the city.

Not only this hypothesis will be examined. Other trends will be investigated in order to give some rudimentary guidelines to set up a dynamic pricing system. Some of the trends are:

1. Peak time: Bikes are used more during the week than during the weekend.

2. Distance from centre: The further away from the city centre a bike is parked, the higher the chance it is left on a place where it is less likely to be picked up by a user.

If we can formulate an answer to these questions or related questions, we can give some guidelines to bike sharing operators on how to set their pricing. The findings from the data will reflect the values customers assign to certain characteristics of trips.

It is important to note that this research is a preparatory paper for further research. This research aims to prove incentives schemes are effective. Future research could conduct a conjoint analysis12 in which the optimal incentive users want in order to use the abandoned bike is sought-after. After the conjoint analysis, a revenue optimising pricing strategy can be defined in which a dynamic pricing algorithm is made.

12A conjoint analysis is a market research technique in which a survey is used to examine customer preferences. Chapter 3

Methodology

After an extensive exploratory research of the literature, more conclusive research will be conducted. In this section, the applied methodology to investigate the research question and test the hypothesis is explained. First, the research design is motivated followed by the research methods. The end of this section gives a critical reflection on the validity and reliability of this research.

As mentioned in section 2.6, the objective of this research is trying to find evidence that incentive schemes work as a means to rebalance the fleet.

3.1 Research Design

In order to investigate whether the predetermined hypothesis stated in section 2.6 is true or false, a Case Study will be conducted. A case study is an exploratory research in which one problem is described as fully as possible with the help of different sources. It is a multi modal research design. (De Pelsmacker & Van Kenhove, 2015) The case study will include two different sources. On the one hand there is a qualitative part and on the other hand we have a quantitative part part. The combination of the two different parts will lead to better and more complete insights into the problem.

The qualitative part is of an exploratory nature. The goal of the exploratory design is to observe and describe the problem in a more detailed way by talking to an expert in the field. With this interview we expect to gain insights not found in the literature. This part is designed to give an answer to the ’what’, ’why’ and ’how’ questions. Answers to these questions help formulating the a posteriori hypothesis. They are not sufficient to approve

32 Chapter 3. Methodology 33 or disapprove a hypothesis. Using the findings of this qualitative part are helpful to define a scope and a direction in which the quantitative data will be explored. They will give indications on what to look for in the data.

The design of the quantitative part consists of two sub-parts. Firstly there is a descriptive part, explaining the characteristics of the data. This part looks if there are any odd trends or observation. The second and more important part is a conclusive part. In this part, the quantitative data will be used to test the a priori postulated hypothesis.

To conduct this case study, a company active in the bike sharing industry had to be found that was willing to collaborate. After making a list of the minimal company requirements, a shortlist of candidates was made. These candidates were contacted and a good match was found. This research paper is working closely together with the Belgian company Mobit that was willing to participate in the qualitative research and provide the necessary quantitative data. The next section is a short introduction about Mobit.

3.1.1 Mobit

Mobit is a Belgian company incorporated in May 2017 by 3 founders. But the idea of starting a bike sharing company in Belgium dates from November 2016. The company is mainly operational in Belgium in 11 cities. They have around 1,500 bikes registered. The average duration of a trip is 12 minutes and the usage rate of a bike is approximately 0.3 rides per bike, per day. This means there is some room for improvement and their goal is to increase this usage rate. Bluebike is at the moment their biggest competitor in Belgium and with the introduction of Jump, the bike sharing system of Uber, a new competitor recently entered the Belgian market. Mobit’s focus is mostly on offering a solution to the first and last mile problem. They do not try to be a replacement for regular bike ownership. Their competitive advantage is a reliable technology with a user-friendly interface and an easy to use and convenient renting process. After downloading the app, renting a bike with Mobit only takes three steps.

1. Scan the QR-code on the bike with the in-app camera. The lock opens automatically and the user can start the trip.

2. Drive around to any location you wish.

3. Park the bike on a legitimate location and the app automatically stops the trip. No need to open the app a second time to lock the bike. Chapter 3. Methodology 34

Step three makes it a lot more convenient for users since they do not have to take their cellphone and open the app a second time. With most competitors this is still a requirement.

In the app you get an overview of the distance of the trip, the amount of CO2-emissions saved compared to using a car, the number of calories burned, the route and the cost price of the trip. The transparency within the app is very benificial for the user satisfaction.

Currently, Mobit is in the start-up phase and is experimenting with a lot of different vari- ables. One of those variables is the flexibility of the system. Most cities operate as free floating systems but 2 of them have a station-based system. These station-based systems are back-to-many. Results of the experiments show that the usage rate of back-to-many is higher than with free floating systems and thus they are planning to change more systems to station-based systems. Ideally, a hybrid system would be implemented. Mobit is currently also experimenting with these hybrid systems.

Another variable with which they try to experiment to find the optimal value is their pricing. The next section zooms in deeper on Mobit’s pricing strategy.

Pricing Strategy

Mobit currently works with a static pricing system with several levels. This means Mobit charges a fee per twenty minutes at a fixed rate per hour. This rate increases the longer the trip takes. Table 3.1 contains an overview of the fixed rates.

Duration Price/20min <4 min e0,29 1th hour e0,45 2nd hour e0,65 3rd hour e0,80 >3 hours e1,00

Table 3.1: Tariffs per 20 min.

In section 2.5 we discussed the different possibilities for setting a price. The in-depth interview learned us that the pricing strategy of Mobit is a competition based strategy. They simply look at what the competition is charging and try to stay within a competitive range. Chapter 3. Methodology 35

Incentive Scheme

In addition to their static pricing system they launched an incentive program on the first of October 2018. Some bikes turn into bonus bikes after 7 days of not being used. There are three different ways of earning bonuses. The first bonus is collected when you use a bonus bike within the operational zone. The reward you get is one coupon1. The second bonus is collected when you bring a bonus bike back to the operational zone from a location outside this zone. For this operation, you get three coupons. The third option is only applicable in the hybrid systems. The user receives three coupons when parking a bonus bike in a Mobit parking location.

At this moment in time, no research has been done on the optimal level of incentives. The number of free coupons is chosen arbitrarily.

3.2 Research Methods

For this case study, both a qualitative and a quantitative research were conducted. The next paragraphs will elaborate more on how both parts were executed.

3.2.1 Qualitative Research

At first, a qualitative research is being conducted to better understand the dynamics in the industry as well as the perception on the effectiveness of incentive schemes as a means to solve the fleet rebalancing problem. This part is a good basis to further investigate the research question in the quantitative part. Trends or phenomena that are difficult to account for in the quantitative part may come to light during this part of the research.

For this qualitative part, an In-Depth Interview is being conducted with the founder of Mobit. An in-depth interview is an unstructured interview in which the interviewer asks open-ended questions on which he can elaborate further if it could provide meaningful insights. (De Pelsmacker & Van Kenhove, 2015) An in-depth interview is used within the pragmatic part2 of qualitative research. This interview will determine the boundaries of the fleet rebalancing problem as well as outline the context of the problem within a bike sharing operator’s daily operations. Questions about the current initiatives on solving the

1A coupon can be used for a free trip up to 20 minutes. 2Pragmatic research is used as a preparatory phase before quantitative research. Chapter 3. Methodology 36

fleet rebalancing problem are being asked as well as questions on their effectiveness. To conduct a correct in-depth interview, the following steps need to be taken:

1. Developing a sampling strategy.

2. Writing an in-depth interview guide.

3. Conducting the interview.

4. Analyzing the data.

The following paragraphs will explain every step more in detail.

Sampling Strategy

Normally, in order to get reliable results, several in-depth interviews need to be conducted with different respondents. But because the goal of the in-depth interview in this disserta- tion is not to obtain a definite answer on the research question but rather to give guidance and direction for the quantitative research, it is sufficient to interview only one expert in the field. Since the interview is conducted in the context of a case study it could provide enough meaningful information.

Purposive sampling3, more specifically judgement sampling, was chosen over random sam- pling. This because it is the most convenient way of finding representatives with the right characteristics. The characteristics of the respondents were the following:

1. Experience with bike sharing systems for at least one year.

2. Working in, or having worked at a bike sharing system on an executive level.

3. The bike sharing systems has to be, partly, free floating.

Ideally, an expert in the field of bike sharing systems who is living in Belgium is willing to take the interview. Since Mobit is collaborating with us for this research, the co-founder was asked to doing the in-depth interview.

We are aware that judgement sampling is prone to a selection error, also called researcher bias. This is an error due to the fact that not the whole population but a selection of this

3Purposive sampling is a non-probabilistic sampling method in which respondents, based on the objective of the study and the characteristics of the population, are selected. Chapter 3. Methodology 37 population is being interviewed. Making an estimate of the size of the selection error is impossible since it is a non-probabilistic sampling method. But we can state that this error is minimised by setting up clear criteria the candidates need to meet. Another downside of this in-depth interview is its low reliability. It is important that this research part is complemented with a more conclusive research.

Interview Guide

In order to conduct a smooth and fluent interview, an interview guide needs to be drafted. This interview guide contains most of the questions that will be aksked during the interview. It gives the interviewer a guideline which will guide him through the interview. Off course, additional questions and probing questions4 can be asked when the interviewer wants to dive deeper into a topic. During the interview, three different types of questions were asked in the following order.

1. Introductory questions

2. Serious questions

3. Delicate questions

This order of the questions is chosen to optimise the willingness to reply. If the delicate questions would have been asked first, the respondent could be hesitant to answer them because they could appear to be too direct. Once the respondent answered the introductory questions, the ice is broken and the respondent may be more willing to answer the more difficult questions. During the introductory part, the questions are closed-ended and very basic. They are asked to obtain straightforward information and facts about the company and its history. The answers to these questions can be compared with facts from the quantitative data. If these answers match, chances are higher that the rest of the answers are reliable as well. The serious questions are more important to outline the context of the rebalancing problem. Answers to these questions give us a lot of information, context and the perception of the founder about the problem at hand. These are the answers that can give us a direction in which we can use the quantitative data. In this part, questions are open-ended and supplemented with probing questions. In the last section, delicate questions

4Probing questions are follow-up questions asked to get more specific information about the given answer or when the answer is vague or unclear Chapter 3. Methodology 38 are being asked. These questions cover sensible company information and are not really necessary to test the hypothesis but could also give us some indication about the solution of the problem.

1. Introductory questions These questions are asked to gain more factual information about the company, Mobit, itself.

1. When was Mobit founded?

2. How many number of bikes are currently registered in the system?

3. In how many cities is Mobit fully operational?

4. What are the average number of trips per month?

5. Are there many competitors in Belgium?

6. Who are/is the biggest competitor(s)?

7. How did you came up with the current pricing system of Mobit?

8. Follow-up: Why is it a trip becomes more expensive the longer it takes?

2. Serious questions These questions will help with finding answers and clarify the problem at hand.

1. Why do you think people use bike sharing systems?

2. How do you think people choose what operator to ride with?

3. Why do they choose Mobit over the competition?

4. How is the rebalancing problem solved today?

5. Has it always been like this?

6. Follow-up: Do you notice a decrease in trucks being used since the introduction of the bonus bikes?

3. Delicate questions These are questions that may be difficult to get an answer to because they are possibly delicate to ask. Chapter 3. Methodology 39

1. Are the bonus bikes brought to life to lower the impact on the environment of the trucks or to lower the operational costs and operational efforts?

2. Why do you think 1 free trip is enough to incentivise the users?

3. Is the use of bonus bikes increasing the revenue?

The actual interview can differ slightly from this interview guide because of the answers given by the interviewee.

The Interview

To obtain the qualitative data, a face-to-face meeting was arranged to conduct the in-depth interview. This interview took place on Monday the 13th of May in the offices of Mobit in Ghent. The duration of the interview was 27’54”. A fully typed out version of the interview is provided in Appendix A.1.

Data Analysis

In this step, the data is being analysed and interpreted. The analysis of the qualitative data mostly relies on the impressions of the researcher. The technique used in this thesis is Recursive abstraction. This means the interview will be written down in full and different categories will be made in which the data will be summarised. This categorised summary will make it easier to notice patterns or important findings.

3.2.2 Quantitative Research

In addition to this qualitative research, a quantitative research is conducted as well. This quantitative research shows the facts that are hidden in the data of the bike sharing operator. In this part, there will be searched for trends in the data and check if the hypothesis should be rejected or accepted. This data is also used to verify the findings from the qualitative research. Some possible trends we will be looking for are the following:

1. More bikes are being used during weekdays than in the weekend.

2. The further away from the city centre, the more likely a bike is to be picked up by a truck.

3. Incentive schemes lead to less trucks being picked up by trucks. Chapter 3. Methodology 40

The bikes being left unused mostly require a truck to pick them up and redistribute them throughout the city. It would be useful to integrate the trends into the pricing strategy and pricing algorithm in order to optimise the revenue and minimise the social and envi- ronmental cost by giving incentives for bikes at the least interesting5 places during the least interesting times.

Another goal of the quantitative research is to check whether or not an incentive schemes works. Optimally, the data has data points before and after the introduction of an incentive program. If so, it would be possible to investigate if more bikes are picked up by users instead of trucks because of the incentive scheme.

The following sections will cover the collection of the data, the preparation of the data and data analysis.

Data Collection

For the quantitative part of this research, a raw data file was provided by Mobit. The raw data file is a .txt-file which was send via e-mail as a ZIP document. The file has a size of 21,1MB and contains data from 01/01/2018 until 17/04/2019. The dataset consists of individual trips of the bikes in Belgium. Since the introduction of the incentive scheme was on 01/10/2018, the data file has data from before and after the introduction of this incentive scheme. The following paragraphs cover the data preparation process which involves reading in the data, data exploration, cleaning the data and variable creation. These steps will be executed in a cloud-based web application called Jupyter Notebooks. The programming language used is Python.

Data Preparation

This is a crucial part in order to correctly analyse the data and interpret the results. The better the quality of the data and the dataset, the more accurate the analysis will be. For the analysis, some changes had to be made to the provided dataset.

A first look at the raw data shows that the first line contains the variable names and each variable is separated by a comma. There are thirteen different variables and thus there should be thirteen different columns. Every row represents one instance of a single trip of a bike. 5Least interesting in the sense of being most likely to be left unused for a long period of time. Chapter 3. Methodology 41

Step 1: Reading in the Data

Reading in the data is the first step in the data preparation process. The data of the .txt-file is put in a pandas dataframe. Figure 3.1 and figure 3.2 show the difference between the first ten lines of the raw .txt-file and the first ten lines after reading in the data in a pandas dataframe. As mentioned before, the headers contain the variable names and every row is an instance of a trip made by one bike. The columns contain the variables attributed to an individual trip.

Figure 3.1: First 10 lines of the raw data from the .txt-file.

Figure 3.2: First 10 lines of the structured dataset. Chapter 3. Methodology 42

Step 2: Data exploration

After reading in the data, we have to become familiar with the different variables. There are thirteen variables with information about a single trip. The different variables are:

1. id: a unique id for every single trip in the dataset

2. city name: the city in which the trip was taken

3. bike id: the id of the bike that was used for this trip

4. user id: the id of the user who made the trip

5. distance: the distance covered during that particular trip (in meters)

6. duration: the duration of the trip (in minutes)

7. start time: the start-date and -time on which the trip was taken

8. start lat: the latitude variable of the GPS coordinates of the start location

9. start lon: the longitude variable of the GPS coordinates of the start location

10. end time: the end-date and -time on which the trip was taken

11. end lat: the latitude variable of the GPS coordinates of the end location

12. end lon: the longitude variable of the GPS coordinates of the end location

13. gps str: an empty string

We noticed different notations for different instances of the variable city name. Some in- stances contain values starting with ’City-’ followed by the name of the city and ending with ’ OM’. Other variables contain the city name in capital letters.

The user id variable holds a number as unique user id. No other information of the user has been handed over. All privacy of the user is protected and no privacy rules were violated since no connection can be made with real names of individuals. The data file and analysis ensure full anonymity of the users.

The variable distance displays the total distance the bike drove during that particular trip. This is not the distance between the start and end location. It could be that a bike ends Chapter 3. Methodology 43 on the same location but drove a certain amount of kilometers. That is why this variable should almost never be equal to 0. Only in cases where the user unlocked the bike but did not use it.

The data file contains two time variables start time and end time. The variable start time contains the start time and date of the trip and the end time the end time and date. The data file contains data off more than one year and should be sufficiently large to conduct a significant analysis on and derive conclusions concerning the postulated hypothesis.

The combination of the variables start lat and start lon give the exact GPS location of where the bikes has been picked up by the user. The same goes for the drop-off location with the variables end lat and end lon. These variables will be very helpful in order to know if the bike has been picked up by a user or by a redistribution truck.

The last variable, gps str is an empty string containing only NaN values.

There are 158,119 instances in the original data file. We notice some variables have missing values, mostly in the GPS coordinates. This can be due to bad connection of the receptors of the bike. These variables have NaN values. Table 3.2 contains values on the number of unique bikes in the system and unique users that used the shared bike system as well as the amount of cities in the dataset. We can check if this number matches the information obtained in the qualitative in-depth interview. More descriptive information of the data will be explored in section 4.2.

Variable unique counts bike id 1472 user id 6070 city name 74

Table 3.2: Number of unique counts.

We notice a large amount of cities. This is very unlikely to be correct. A quick look at the data shows that some city variables have incorrect data and others have correct variables but different notations for the same cities. This will be corrected in the data cleaning section.

Table 3.3 contains the different data types of the variables in the original dataframe. Later we will check if there need to be any modifications with the data types. Chapter 3. Methodology 44

Variable Data Type id int64 city name object bike id float64 user id int64 distance int64 duration int64 start time object start lat float64 start lon float64 end time object end lat float64 end lon float64 gps str object

Table 3.3: Original data types of different variables.

Table 3.4 contains information on the amount of missing values of every variable before modification of the dataset. Chapter 3. Methodology 45

Variable NaN count id 0 city name 1643 bike id 1 user id 0 distance 0 duration 0 start time 0 start lat 1784 start lon 1784 end time 0 end lat 11440 end lon 11440 gps str 156823

Table 3.4: Number of missing values per variable.

Step 3: Cleaning the data

In order to analyse the data in a correct manner, the dataset needs to be cleaned. The following steps will be undertaken to clean this data:

1. Delete spaces at beginning and ending of variables.

2. Modify variables that are not consistent throughout the dataset.

3. Change data types of variables where needed.

4. Delete outliers.

5. Delete unnecessary variables.

6. Delete instances with incorrect values.

7. Drop instances with NaN-values.

The variable city name needs to be modified since two different notations of the variables are being used throughout the dataset. For example the city ’Mechelen’ is sometimes noted Chapter 3. Methodology 46 as ’City-Mechelen OM’ and sometimes as ’MECHELEN’. For the sake of simplicity we will modify all variables so they only contain the name of the city in lower cases. In the example the variable would be transformed to ’mechelen’. Some variables contain wrong values. These variables need to be modified or deleted.

The data type of the variable bike id is a float64 in the original dataset. Because of this, it is impossible to distinguish different bike id’s. Changing the type to int64 makes it possible to display all ten digits of the bike id. The data types of the variables that contain time- values, start time and end time, are object-types. These variables are changed to variables of the type datetime.

Variable Data Type id int64 city name object bike id int64 user id int64 distance int64 duration int64 start time datetime64 start lat float64 start lon float64 end time datetime64 end lat float64 end lon float64 gps str object

Table 3.5: Modified data types of different variables.

For some variables, it is possible that the dataset contains outliers. Especially the variables distance and duration are prone to miscalculations and thus having outliers. We need to remove these outliers because they could affect the results unjustly, skew the data and have a significant impact on the mean and standard deviation. The outliers of newly created variables will be deleted as well.

The variable gps str is being deleted since it only contains NaN-values. This variable has no added value to the dataset. Chapter 3. Methodology 47

We noticed some instances contained incorrect, illogical, impossible or impractical values for certain variables. The variable user id contained negative values. The variables duration and distance contained negative variables or variables equal to zero. These instances have been deleted because they did not hold data of a real trip.

After cleaning the data 128,719 instances remain in the final dataset which will be used to conduct the analysis on.

Step 4: Variable creation

In order to be able to analyse the data and test the hypothesis, some new variables need to be created. The following variables were created:

1. truck

2. gps deviation

3. weekday

4. weekend

5. dist centre

6. range centre

The subsequent paragraphs will elaborate further on the meaning of the variables as well as on how they are being created.

1. truck

A dummy variable indicating whether or not the bike has been picked up by a truck is needed. This variable is not present in the current data file.

There are three options concerning the pick up and redistribution of the bikes. Firstly, the bike can be picked up by a user for a trip. Secondly, the bike can be collected by the user because of a given incentive to manually redistribute the bike or thirdly, the bike can be collected by a truck in order to relocate the bike. A dummy variable truck will be created indicating whether or not the bike has been picked up by a truck. The first two options will result in the variable having the value ’0’ and the last option will result in the variable holding the value ’1’. Chapter 3. Methodology 48

This variable will be created by comparing the start location6 of a trip of a bike with the end location7 of this bike during the previous trip. If these locations match, the bike is picked up by a user. If the locations do not match, a van or truck picked up the bike between those two trips and dropped it on another location.

When looking at the data, it is clear that there is a measurement error in the variables start lat, start lon, end lat and end lon. Therefore the latitude and longitude of the gps variables are not completely accurately indicated. For that reason, we have to take into account an error margin for the making of the variable truck. To find a reasonable error margin, a second variable, gps deviation, needed to be created. This variable calculates the distance between the start location of the trip and the end location of the previous trip. Based on this error we can now classify all trips into being picked up by a user or by a truck more accurately.

2. gps deviation

This variable is made to help find a reasonable error margin for the calculation of the truck variable. We calculated the distance between the start location (lat1, lon1) of a trip and the end location (lat2, lon2) of a previous trip by using the Haversine formula8:

gps deviation = R ∗ c with a = (sin(dlat/2))2 + (cos(lat1) ∗ cos(lat2) ∗ (sin(dlon/2))2) √ √ c = 2 ∗ atan2( a, 1 − a)

dlon = lon2 − lon1

dlat = lat2 − lat1 in which R is the radius of the earth.

To find the reasonable error margin, we display the density plot of the gps deviation variable. A first guess would be to have an error margin of around 175 meter. We find it logical to assume a truck would not drive out to pick up a bike an drop it less than 175 meter further. We can now look at this density plot to verify this. We see a spike in the begin values and

6Start location is indicated by the variables start lon and start lat. 7End location is indicated by the variables end lon and end lat. 8The Haversine formula is a formula often used when calculating small distances on a sphere. Chapter 3. Methodology 49 then a sharp drop. This spike probably contains all the values of the error. After a certain distance, the function drops and it is likely that those distances are covered by a truck and are not due to an error. The density plot of the full dataset is given in figure 3.3.

Figure 3.3: Density plot of gps deviation.

On this figure, it is hard to tell around what value the density drops. We assume that the measurement error is not larger than 400 meter and thus we make a subset of the total dataset with gps deviation values smaller than 400 meter. On figure 3.4, we see a drop before the 0.1, which indicates an error of 100 meter, mark on the x-axis. The descriptive statistics show that 75% of the instances have a gps deviation smaller than 75 meter. Chapter 3. Methodology 50

Figure 3.4: Partial density plot of gps deviation.

Based on this density plot, we can set our error margin. In this research, it is better to incorrectly label a trip that was picked up by a truck as being picked up by a user than the other way around. Incorrectly labelling a trip that was picked up by a user as being picked up by a truck has a bigger impact on the analysis. Because of this, we will set our error margin at 200 meter. As said before, it is unlikely a truck will pick up a bike and drop it 200 meter further.

In order to understand better how to make this new variable, some basic understanding of how GPS coordinates work is necessary. A GPS coordinate consists of two numbers. Longitude and latitude. Longitude lines are vertical lines on the globe all coming together in the North and South Pole. The value of the longitude shows how much east or west of the Prime Meridian the location is located. Longitude ranges between 0 and 180. Latitude lines are horizontal lines on the globe. The value of latitude shows how much north or south of the equator the location is located. Latitude ranges between 0 and 90. In the dataset, a value typically consists of a number with six digits after the comma. The first decimal Chapter 3. Methodology 51 point typically indicates a distance in the range of 11.1km. The second decimal point 1.11km. The third decimal point 111m. The fourth 11.1m and the fifth 1.11m. This means the locations with latitude 50 and 50.1 are 11.1km away from each other in a northerly direction. The locations with latitude 50 and 49.9 are 11.1 km removed from each other in southerly direction. The same goes for longitude in a westerly and easterly direction.

3. weekday

A variable weekday is made that displays what day of the week the trip was conducted. This variable is made based on the start time variable of the trip.

4. weekend

A dummy variable weekend is created by simply looking at the previously made variable weekday. If the trip is made on Saturday or Sunday, the variable gets label ’1’. If not, the variable is labeled ’0’.

5. dist centre

Part of the analysis focuses on the location of a bike within the city. Therefore, a variable dist centre is being made. This variable indicates how far from the city centre the bike is left behind. To calculate this variable, the gps coordinates9 from every city in the dataset are stored. Then, the distance between the end location and the city centre is being calculated, again with the Haversine formula.

6. range centre

In order to split the data of a city into a centre part and an outskirt part, a variable range centre needs to exist. This variable contains a value that indicates what part of the city is considered centre and what part is considered to be the outskirt of the city. This value is different for every city according to the size of the city.

To calculate this range, the size of the operational zone of every city needs to be taken into account. According to the founder of Mobit, the operational zone is roughly the same as the surface of a city. There is no research on how to estimate what part of a city is centre or outskirt. This thesis divided the total surface of a city by thirty in order to have the size the centre. Thirty was chosen after an analysis of all the cities in the dataset and trial and error with different values. 9The gps coordinates are found on the website: https://latitude.to. Chapter 3. Methodology 52

Data Analysis

After cleaning the data and creating the extra variable, a new dataset has been obtained. On this dataset, the actual analysis will be conducted. In this subsection, we will walk through the programming that has been done to obtain the results that will be discussed in chapter 4.

After reading in the data, modifying the variable city name and changing the data types of some variables, the dataset is being sorted. First, we sort the dataset on the variable bike id. We now have a dataset in which all trips from a certain bike are grouped together. Secondly, for every individual bike we sort the trips chronologically by sorting the sorted dataset on the variable start time. Figure 3.5 shows an example of how this dataset is ordered.

Figure 3.5: Example of ordered dataset.

After ordering the dataset, the variable truck is being made. It is very important for the analysis that this variable is made in the very beginning. Some rows may have missing or incorrect values but these rows need to stay in the dataset in order to not incorrectly classify the bike as being picked up by a user or by a truck. The function to classify the trip compares the start location of a trip with the end location of the previous trip so it is important that all trips are in the dataset. If we delete a certain trip because of incorrect values, the function will compare the start location of a trip with the end location of a trip before the previous trip and have a high chance of labeling the trip as being picked up by a truck while this is not the case. This would influence the analysis in a non beneficial way.

Once every trip has a value for the truck variable, we can delete the instances with wrong or incorrect variables. User id’s are unique, positive values assigned when a user makes an account. We noticed one odd user id ’-99’, this id is being deleted from the dataset. Since Chapter 3. Methodology 53 we want to conduct our analysis on real trips, we defined a trip as having a duration of more than zero minutes and a distance of more than zero meter. If one of the two conditions is not met, we can not consider the instance a real trip and the variable is being deleted from the dataset. When investigating what instance did not meet these conditions, we see that most of these instances contain missing values, wrong city names or other incorrect information. We do not lose a lot of meaningful data. Together with the removal of these instances, the variables city name, bike id and the empty variable gps str are removed from the dataset.

The variable city name contains more cities than Mobit is operational in. After inspection of all the possible values, we noticed incorrect values as well as cities where Mobit is only conducting small experiments. These experiments include conducting market research in new cities, as well as providing small fleets to be used on events. These cities as well as incorrect values are being removed. 11 cities remain, this is in line with the answer the founder gave during the in-depth interview.

After all these modifications, only 3098 values contain missing values. The missing values only appear in the end lat and end lon variables. These variables are being removed from the dataset since we cannot impute them with a meaningful statistic. A variable with missing values for the end location would always have a value ’1’ for the truck variable. Because of this, some trips will incorrectly be labeled as being picked up by a truck and will influence the analysis in an incorrect way.

As mentioned before, having outliers in the data will influence the analysis. The outliers are detected by calculating the Z-score for the distance, duration, gps deviation and dist centre variable. A threshold of 3 is being used as a cut-off value. Every instance with a z-score lower than -3 or higher then 3 is deleted from the dataset. For the variable distance 113 outliers have been detected. The variable duration has 453 outliers, gps deviation contains 785 outliers and dist centre counts 516 outliers.

Once the dataset has been set on point. An important step is dividing the dataset into different datasets based on the analysis that will be performed. For one part, the dataset needs to be divided in a subset with data from before and a subset with data from after the introduction date of the incentive scheme. 47.80% of the dataset contains information from before the introduction and 52.20% from after the introduction. For another analysis the dataset is being divided based on the variable weekend. A third division is made based on the variable range centre. Chapter 3. Methodology 54

The different analyses can be conducted now. Firstly, we can investigate how many of the instances have been picked up by a truck or a bike. To do this, we compute the percentage of bikes that has been picked up by a truck and compare this before and after the introduction of the incentive scheme.

Secondly, we can check if there is a significant difference in use in the week or the weekends.

Thirdly, based on the variable dist centre we can investigate if there are more bikes being picked up by a truck outside of the city centre. We check this for every city separately. The findings of this analysis will be displayed in maps created with the package Folium in Python.

For every test, the significance is being checked with a two proportion Z-test. The null hypothesis of a Z-test states that the means of both subsets are statistically equal. If the p-value is smaller than 0.05 we can reject this hypothesis on a 5% significance level.

3.3 Reliability and validity

Reliability and validity are two very important concepts in order to conduct a good and reliable research. It is rarely the case that research is both perfectly valid and reliable. Even though this thesis is a case study in which we explore the problem and the provide the basis to investigate the matter in more detail, this research is still designed in order to maximise both reliability and validity. If these two concepts are not guaranteed it is incorrect to generalise the findings of the research to a larger population.

Reliability is a necessary condition in order to have validity. This means there can be no validity when there is no reliability. But this does not hold the other way around. (De Pels- macker & Van Kenhove, 2015) For this reason, we will start by checking the reliability of the research. The next two sub-paragraphs will elaborate further on the validity and reliability.

3.3.1 Reliability

Reliability is the extent to which the research is free of random errors and thus leads to consistent results.

It is very hard to assess the reliability of an in-depth interview because of several reasons. The first reason is the difficulty to conduct a repeat study to verify if the results are the Chapter 3. Methodology 55 same. A second reason is the fact that an interviewer has to interpret what the interviewee says. Misinterpretation could lead to low reliability.

To check the reliability of the research, we can compare the qualitative data with the quantitative data and try to look if there are any inconsistencies. During the in-depth interview, the interviewee mentioned being fully operational in 11 cities and having around 1,500 bikes in the system. This is also the case when we look at the quantitative data, we find 11 cities and 1,472 bikes which is roughly the same. The average duration of a trip is 12.06 minutes, which is around the 12 minutes mentioned in the interview. So far, no inconsistencies are found. According to these findings, we can assume the data is quite reliable. The interviewee believed the incentive schemes are working and induce less trucks being on the road to rebalance the system. To check if the quantitative data also reflects this finding we refer to chapter 4.

3.3.2 Validity

Validity is the characteristic of a research design that ensures the research measures what it aims to measure. If there is no systematic error we can say the research is valid. This systematic error is also called bias. (De Pelsmacker & Van Kenhove, 2015)

There are different kinds of validity that can be checked. An important one is content validity. This validity is ensured because of the thorough literature study that preceded the making of the interview guide. Also a check by an expert was being made before conducting the interview.

Since a case study is a complete and accurate description of the problem at hand by con- sulting various different information sources, it has a large internal validity. Because of these reasons we can conclude that the results in this dissertation are valid. (De Pelsmacker & Van Kenhove, 2015) Chapter 4

Results

In this chapter, the results of both the qualitative and quantitative research will be dis- cussed. First, a summary of the findings from the in-depth interview will be given. After, the results from the descriptive research of the quantitative data are examined followed by the results of the conclusive part.

4.1 Qualitative Research

To analyse the qualitative results, the in-depth interview is interpreted. For some parts that are not entirely clear or needed some more background information, the world wide web is being used as a supplementary source to gain more information and insights. The answers of the in-depth interview can be categorised in to four subcategories by using the techniques of recursive abstraction. Those subcategories are:

1. Competition in Belgium

2. Pricing Strategy

3. Motives for using Bike Sharing Systems

4. Fleet Rebalancing Problem

The next paragraphs will briefly summarise the answers on these topics and report the findings that follow out of these summaries.

56 Chapter 4. Results 57

4.1.1 Competition in Belgium

The bike sharing industry in Belgium is in a very early phase. Because of that, not a lot of competition exists and the competitors that are active are mostly in a start-up stage. This also means that a lot of new entrants are entering the market. These new entrants all have a specific focus and strategy. Some focus on fixed stations while others are free floating or hybrid. Some focus on big cities while others want to establish market share in smaller cities and then move with a fully developed concept and strategy to the bigger cities. Currently, there are 7 players on the market. Table 4.1 gives an overview of these players and some key characteristics. As you can see, the Belgian market has a mix of free floating, station-based and hybrid systems. Mobit considers Blue-Bike as their biggest competitor because of their similar profile. Both companies are active in small and bigger cities and have roughle the same price point. The only difference is Mobit operates as a free floating system and is moving more to a back-to-many station-based system while Blue-Bike is fully station-based.

Some operators have only electrical bikes or a combination of electrical and standard bikes to increase the convenience for the users. Off course, this brings an extra operational issue of charging these bikes.

While the charging of electrical bikes is only an issue for some operators, the fleet rebalancing problem is a problem all operators have tot deal with. Currently, they all use trucks to rebalance this fleet and as you can see in table 4.1, only 2 players use incentive schemes in addition to help solving this rebalancing problem. Villo!, the bike sharing initiative from Brussels , gives free extra minutes to people who park a bike in a station where demand for bikes is very high. The other bike sharing operator that works with an incentive scheme is Mobit. This incentive scheme is discussed in section 3.1.1. Chapter 4. Results 58

‘ Chapter 4. Results 59 X X X X X X X Incentive Scheme X X X X X X X Electrical X X X X X X X Hybrid System X X X X X X X Station-Based X X X X X X X Free Floating Table 4.1: Overview of Belgian Bike Sharing Market. + + + + year different Pricing price/min price/min unlock fee possibilities price/30min price/30min price/30min price/20min day/week/year day/week/year #Bikes 4,200 5,000 +1,000 150 300 500 1,500 #Cities 1 1 +20 1 1 1 11 Company V´elo Villo! Blue-Bike Billy Bike Cloudbike Jump Mobit Chapter 4. Results 60

4.1.2 Pricing Strategy

As you can see in table 4.1, pricing strategies of the different operators are roughly the same but still some difference are noticeable. Broadly, there are 3 different categories. In the first category, the user buys a day-,week- or year-pass at a fixed price and pays an additional fee for every trip depending on the duration of that trip. The additional fee is based on the price metric of price per time interval. This time interval varies between one and thirty minutes. In the second category, the user pays a fee to unlock the bike and an additional fee per number of minutes driven. These pricing strategies are called Two-Part Pricing. The third and most simple category is simply a price per time interval with no additional fees. This is the pricing strategy that Mobit is using.

To set their pricing, mobit used a Competition Based Pricing Method. They simply looked at what the competition was doing, and copied their strategy. Instead of charging per thirty minutes, they changed it to twenty minutes. Their price point is very competitive in the current landscape. But Mobit knows that there are different techniques to set their price. They do not have to copy the competition in order to have a good and appealing pricing for the customer. Instead, they have to find out what the customer really values and how they can maximise their revenue by optimising their pricing strategy. Implementing a dynamic pricing strategy could lead to a situation in which prices better reflect the value a customer assigns to a certain trip. This would increase revenues in the long term.

Mobit wants to shift more from price per 20 minutes to so called Heen-en-Weer bikes1. These bikes are rented per 24 hours for the fixed price of e3. These bikes would operate in a back-to-one system meaning they have to be returned to the initial pick up location. Mobit believes this would increase the rentability and usage rate of a bike significantly.

Normally, volume discounts are awarded when people consume large quantities of a product or service. So in theory, prices decrease when volumes increase. The opposite is true with bike sharing systems. One could think the rather odd pricing strategy of charging higher prices the longer the trip, is to make sure enough bikes are available in the system because the convenience for the user results from having a bike available whenever, wherever. If bikes are occupied for long trips, the bikes are not available in the system. But this is not the original motive. The in-depth interview learned that this pricing strategy is because of tax reasons. The longer the trip, the more taxes that have to be paid. While a short

1A literal translation would be Back-and-forth bikes. Chapter 4. Results 61 trip only induces 6% VAT2, a long trip requires more VAT. The assumption made in the introduction seems to be wrong.

4.1.3 Motives for using Bike Sharing Systems

In section 2.2.1 the motives why people use car sharing are thoroughly discussed. During the in-depth interview, some questions related to the motives of using bike sharing initiatives were asked. Some reasons came to light and are similar to the reason why people use car sharing. Firstly, there is the flexibility a bike sharing system offers. People can integrate bike sharing into their route planning together with public transportation. It fits perfectly in the multi modal shift3. A second reason is the convenience of the systems. Users of bike sharing systems are often local inhabitants. They find it really convenient to use the bikes to go shopping or drive home after having some beers in the local pub. They have to worry less about the hassle of taking a car. There is also an ecological motive. Using the bike and public transport instead of using a car can result in a lower impact on the environment but the flexibility and convenience of the system are more important to users.

These findings match with the motives found in the literature.

4.1.4 Fleet Rebalancing Problem

Some questions of the in-depth interview focused on the fleet rebalancing problem, the founder of Mobit’s perception on the problem, possible solutions and the perceived effec- tiveness of these solutions.

Working with a free floating system is beneficial for the flexibility and convenience of the system, but it adds complexity to the operations of a bike sharing system. When a bike is left unused, there are some different options. An individual can report a lost or unused bike or the operations team notices an unused bike in the system. In those cases, a truck is used to drive around the city and pick up these bikes. Another option is the system notices a bike is being unused for a long period of time and turns the bike into a bonus bike. This bonus bike can be used by a user of the system or when the bonus bike is not used for a long period of time the operations team also picks up this bike for redistribution.

2VAT stands for Value-Added Taxes. This is a consumption tax the company needs to pay to the government when selling a service or product. 3The multi modal shift is the trend in which individuals shift from using the car to integrating other means of transportation. Chapter 4. Results 62

According to the founder of Mobit, too many trucks are being used for the rebalancing of the fleet. Currently it is difficult to see how well the bonus bikes are helping with solving this problem but the founder believes the incentive scheme works and reduces the amount of trucks used for the rebalancing. The feedback from the users on the incentives is really positive. Users like the fact that they can earn free rides but also that the operator is working hard on lowering the impact on the environment.

From the in-depth interview, we can conclude that the founder of Mobit believes, according to feedback of the customers and a noticeable decrease in the usage of trucks, that the incentive scheme works to solve the fleet rebalancing problem in the case of Mobit.

4.2 Quantitative Research

The results from the qualitative research are being used as a guide to conduct the quanti- tative research. Ideally, the quantitative results match the qualitative results and we can approve the hypothesis.

First, an overview of some of the most important statistics will be given followed by the conclusive results.

4.2.1 Descriptive statistics

For the description of the descriptive statistics we will divide the dataset into different subsets in order to compare the results and look for any interesting findings. We start with the full dataset. Then we firstly divide the dataset in subsets from before and after the introduction of the bonus bike. Secondly, we divide into week or weekend data and lastly, we subset the dataset into centre and outskirt data.

Full dataset

Table 4.2 contains the most important key statistics of the variables in the full dataset. The mean distance is 1.750 kilometers and the mean duration is approximately 12 minutes. The mean distance between the end location of a trip and the start location of a subsequent trip is around 600 meters. It is important to note that this is largely because of the proportion of bikes that gets picked up by a truck and dropped on another location since for the first 75% of the trips, this distance is smaller than 75 meter. Chapter 4. Results 63

distance duration truck gps deviation dist centrum weekend count 128719.00 128719.00 128719.00 128719.00 128718.00 128718.00 mean 1750.96 12.01 0.1395 0.60 1.79 0.27 std 1589.13 16.24 0.35 4.87 6.84 0.44 min 1.00 1.00 0.00 0.00 0.00 0.00 25% 711.00 5.00 0.00 0.01 0.47 0.00 50% 1334.00 8.00 0.00 0.02 0.94 0.00 75% 2318.00 13.00 0.00 0.07 1.69 1.00 max 21096.00 304.00 1.00 80.32 567.24 1.00

Table 4.2: Descriptive statistics of Full dataset.

Around 27% of the trips are made during the weekend. This indicates a roughly constant use of bikes during the week and weekend since the weekend also accounts for 28% percent of a full week. This is also found in figures 4.1 and 4.2. A value of 0 indicates weekday and a value of 1 indicates weekend.

Figure 4.1: Plot of amount of trips per day of the week ordered from most to least. Chapter 4. Results 64

Figure 4.2: Proportion of trips during weekday and weekend.

During Sunday and Monday there are less trips than on average. The day on which most people use a bike from the bike sharing initiative is on Friday.

In 14% of the cases, the bike has been picked up by a truck between two rides. This can be either because of a defect in the bike or because of redistribution purposes. Figure 4.3 shows, on the red beam, the proportion of bikes being picked up by a truck between two trips and, on the green beam, the proportion of bikes being picked up by a user for a trip. Chapter 4. Results 65

Figure 4.3: Proportion of trips picked up by user or truck.

Figure 4.4 shows all cities in which Mobit is operational. The size of the red bubble is representative for the amount of trips in the city. As you can see most trips are taken in Mechelen, followed by Kortrijk and Hasselt. The smallest bubble is Ghent, this was only a test project. As you can see, Mobit is currently only operational in Flanders. Chapter 4. Results 66

Figure 4.4: Map: Operational cities of Mobit in Belgium.

The next three paragraphs will dive deeper into the differences in the descriptive statistics of subsets of the full dataset. a. Before vs. After introduction Bonus Bike

In table 4.3 and 4.4 we notice that most of the variables are roughly the same as for the full dataset except for the variable truck. The variable truck has a value of around 20% before the introduction of the bonus bike and only 8.5% after this introduction. Section 4.2.2 will further elaborate on this phenomenon. Other than that, before the introduction the distances were shorter and durations longer than in the general case. While after the introduction it is the other way around. The distance from where the bikes are being left behind to the centre is almost equal as well as the variable weekend.

There is a noticeable difference in the distribution over the weekdays. Before the introduc- tion the distribution over the weekdays is less outspoken than in the general case. Friday is still the most popular day but the other days are now more equally popular. While after the introduction, the distribution is more or less equal to the one in the general case. Chapter 4. Results 67

distance duration truck gps deviation dist centrum weekend count 61309.00 61309.00 61309.00 61309.00 61309.00 61309.00 mean 1703.29 12.41 0.1996 0.48 1.83 0.28 std 1530.87 16.98 0.40 3.67 9.18 0.45 min 1.00 1.00 0.00 0.00 0.00 0.00 25% 687.00 5.00 0.00 0.01 0.44 0.00 50% 1295.00 8.00 0.00 0.03 0.91 0.00 75% 2228.00 14.00 0.00 0.13 1.60 1.00 max 9996.00 299.00 1.00 80.10 567.24 1.00

Table 4.3: Descriptive statistics of subset Before Bonus Bikes.

distance duration truck gps deviation dist centrum weekend count 67409.00 67409.00 67409.00 67409.00 67409.00 67409.00 mean 1794.25 11.65 0.0849 0.71 1.75 0.26 std 1639.08 15.53 0.28 5.75 3.58 0.44 min 1.00 1.00 0.00 0.00 0.00 0.00 25% 733.00 5.00 0.00 0.01 0.50 0.00 50% 1369.00 8.00 0.00 0.02 0.98 0.00 75% 2395.00 13.00 0.0 0.05 1.79 1.00 max 21096.00 304.00 1.00 80.32 126.36 1.00

Table 4.4: Descriptive statistics of subset After Bonus Bikes. b. Week vs. Weekend data

Table 4.5 contains the descriptive statistics of the subset with week data. If we compare this with table 4.6 we notice almost all variables are roughly the same except for the variables distance and duration. During the weekend, people tend to make longer trips. A possible explanation could be that users use the bikes more as a leisure activity and do some sightseeing trips or take the bikes to drive away from the city centre. The variable truck is almost the same. Even though it may look like there is no difference, it could be that those percentage are significantly different due to the size of the dataset. Chapter 4. Results 68

distance duration truck gps deviation dist centrum weekend count 93921.00 93921.00 93921.00 93921.00 93921.00 93921 mean 1711.93 11.45 0.1420 0.59 1.80 0.0 std 1531.56 15.08 0.35 4.80 6.45 0.0 min 1.00 1.00 0.00 0.00 0.00 0.0 25% 706.00 5.00 0.00 0.01 0.47 0.0 50% 1315.00 8.00 0.00 0.03 0.94 0.0 75% 2269.00 13.00 0.00 0.08 1.67 0.0 max 21096.00 304.00 1.00 80.32 567.22 0.0

Table 4.5: Descriptive statistics of subset from Weekdays.

distance duration truck gps deviation dist centrum weekend count 34797.00 34797.00 34797.00 34797.00 34797.00 34797 mean 1856.18 13.52 0.1327 0.63 1.75 1.0 std 1730.55 18.92 0.34 5.07 7.78 0.0 min 1.00 1.00 0.00 0.00 0.00 1.0 25% 728.00 5.00 0.00 0.01 0.46 1.0 50% 1387.00 9.00 0.00 0.02 0.96 1.0 75% 2454.00 15.00 0.00 0.07 1.77 1.0 max 20665.00 301.00 1.00 79.98 567.24 1.0

Table 4.6: Descriptive statistics of subset from Weekends. c. Centre vs. Outskirt data

It is very logical that the mean distance of the centre data is smaller than the mean distance of the outskirt data. The same goes for the duration of those trips. A difference in the variable truck is perceivable. Trips further away from the city have the tendency to be more likely to be picked up by a truck than a user. Another, but smaller, noticeable difference is the variable weekend. During the weekend, people use a bike more to drive to the outskirt of the city than to drive within the city centre. Again, this could be explained by the fact that people tend to use the bike sharing system during the weekend more as a leisure activity and during the week more as a integrated means of transportation.

In this case, the difference in the variable truck is larger. We notice that for the centre data Chapter 4. Results 69 in 13.42% of the instances a truck picked up the bike in between two trips. For the outskirt data this is 16.46%. A difference of a little more than three percent.

distance duration truck gps deviation dist centrum weekend count 106314.00 106314.00 106314.00 106314.00 106314.00 106314.00 mean 1542.93 11.06 0.1342 0.41 0.86 0.27 std 1340.84 15.33 0.34 3.77 0.54 0.44 min 1.00 1.00 0.00 0.00 0.00 0.00 25% 676.00 5.00 0.00 0.01 0.39 0.00 50% 1242.00 8.00 0.00 0.03 0.79 0.00 75% 2025.00 12.00 0.00 0.07 1.22 1.00 max 21096.00 301.00 1.00 80.32 2.30 1.00

Table 4.7: Descriptive statistics of subset from Centre Data.

distance duration truck gps deviation dist centrum weekend count 22404.00 22404.00 22404.00 22404.00 22404.00 22404.00 mean 2737.96 16.55 0.1646 1.51 6.19 0.29 std 2190.32 19.37 0.37 8.23 15.62 0.45 min 1.00 1.00 0.00 0.00 2.30 0.00 25% 1000.00 7.00 0.00 0.01 2.83 0.00 50% 2453.00 13.00 0.00 0.02 3.52 0.00 75% 3846.25 19.00 0.00 0.08 5.16 1.00 max 20665.00 304.00 1.000 80.00 567.24 1.00

Table 4.8: Descriptive statistics of subset from Outskirt Data.

4.2.2 Conclusive results

In section 3.2.2, some possible trends were presupposed. During this conclusive research, these trends were being investigated. In this part, the outcome of the research on these trends will be given.

1. More bikes are being used during weekdays than in the weekend.

As mentioned in the previous section. The distribution of the trips over the weekdays is fairly constant with a peak on Fridays and a dip on Sundays. A possible explanation Chapter 4. Results 70 was given during the in-depth interview. The founder noted that a lot of users are local inhabitants that use a shared bike to drive home after going to a bar and having too many alcoholic drinks. Since Friday is in Belgium for most people the end of the work week a lot of people go for a drink with their colleagues as a start of the weekend. This explanation could indeed be an indication on why Friday is the most popular day. Unfortunately, we do not find a possible explanation in the quantitative data.

Another investigated trend closely related to the previous one is:

1.bis More bikes are being left on places where they are likely to be picked up by a truck during the weekend than during the week.

In the descriptive statistics, we noticed only a small difference in the variable truck between the week and weekend data. The subsets had a percentage of respectively, 14.20% and 13.27%. While this difference may look small, only less than one percent, it proved to be significantly different. This can be explained by the size of the dataset. Because it contains a lot of data on individual trips, only small differences can be statistically significant. With a p-value of <0.001 we can reject the null hypothesis on a 5% significance level. The null hypothesis states that the mean values are statistically the same. So we can state that the presupposed trend is false and the opposite trend is true. During the week, more bikes are likely to be left behind on uninteresting places than during the weekend. A possible explanation could be that during the weekend, when people use the bikes as a leisure activity, they use it more for round-trips. This causes bikes to end up on roughly the same location as where it started.

2. The further away from the city centre a bike is parked, the more likely this bike is to be picked up by a truck.

This is being tested for every city in the dataset. Table 4.9 displays the different p-values on the 5% significance level. Only for the cities Antwerp, Brasschaat and Aalst and the region Hoog Kortrijk is the difference in bikes being picked up by a truck between city centre and city outskirts not significant. For the other 7 cities, the difference is significant. The presupposed trend is true. The further away from the centre, the more likely a bike is to be left behind and picked up by a truck after a certain amount of time. Chapter 4. Results 71

city p-value Genk 0.000 Hasselt 0.000 Mechelen 0.000 Kortrijk 0.000 Antwerp 0.465 Brasschaat 0.628 Aalst 0.437 Bornem 0.000 Ghent 0.000 Hoog Kortrijk 0.270 Evergem 0.000

Table 4.9: P-value per city.

One example will be treated in detail. Figure 4.5 displays a map of Kortrijk4 with all the end locations of the bikes of every trip. A green icon means the bike is being picked up by a user for a next trip while a red icon means a truck picked up the bike. In 12.94% of the trips within the city centre, a truck redistributed the bike in between two trips. Figure 4.6 displays the map of the outskirt of Kortrijk. The same color legend is applied. Here, in 18.56% of the instances a truck redistributed the bike in between two trips. The maps clearly display the trend. The further away from the city centre the more likely a bike is to be picked up by a truck.

4A random sample of the city Kortrijk is taken in order to be able to map the outcome in a clear manner. Chapter 4. Results 72

Figure 4.5: Map: Centre of Kortrijk.

Figure 4.6: Map: Outskirt of Kortrijk.

Figure 4.6 displays relatively more red than green icons compared to figure 4.5. Chapter 4. Results 73

3. Incentive schemes lead to less bikes being picked up by trucks.

This is the hypothesis of this master dissertation postulated in section 2.6. The mean of the variable truck before and after the introduction of the incentive scheme is being statistically compared.

Before the introduction of the incentive scheme, the mean of the variable truck was 0.1996. This means in 19.96% of the instances, a truck picked up a bike in between two trips. After the introduction, this percentage dropped to 8.49%. The p-value of the two proportion Z-test is <0.001 and thus we can conclude on a 5% significance level that the means are significantly different.

An important note to make is the fact that bikes are not only picked up by trucks for redistribution purposes. When a bike has a defect and needs maintenance, they get picked up by a truck as well. However, in this research we assumed the fraction of the bikes being picked up for maintenance as constant before and after the introduction of the bonus bikes. There is no relation between the introduction of an incentive scheme and the frequency with which bikes break down. There are no other signs to assume less bikes had defects or were picked up for maintenance after the introduction of the incentive scheme. No new pick up policy, no new bikes, etc.

For these reasons, we ascribe the full decline in percentage of picked up bikes to the intro- duction of the incentive scheme and conclude that the introduction of an incentive scheme as a means of dynamic pricing is an effective tool to decrease the usage of trucks to solve the fleet rebalancing problem by incentivising users to using bikes that would otherwise be rebalanced by trucks. Chapter 5

Discussion

This final chapter will give a brief summary of the results of the case study from both the qualitative and quantitative part, followed by a general conclusion of these results. This conclusion will include some managerial implications towards bike sharing operators. A discussion in which the contribution to the existing literature is explained will be given. Thereafter, the limitations of the research are summed up and lastly some suggestions for future research are cited.

5.1 Brief Summary of Results

The bike sharing industry in Belgium is in a very early phase. Only a handful of operators are active in Belgium and now and then, a new entrant enters the market. Every competitor has more or less its own focus and strategy. This makes it possible to compete within such a small surface area and obtain market share.

Even though companies have different strategies, their pricing strategies are more or less the same. They all use the same price metric, price per time interval. This time interval varies between 0 and 30 minutes, but the concept is the same. Mobit set its pricing based on the competition. This Competition Based Pricing Strategy is the most simple to use but does not reflect the true value a customer assigns to the service. Better would be to implement a dynamic pricing strategy. One rather strange finding is the fact that prices increase the more a user uses the service. This is in contrast with normal volume discounts and is purely out of tax considerations.

The motives for using a bike sharing operator match with the motives found in the literature.

74 Chapter 5. Discussion 75

Flexibility and convenience are the biggest reasons.

According to the founder of Mobit, the incentive scheme leads to less trucks being used and higher customer satisfaction. But it is difficult to measure the effectiveness and quantify these results.

In the quantitative research we found that the distribution of the trips over the weekdays is roughly constant. In the general case, 14% of the time a bike is being picked up by a truck in between two trips. When we split the dataset in centre and outskirt data we find that these proportions change to respectively 13.42% and 16.46%. When divided into week and weekend data we find respectively, 14.2% and 13.27%. And when we split the dataset in a dataset before and after the introduction of the bonus bikes we find respectively, 20% and 8.5%. All these difference are proven to be significantly different from each other.

5.2 Conclusion

There are some conclusions that can be drawn out of the results from this case study. The first conclusion concerns the current incentive scheme. In the case of Mobit, a relatively large Belgian Bike Sharing Operator, giving away free coupons to users works as a means to help with the redistribution the fleet. It caused a decrease in the usage of trucks of almost 12%. This has a big impact on both the operational cost of using this fleet of trucks and on the environmental impact. At this moment in time, it is impossible to make statements about the size of this impact. It is important to note that an incentive scheme works in this specific context. There is not a lot of price differentiation between the competitors, which means that the incentive makes a difference for the user. If there would be a much cheaper operator, maybe users would simply shift to this operator instead of using a bonus bike.

The other conclusions concerns the dynamic pricing aspect. Two trends are found. Firstly, during the weekdays bikes are left on places where they are more likely to be picked up by a truck. The data did not give an explanation for this. It could be people use their bike to drive to work and carpool or use public transport on their way back. Secondly, in most of the cities the further away from the centre a bike is left behind, the more likely it is to eventually be picked up by a truck. An explanation for this could be the fact that in the centre, the population density is larger which means more possible users live closer together. This increases the chances for a bike of being picked up by a user. Chapter 5. Discussion 76

5.2.1 Managerial Implications

Based on these conclusions, some managerial implications can be made. These implications could be used as a first form of dynamic pricing to try and capture as much value as possible and optimise revenues.

1. Parking a bike further away from the centre should induce a larger fee.

2. Picking up a bike that is parked further away from the city should be cheaper.

3. Trips on Sunday or Monday should become cheaper.

4. Trips on Friday(-evening) should become more expensive.

5. Using incentives, in the form of free trips, to rebalance the fleet works.

Implication 1 and 2 reflect the value a user gives to the distance to find a bike, this is the place of use. Users who value driving a bike further away from the city will also pay more since the cost is larger for a bike operator. This will optimise revenues because it lowers the cost of picking up these bikes.

Implication 3 and 4 reflect the time of use. More users use the bike on Friday as explained in section 4.2.2. when users assign more value to using a bike sharing system on Friday evening to get home, the operator can charge more without losing the customer. Same goes for Monday and Sunday. Apparently, people are using the bike sharing systems less on these days. Decreasing the price could persuade people to use the bikes instead of other means of transportation.

The final implication reflects the willingness of users to collaborate to rebalance the fleet. However, the motive for this willingness is unclear. It could be an ecological motive, really wanting to help lower the impact of the bike sharing operator. Or it could be an economical motive, wanting to earn free trips. Nevertheless, incentive schemes are proven to be working so it is of great usefulness for an operator to implement one.

5.3 Discussion

This research differs from the existing literature in a couple of ways. In the existing lit- erature, most research focuses on solving the fleet rebalancing problem in station-based systems. This thesis focuses on the problem in a free floating system. Another key differ- ence lies in the method used to solve the rebalancing problem. Existing literature focuses on Chapter 5. Discussion 77 optimising the balance within the system in order to need less trucks for the repositioning of the bikes. They use forecasting methods as well as methods from operations research such as Markov chains in order to calculate the optimal fleet level at the different stations and figure out the most efficient route for the trucks to rebalance the system. This research investigates the effectiveness of dynamic pricing to solve this problem. One last difference with the existing literature is that it concerns a real case study of a European free floating bike sharing operator. The most recent studies concern Chinese bike sharing operators since they encounter the largest growth in the recent decades.

5.4 Limitations

Off course, this research has some limitations. This section will talk about these limitations and how they could be avoided or improved in future research.

A first limitation is in the making of the variable range centre. In order to make this variable, the operational zone of a city has to be known and a distinction between centre and outskirt has to be made. The size of the operational zone was asked to the founder of Mobit. But for making the distinction between centre and outskirt, a rather arbitrary variable was chosen as a radius for the centre of a city. The program does take into account the surface of the city, this means the larger the operational zone, the larger the city centre. However, the value to determine what portion of the city is centre and what portion is outskirt is based on common sense and logical thinking. It would be beneficial for the study if this variable could be estimated more precisely.

Another limitation is related to the variable truck. Bikes are not only picked up by a truck when they are left unused for a long time. In some cases, broken bikes are also picked up by a truck when they need maintenance. In this study, this is not taken into account. However, we believe this does not influence the analysis in a major way. The proportion of broken bikes does not change dramatically because of the introduction of bonus bikes. We can assume that the difference in proportion of picked up bikes before and after the introduction of the bonus bikes is almost entirely due to users reallocating the bikes and not due to less bikes needing maintenance. For this study, we did some research and asked the founder to find out if there are other factors that could have influenced the variable truck but nothing came to light. Nonetheless, it would be interesting and advantageous to take this in to account and split the variable truck in to a part due to the redistribution and another part related to the maintenance of bikes. Chapter 5. Discussion 78

As with every analysis that relies on data, it would be interesting to add more variables into the dataset. This dataset purely focussed on proving if the existing incentive scheme works or not. However, the explanatory power is rather low. This could increase if more variables are added in the analysis.

5.5 Suggestions for future research

As mentioned before, this paper is a preparatory paper for further research. It proved that incentive schemes could work as a means to redistribute the fleet and that there is opportunity to implement a dynamic pricing system. However, more research could be done on this topic.

An interesting extension of this thesis would be to incorporate data on prices, revenues and costs as well as variables on the impact of the trucks on the environment. That way, a study could investigate whether or not an incentive scheme is actually profitable for the operator. The analysis of the impact on the environment could be used as an argument to convince cities to invest more in such green transportation initiatives and scale the businesses. It would not only convince governments but also consumer to include bike sharing into their daily modes of transportation.

Another interesting study that builds on this thesis would be a conjoint analysis to explore what the characteristics are a user really values. The types of incentive users want would come to light. This way, more effective incentive schemes could be made on which the algorithms for the dynamic pricing strategy could be build. With these dynamic pricing strategies in place, bike sharing could become a major part of our daily transportation and be part of the future of Smart Mobility. Appendix A

Appendix

A.1 In-depth Interview

In this part of the appendix, the fully typed out in-depth interview is included. This interview took place on the 13th of May 2019 at the offices of Mobit in Ghent. The total duration of the interview was 27’54”. As you can see, the interview roughly followed the interview guide provided in section 3.2.1. Off course, some changes had to be made to ensure a smooth and meaningful conversation. Some follow-up questions were added during the conversation and are also added in the appendix.

1. Introductory questions

1. Q: When was Mobit founded and was it only by your or any co-founders? A: The company is incorporated in early May 2017. Around 2 years ago, but the first idea we had was in November 2016. We really started the project in February 2017. There are 3 founders.

2. Q: How many number of bikes are currently registered in the system approximately? A: Around 1,500.

3. Q: In how many cities in Mobit fully operational? A: The bikes are divided over 11 cities in Belgium.

4. Follow-up Q: Are the locations free-floating or station-based? A: 9 locations are free-floating and 2 locations are back-to-many1. But we are now introducing to go more to a back-to-many system. Because the utility rate or the

1In a Back-to-Many system people can pick up a bike at any given station and park them at another

79 Appendix A. Appendix 80

usage rate of back-to-many is higher than with the free-floating bikes. So we want to combine the advantages of free-floating with the advantages of back-to-many. We want to go to a system where we have stations where people pay a standard tariff when going to a station and they pay a higher fee when they park freely in the operational zone.

5. Q: What are the average number of trips per month? A: Now we are at 0.3 rides per day, per bike.

6. Q: Are there many competitors in Belgium? A: Off course it is a new market. I would say limited competition. For the fixed stations there are three existing players. VA˜©lo in Antwerp, Villo in Brussels and Blue-bike in more than 50 locations. Billy Bikes has 300 bikes in Brussels,they are launching in Ghent too. And then we have Cloudbike. They have 300 bikes in Antwerp. Recently, Jump entered the market.

7. Follow-up Q: Jump, the bike sharing system of Uber?

8. A: Yes, they launch now 600 bikes in Brussels.

9. Follow-up Q: Free-floating probably as well? A: Yes, but electric bikes!

10. Q: Who are/is the biggest competitor(s)? A: Good question. I think Jump mainly wants to focus on major cities while we are not. It is different than with a taxi service. with the taxi service you can have a running system with a limited amount of cars while you need a lot of bike to be able to have a good service level in a bike sharing system. So maybe more Bluebike. Because we are active in big and small cities just like they are. They operate in a back-to-one while we are free-floating or back-to-many. So Jump is a competitor mostly in the major cities while Blue-Bike is one in all cities.

11. Q: How did you came to the pricing system of Mobit? A: We checked a bit with our competition how they are doing it. Some of them have e0.50/30’. We said let’s make it 20’ and e0.45 but pricing is something we are open to review. The concept of paying per 20’ is something we learn from other players. The exact time and price we chose ourselves but it has to stay competitive. We would love station. A Back-to-One system means people can pick up a bike at any given station and have to return the bike to that particular station. Appendix A. Appendix 81

to see how price changes effect the revenues. Now we have launched the ’Heen-en-weer’ which is the back-and-forward bike. This is actually a back-to-one bike where the user is forced to buy a day-pass. We would like to change more bikes to day-passes to see what the average usage would be. At this moment, we just want to increase the usage rate of the bikes with the bonusbikes and the back-to-one bikes. Priority number one is to increase efficiency. You have the number of rides per bike, per day but you also have the return per bike, per day and then day-passes are more interesting. Or we could increase the price a bit from e0.45 to e0.55 but the question is what will the effect of this price increase be compared to introducing more day-passes. This is for sure something we would love to investigate.

12. Follow-up Q: Why is it a trip becomes more expensive the longer it takes? A: Because there is a 6% VAT for short trips. Normally the VAT for services is 21%. If you have a short-term rental it is lower and the tax authorities said you need to show that you encourage to people to take short trips. So it is more interesting to have a short trip over a long trip.

13. Follow-up Q: It is not because you want to have the bikes always available in the system and thus encourage people to only take short trips? A: No, that is not the major reason. Mainly tax purpose. We barely see anyone using the bike longer than one hour. In the beginning we really focused on single ride. I think, looking back now, it should be more day-passes.

2. Serious questions

1. Q: What do you think motivates people to use bike sharing systems? A: For sure, the flexibility a bike sharing system offers. It fits perfectly in the multi modal shift. Instead of taking the car from a to b, they just want to split up the journey in the first mile, then public transport followed by the last mile. And we really play the last mile part. Quite a lot of people are local inhabitants and they use the system for convenience. For example, they go to a bar by car and then return by bike because they had a few beers. We want to focus more and more on this last mile. A day-pass type of client. We don’t want to be the alternative for a private bike, there you have Swap-fiets, that is more bike leasing. We are additional to your own bike.

2. Follow-up Q: Do you think there are any ecological motives? A: Off course, that’s why they use it for the last mile. The combination of bus Appendix A. Appendix 82

or train and bike as alternative for the car. Getting to the final destination with public transport has always been a hassle and now people can easily reach their final destination by incorporating the bike sharing into their journey. But I still think people convenience is a bigger motive than the ecological part.

3. Q: How do you think people choose what operator to ride with? A: I think the user interface and use of technology are very important. The system which is the easiest to operate will be chosen over other systems. Now with the introduction of the ’MaaS’ companies, the competition get lower. Through one app people see all bikes available. In the non-MaaS situation you can have a first mover advantage. People are rather lazy so they just pick the first company and only install one app. With the MaaS you need good reliable technology and a good bike. People will choose the best bike when they have two options at the railway station. We see competitors with higher prices but they still work fine, people do not care too much about the price. Once people downloaded an app to work with they will not download a second one to compare prices. But once they notice on the price difference through the MaaS-application they will take into account the price difference more.

4. Q: Why do you think people choose Mobit over the competition, in the Maas-situation? A: We have a good bike and a good price and the system works very stable. Competi- tors have difficulties connecting to the MaaS because their platform is not as good as ours. We have very good technology. Also the convenience of using the application. One example is in our system you do not need the app to close to lock while with the competition you do.

5. Q: I can imagine bikes are left in places where they are unlickely to be picked up again. How is this rebalancing problem solved today? A: Well, there are a few options. First one is a user calls us to report a lost bike. But we also launched a bonus system for this. If the bike is not used after a certain period of time it becomes a bonus bike and users get a free ride if they unlock the bike. If the bike is still not used for a longer period, we will collect the bike.

6. Follow-up Q: How do you collect these bikes? A: We use vans or trucks to pick them up. The repair we can do by cargo bike. We have one partner in Antwerp who helps with the repairing.

7. Follow-up Q: After what period of time does an unused bike become a bonus bike? A: I think after one week it is a bonus bike and it remains a bonus bike for three Appendix A. Appendix 83

weeks and then after this we start collecting.

8. Changed Question: Before the introduction of the bonus bike. Did you have to collect the bikes a lot by truck? A: Yes, a lot. And it is difficult to see now how often the bonus bikes are used. We do not monitor this yet. It would be very interesting to see how effective this system is.

9. Follow-up Q: Do you notice a decrease in trucks being used since the introduction of the bonus bikes? A: No, not yet. We see that the system works, that’s clear. But we have not done the quantitative analysis yet. In the ideal situation we do the maintenance of the bikes at the parking spots or drop zones and we spend less time collecting the bikes.

3. Delicate questions

1. Q: Are the bonus bike brought to life to lower the impact on the environment of the trucks or to lower the operational costs and operational efforts? A: One reason is certainly the lower maintenance cost. Our operations team has to relocate bikes but also maintain them. We want them to spend more time on maintaining and less on rebalancing the bikes. Therefore, we need to find a way to effectively rebalancing the system autonomously. Ideally we have a system where we can incentivise people to bring bikes to zones where there is a need of bikes. We have heat maps now but with the use of dynamic pricing we can optimise demand and supply without using trucks.

2. Q: Why do you think 1 free trip is enough to incentivize the users? A: Good question. No idea actually. Maybe this incentive has to be changed. Maybe we could work with a credit system but that is not the case yet. For now, the free rides look like a good solution.

3. Q: Do you see an increase or decrease in revenue since the introduction of the bonus- bike? A: Let’s say we see a continuous increase in the number of rides, that’s positive. The month of introduction was a very good month. We see and heard that people really appreciate the initiative. Maybe to come back to your question of why we use the bonus bikes. I think we should focus on giving the customer the best experience pos- sible and if that means giving away free rides, that’s okey. Off course it is nice to see that we use less trucks because of that. Appendix A. Appendix 84

4. Follow-up Q: To come back to the previous question. Is it possible to tell if the increase in trips or revenue is because of the bonus bikes? A: We are now in a growing phase so it is hard to tell what causes this growth. We have not done too much research on the effective of the bonus bikes or how often they were used. [We need to be able to define better per zone if it is a good working zone or not and maybe introduce bonus zones. Rather then giving away free rides if you unlock a bike maybe give a free ride if you put a bike in a bonus zone. Average trip is 12’.]

5. Extra Question: Anything else you think would be useful for me to know about the bonus bikes? A: No, I think you know most of it. Maybe that we have a lot of feedback that people really appreciate the bonus bike system and really use it. And we need to do a bit more analysis on it. Bibliography

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