Feasibility Study: Bikeshare System Implementation in Rome

Feasibility Study: Bikeshare system implementation in Rome

Faculty of Civil and Industrial Engineering Department of Civil, Constructional and Environmental Engineering (DICEA) Master Degree in Transport Systems Engineering

Daniel Felipe López Velásquez Matricola 1794090

Relatore Luca Persia

A.A. 2018-2019

ABSTRACT

This thesis develops a feasibility study of a new bikeshare program in Rome. A literature review was done to investigate the state-of-the-art of bike-sharing as well as the trends and related impacts. A comparison between the station-based and the free-floating systems was carried out and the previous experiences in Rome were studied. A context analysis was implemented to study the security, the mobility, the potential barriers to bikeshare and possible strategies to deal with them in Rome.

Initial planning of the bikeshare was defined using a combination of georeferenced spatial analysis, territorial analysis, and multi-criteria evaluation. Five different criteria were taken into account: 1) Medium-Low accessibility zones, 2) Cycle path infrastructure, 3) Population and employment density, 4) Origin and destination trips, and 5) Slope. Using this multi-criteria approach, a global suitability layer was built and the service area of the system was proposed accordingly.

More detailed planning of the system was performed, to establish the location of stations and determine the system size in terms of the number of stations, bikes, and docks. Spatial analysis with Thiessen Polygons was used to assign a specific size to each bikeshare station. Accordingly, a cost- benefit analysis was implemented using the Net Present Value to study the profitability of the project under six different scenarios, i.e. Conservative, Intermediate and Optimistic, with/or without additional revenue streams. As well, a financial analysis was made including the external costs/benefits of the bikeshare, regarding accidents, air pollution, climate change, noise, congestion, and health.

Findings suggest that just in a conservative scenario, negative profitability would be obtained, thus, additional revenue streams as advertisements and sponsorships are recommended to reduce the financial risk of the project. With these additional revenues, profitability would be positive on each one of the scenarios. Financial analysis with externalities confirms the benefits of the project for the society and the environment in every scenario.

Keywords: Bikeshare; e-bike; externality; multi-criteria; suitability; Rome; sustainability; public health.

SOMMARIO

Questa tesi sviluppa uno studio sulla fattibilità di un nuovo programma di bike sharing a Roma. È stata effettuata un’ analisi della letteratura per studiare lo stato dell'arte del bike sharing, nonché le tendenze e gli impatti correlati. È stato effettuato un confronto tra i sistemi basati su stazione e quelli free-floating e sono state studiate le precedenti esperienze a Roma. È stata implementata un'analisi del contesto per studiare la sicurezza, la mobilità, i potenziali ostacoli al bikeshare e le possibili strategie per affrontarli a Roma.

La pianificazione iniziale del bike sharing è stata definita utilizzando una combinazione di analisi spaziale georeferenziata, analisi territoriale e valutazione multi-criterio. Sono stati presi in considerazione cinque diversi criteri: 1) Zone di accessibilità medio-bassa, 2) Infrastruttura delle piste ciclabili, 3) Densità della popolazione e dell'occupazione, 4) Viaggi di origine e destinazione e 5) Pendenza. Utilizzando questo approccio multi-criterio, è stata creata una mappa di idoneità globale e l'area operativa del sistema è stata proposta di conseguenza.

È stata eseguita una pianificazione più dettagliata del progetto, per stabilire la posizione delle stazioni e determinare le dimensioni del sistema in termini di numero di stazioni, biciclette e banchine. L'analisi spaziale con i poligoni di Thiessen è stata utilizzata per assegnare una dimensione specifica a ciascuna stazione di bike sharing. Di conseguenza, è stata effettuata un'analisi costi-benefici utilizzando il valore attuale netto per studiare la redditività del progetto in sei diversi scenari, vale a dire conservativo, intermedio e ottimista, con / o senza flussi di entrate supplementari. Inoltre, è stata eseguita un'analisi finanziaria comprendente i costi / benefici esterni alla condivisione di biciclette, per quanto riguarda gli incidenti, l'inquinamento atmosferico, i cambiamenti climatici, il rumore, la congestione e la salute.

I risultati suggeriscono che solo in uno scenario conservativo si otterrebbe una redditività negativa, quindi si raccomandano ulteriori flussi di entrate attraverso annunci e sponsorizzazioni, così da ridurre il rischio finanziario del progetto. Con questi ricavi aggiuntivi, la redditività sarebbe positiva per ciascuno degli scenari. L'analisi finanziaria con esternalità conferma i benefici del progetto per la società e l'ambiente in ogni scenario.

Parole chiave: Bikeshare, bicicletta elettrica; esternalità; multicriterio; idoneità; Roma; sostenibilità; salute pubblica. 4

ACKNOWLEDGMENTS

I would like to express my special thanks of gratitude to all who contributed to the development of this thesis. To the professor Luca Persia and to Davideshingo Usami for their feedback and valuable comments, and to the professor Cristiana Piccioni for the initial guidance and help.

I would also like to extend my gratitude to my friend Juan David Correa, that contributed to creating the slope map for Rome, and to Paulo Cantillano and Angie Carrillo. Thank you all for your contributions, friendship, and support in the thesis. Also, I would like to thank my roommates Simona, Saro, Elina, and Alice for their reviews and their amazing company.

I must express my very profound gratitude to my parents, my brothers, and my cousin Sebastián, that despite the distance, gave me unconditional support and permanent encouragement through my period of study. This achievement would not have been possible without them. Thank you a lot.

To my mom, dad, and my brothers.

To all those who believe in more sustainable ways to transport.

TABLE OF CONTENTS

1. INTRODUCTION ...... 13 2. BIKESHARE CONTEXT...... 16 2.1. What is Bikeshare ...... 16 2.2. Bikeshare development ...... 16 2.3. Impacts of bikeshare ...... 17 2.4. Bikeshare trends ...... 21 3. PREVIOUS EXPERIENCES ...... 23 3.1. Station-based and Free-Floating bikeshare comparison ...... 23 3.2. Common features of successful bikeshare systems ...... 27 3.3. The e-Bike ...... 29 3.4. Rome’s case: Roma’n Bike and oBike ...... 31 4. ROME CONTEXT ANALYSIS ...... 37 4.1. Security analysis ...... 37 4.2. Mobility analysis ...... 39 4.3. Potential barriers to Bikeshare in Rome ...... 46 4.4. Possible strategies to deal with barriers ...... 49 4.5. Compatibility with current transportation plans ...... 56 5. INITIAL PLANNING ...... 58 5.1. Service area determination ...... 58 5.1.1. Medium-Low accessibility zones ...... 58 5.1.2. Cycle path infrastructure ...... 61 5.1.3. Population and Employment density ...... 63 5.1.4. Origin and destination trips ...... 69 5.1.5. Slope ...... 75 5.1.6. Multicriteria analysis – Analytical Hierarchy Process (AHP) ...... 79 5.1.7. Global suitability ...... 82 5.1.8. Service Area ...... 83 5.2. Bikeshare system type description ...... 85 7

6. SYSTEM PLANNING ...... 89 6.1. Location of stations ...... 89 6.2. System size ...... 93 6.3. Station sizing ...... 95 6.4. Parking replacement ...... 98 6.5. Demand boosting strategies ...... 101 7. COST-BENEFIT ANALYSIS ...... 104 7.1. Costs estimation ...... 107 7.2. Revenues estimation ...... 109 7.3. Net Present Value estimation ...... 111 7.4. Sensitivity analysis ...... 117 7.5. External costs/benefits of bikeshare ...... 118 7.5.1. Accidents ...... 119 7.5.2. Air pollution ...... 121 7.5.3. Climate change ...... 121 7.5.4. Noise ...... 122 7.5.5. Congestion ...... 123 7.5.6. Health: reduction in mortality ...... 124 7.5.7. Financial analysis with external benefits ...... 125 8. CONCLUSIONS ...... 127 9. BIBLIOGRAPHY ...... 131 10. APPENDIX ...... 137

LIST OF FIGURES

Figure 1 Substituted mode by bikeshare system (Fishman, 2016) ...... 18 Figure 2 Average cyclist fatalities per million trips (New York City Department of Transportation, 2017) ...... 20 Figure 3 Growth in bikeshare cities (Fishman, 2016) ...... 21 Figure 4 Bikeshare programs worldwide (STATISTA, 2018) ...... 22 Figure 5 Roma n’ Bike pilot test (Roma'n Bike, 2009) ...... 31 Figure 6 Roma’n Bike service area – Buffer 300m ...... 32 Figure 7 oBike abandoned in Rome. Foto took in Rome the 06/04/2019 at 12:11 ...... 35 Figure 8 Trip Origins ...... 39 Figure 9 Detail zone 31 ...... 40 Figure 10 Trip destinations ...... 41 Figure 11 Detail zones 32 and 20 ...... 42 Figure 12 Desire lines in Rome ...... 43 Figure 13 Detail of desire lines in the city center ...... 44 Figure 14 General trips behavior-Morning peak hour (Roma Servizi per la Mobilità, 2015) ...... 45 Figure 15 “Ciclocarril” in Madrid (Barahona, 2014) ...... 53 Figure 16 Existing traffic-limited zones in Rome ...... 59 Figure 17 Medium-Low accessibility areas ...... 60 Figure 18 Accessibility - Scoring ...... 61 Figure 19 Cycle paths - Scoring ...... 63 Figure 20 Population density - Scoring ...... 65 Figure 21 Population density – Detail on the east side of Rome ...... 66 Figure 22 Employment density - Scoring ...... 67 Figure 23 Employment density- Detail on the city center ...... 68 Figure 24 Density -Population and Employment final scoring ...... 69 Figure 25 Origin trips density – Scoring ...... 71 Figure 26 Detail on origin trips density scoring ...... 72

Figure 27 Trip destinations density – Scoring ...... 73 Figure 28 Detail on destination trips density scoring ...... 74 Figure 29 Trips – Origins and Destinations final scoring ...... 75 Figure 30 Contour lines Rome - 5m ...... 76 Figure 31 Contour lines and slopes ...... 77 Figure 32 Slope- Scoring ...... 78 Figure 33 Slope - General view of Rome ...... 79 Figure 34 AHP scheme ...... 81 Figure 35 Suitability map for bikeshare ...... 83 Figure 36 Service Area ...... 85 Figure 37 Velib’s automated stations (Velib' Metropole, 2019) ...... 86 Figure 38 Bikemi’s terminal and smartcard (Bikemi, 2019) ...... 87 Figure 39 E-bike from Bicimad (Bicimad, 2019)...... 88 Figure 40 Location of stations near metro station’s exits ...... 91 Figure 41 Location after the buffer of 200m was performed, near cycle infrastructure ...... 91 Figure 42 Optimization of stations ...... 92 Figure 43 Bikeshare station location ...... 93 Figure 44 Buffer 500m around stations ...... 94 Figure 45 First Thiessen polygons ...... 95 Figure 46 Second Thiessen polygons ...... 96 Figure 47 Bikeshare stations sizing ...... 98 Figure 48 Parking replacement in Madrid. Comparison years 2014 and 2018 ...... 99 Figure 49 Parking replacement in Madrid. Comparison years 2008 and 2018 ...... 100 Figure 50 Parking replacement in Mexico D.C. Comparison years 2011 and 2017 ...... 100 Figure 51 Parking replacement in Barcelona. Comparison years 2014 and 2018 ...... 101 Figure 52 Area served in zone 30 ...... 105 Figure 53 Calculation parameters - HEAT ...... 125 Figure 54 HEAT assessment example – Intermediate scenario ...... 125

LIST OF TABLES

Table 1 Theft rates of private cars and robbery rates by country ...... 37 Table 2 Scoring rule for population density and employment density ...... 64 Table 3 Breakpoints for population and employment density scoring ...... 64 Table 4 Scoring rule for trip origins-destinations ...... 70 Table 5 Breakpoints for trip origins-destinations scoring ...... 70 Table 6 Pairwise criteria comparison ...... 82 Table 7 Criteria weights ...... 82 Table 8 Resumed information Bikeshare ...... 98 Table 9 Reduction factors for OD trips ...... 105 Table 10 OD trips estimation ...... 106 Table 11 Considered scenarios ...... 107 Table 12 Capital investment ...... 108 Table 13 Cost of staff ...... 108 Table 14 Operation and Management costs ...... 109 Table 15 Proposed revenue scheme ...... 110 Table 16 Annual income for memberships ...... 110 Table 17 Trips and members statistics from Bicimad-Madrid ...... 111 Table 18 Revenues estimation according to each scenario...... 111 Table 19 Revenues and Costs resume table ...... 112 Table 20 Net Present Value – Conservative scenario ...... 113 Table 21 Net Present Value – Intermediate scenario ...... 113 Table 22 Net Present Value – Optimistic scenario...... 114 Table 23 Net Present Value – Conservative scenario with sponsorships/advertisements 115 Table 24 Net Present Value – Intermediate scenario with sponsorships/advertisements 116 Table 25 Net Present Value – Optimistic scenario with sponsorships/advertisements ..... 116 Table 26 Sensitivity Analysis - NPV variation with changes in Discount Rate ...... 117 Table 27 Modal shift by scenario ...... 119 Table 28 Passenger kilometers per year by scenario ...... 119

Table 29 Average external accident costs for land-based modes for Italy (European Commission, 2019) ...... 120 Table 30 Cyclist fatalities estimation ...... 120 Table 31 Accident costs by scenario ...... 120 Table 32 Average air pollution costs for land-based modes for Italy (European Commission, 2019) ...... 121 Table 33 Air pollution costs by scenario ...... 121 Table 34 Average climate change costs for land-based modes for Italy (European Commission, 2019) ...... 122 Table 35 Climate change costs by scenario ...... 122 Table 36 Average noise costs for land-based modes for Italy (European Commission, 2019) ...... 123 Table 37 Noise costs by scenario ...... 123 Table 38 Average congestion costs for Italy (European Commission, 2019) ...... 123 Table 39 Congestion costs by scenario ...... 124 Table 40 External cost reductions resume ...... 126 Table 41 Resume of external benefits by scenario and NPVs ...... 126

1. INTRODUCTION

Bikeshare is nowadays an emerging transport option around the world, as its success has been proved already in many implemented systems, that goes along with a numerous quantity of benefits for the society, the environment and the economy, including; an improvement in the air quality, traffic congestion reduction, serves as a complementary urban transport mode, improves the health of its users, enhances the safety of the cyclists, boosts the use of bike and contributes to the further development of the city’s bike network infrastructure (Montezuma, 2015).

The main objective of the thesis is to study the feasibility of a new bikeshare system implementation in Rome, based on; the previous experiences, the local bike context in Rome, an initial planning of the system to determine the service area, a more profound planning to determine the system size and the cost-benefit analysis, considering the financial sustainability of the project and the externalities’ costs reduction.

In Chapter 2 the Bikeshare context will be studied. For that, the general concept of Bikeshare will be explained, as well as it’s development and evolution throughout the years, from the first generation up to the fourth generation of bike-sharing. Afterward, the impacts of the implementation of Bikeshare in the cities and its trends will be exhibited.

In Chapter 3 an analysis of previous experiences will be presented. For that scope, a comparison between Station-Based and Free-Floating Bikeshare systems is done, considering the global previous experiences. Next, the common features of successful bikeshare are presented, according to the Bikeshare Planning Guide of the Institute for Transportation & Development Policy (versions 2013 and 2018). Then the concept of the e- bike will be presented, and it’s potential is explained. Finally, the previous experiences in Rome with Roma’n Bike and oBike are analyzed.

In Chapter 4, a context analysis of Rome is performed, studying the security issues related to vandalism and theft, and making a comparison with other cities in the world. Next, the mobility in the city is analyzed, studying the OD matrix and the zones with higher trip generation and attraction, the strongest OD relations trough desire lines, the mobility relation of Rome and the province, the current modal split and the performance of the private and public transport services. Next, the potential barriers that could affect a new bikeshare implementation in Rome will be analyzed as well as a set of strategies in order to deal with them, to increase the success probability of the system.

Next, in Chapter 5, initial planning is done, following the guidelines established in "The Bikeshare Planning Guide” of ITDP, editions 2018 and 2013. In this section, the service area is determined, using georeferenced spatial analysis and territorial analysis combined with multi-criteria evaluation in order to create a suitability layer for bikeshare location. For this scope, five different criteria were taken into account; 1) Medium-Low accessibility zones, 2) Presence of cycle-paths infrastructure, 3) Population and employment density, 4) Origin and destination trips, and 5) Slopes. The criteria layers were combined and trough the Analytical Hierarchy Process a final scoring was performed, which results in the global suitability layer. Based on that suitability layer, the Service Area was proposed.

In Chapter 6, a more specific system planning is performed, following as well the guidelines of ITDP’s “Bikeshare Planning Guide". In that sense, the location of the stations is purposed, by using the tool ArcGIS and following the recommendations from NACTO’s Bikeshare station siting guide. Next, a system size calculation is performed to determine the number of bikes and docks, according to the international experience of the most successful bikeshare systems. In the next section, the station sizing is performed by using spatial analysis with Thiessen Polygons. After that, the parking replacement done in other cities is presented and some demand-boost strategies will be purposed.

In Chapter 7 the Cost-Benefit analysis is presented. Therefore, in the first two sections the costs and revenues estimations are shown. Next, the financial evaluation through the Net Present Value (NPV) is presented for six different scenarios; Conservative, Intermediate

14 and Optimistic, with/or without additional revenue streams (each one of them). After, a sensitivity analysis is performed in order to check how the results of NPV vary with changes in the discount rate. Then, the external benefits of bikeshare are calculated in the final sections of the chapter, regarding; accidents; air pollution, climate change, noise reduction, congestion reduction, and public health. Finally, a calculation of the NPV is done again considering the external benefits.

In Chapter 8 the conclusions of the thesis are given, in Chapter 9 the bibliography is presented and in the final section corresponds to the Annex, with specific maps with a higher resolution (as the ones from the station locations) and additional information.

2. BIKESHARE CONTEXT

2.1. What is Bikeshare

Bike share is an emerging urban transport option based on the temporal rental of bicycles without the need to possess one. It is intended mainly for trips that are too far to walk, but not so distant to justify waiting for transit, as well it represents a much cheaper and sustainable alternative than private vehicles.

A bikeshare system includes a set of stations distributed across the city, several bikes and docks where it is possible to unlock and lock them. These bikes normally have adapted and less common components in order to deal with theft and the reselling of the parts. Also, there are systems without stations, called free-floating, where the bikes can be found via GPS, taken and then left again in any part inside the service area.

However, bikeshare includes much more than the bikes themselves, indeed they might represent just between 10-15% of the implementation costs and maintenance (Montezuma, 2015). A bike-sharing system includes; a management and administrative structure, pricing structures, identification of users, a control and monitoring center, redistribution logistics, a maintenance system, and financing.

2.2. Bikeshare development

Bikeshare has had a very important evolution through the years, from the first generation of bikes that in general did not succeed, to the fourth generation bikeshare systems that exist nowadays in some cities.

The first generation of bikeshare was launched in Amsterdam in the 1960s with an idea called Witte Fietsenplan. It consisted of a set of bikes that were painted in white, which could

16 be taken and released anywhere by any user, without any cost. However, as the bikes were left with no kind of lock and there was no control, the amounts of thefts and damages to bikes made the system fail quickly.

In the 1990s the second generation of bikeshare included the use of bike docking stations inside a determined service area. The stations included a coin-deposit system and required users to pay a fee in order to unlock the bike. However, the minimal deposit was not enough to reduce significantly theft. Bycyklen, a second-generation system founded in 1995 in Copenhagen, estimated that 300 bikes (or 15% of the fleet) were lost to theft in 1996 (Alta Planning + Design, 2013).

The third generation started with the launch of systems as “Vélo à la carte”, which was introduced on the 6th of June of 1998 in the city of Rennes, France. This generation is characterized by the full automation of stations, the use of integrated information technology, credit card transactions and the use of RFID chips (radio-frequency identification). Systems of this type were now able to track user information and to have a cost recovery system in case that the bikes were not returned or suffered from vandalism.

Finally, the fourth generation of bikeshare includes elements as electrical bikes, tablets, and battery charging at stations. As well, the systems include solar-powered stations and wireless communications that allow a higher capacity to change the station locations if needed. The information about the number of bicycles taken and returned, about the availability of docks and reports generated by the informatics systems at each station, is a very powerful tool used for the organization of the logistics operations, such as balancing the availability of bikes in diverse stations and the replacement of the damaged bicycles.

2.3. Impacts of bikeshare

One of the main impacts of bikeshare is mode substitution and car use reduction. In a multi- city analysis of bikeshare’s impact on the mode substitution, Fishman (2016) gathered the

17 mode substitution information of the systems of Melbourne, Brisbane, Washington D.C., Minneapolis/St. Paul and London.

Figure 1 Substituted mode by bikeshare system (Fishman, 2016)

The results show that in every bikeshare system there was a substitution of trips formerly made by cars. However, mainly the trips previously made by public transport and by walk are the ones substituted by the bikeshare. Thus, the conception of the bikeshare as a complementary mode and not as a replacement for public transport is desirable. The goal of bikeshare is to reduce the number of trips made by private vehicles.

Bikeshare helps to deal with the pollution generated by private vehicles. American cars emit about one pound of CO2 per mile, thus, a bicycle commuter who rides four miles to work, five days per week, avoids 2,000 miles of driving (in the EE.UU) and nearly 2,000 pounds of CO2 emissions, each year. This amounts to a 5% reduction on the average American’s carbon footprint (Gardner G., 2010).

As well, bikeshare increases the connectivity of the cities as it helps to deal with the last mile problem, being a very efficient alternative for short distances. The connectivity of a city can be boosted with good integration between transit and bikeshare. According to Gardner &

Gaegauf (2014), 79% of surveyed users of BIXI agree or strongly agree that the bikeshare system improved the connectivity of the Montreal public transit system, 67% in the case of BIXI in Toronto and 81% of Nice Ride in Minnesota.

Health benefits are another important impact of bikeshare. This transport mode provides a simple way to incorporate moderate exercise into the daily routine. One of the major benefits for men comes from the reduction in ischaemic heart disease and for women from a reduction in depression (Gardner & Gaegauf, 2014). According to Gotschi (2011), the bicycle investments in the range of $138 to $605 million in Portland, Oregon, would result in a health care cost savings of $388 to $594 million by 2040, fuel savings of $143 to $218 million, and savings in terms of statistical lives of $7 to $12 billion. The benefit-cost ratio for health care is about 3.8 to 1 (Gotschi, 2011).

On another hand, bikeshare might contribute to an overall increase in cycling safety. Even though it seems counter-intuitive, an increment in the bicycle ridership can diminish the number of fatalities. According to Jacobsen (2003), a motorist is less likely to collide with a person walking or bicycling if more people do it, there is safety in numbers. The theory suggests that the more cyclists there are on the road, the more attentive drivers become to their presence and cycling becomes safer. It is possible to appreciate one example of this theory in New York City, according to the following figure.

Figure 2 Average cyclist fatalities per million trips (New York City Department of Transportation, 2017)

The strong correlation between the increase of cycling and the drop in cycling risk in New York suggests that the safety in numbers dynamic might have occurred there. The estimated annual cycling trips increased from 51 million per year from the period 1996-2000 to 134 million in the period 2011-2015 (increase of 165%) while cyclist fatalities per 100 million trips fell from 44.2 in the 1996-2000 period to 12.8 in the 2011-2015 period (drop of 71%) (New York City Department of Transportation, 2017).

Moreover, the launch in 2013 of the bikeshare system in New York, Citi Bike, coincided with a drop in the cyclist KSI (number of cyclists killed and severely injured). Cyclist KSI declined 17%within the bikeshare service area after the first year of operation, despite the 8.2 million trips recorded during the same period (New York City Department of Transportation, 2017).

2.4. Bikeshare trends

After the first failure of the Witte Fietsen program (White Bikes) in 1965 in Amsterdam, Bikeshare experienced little growth, until technological advancements designed to reduce the threat of vandalism and theft emerged. In the period from 2004 to 2014, the number of cities operating a bikeshare program increased from 13 to 855 (Fishman, 2016).

Figure 3 Growth in bikeshare cities (Fishman, 2016)

The amazing growth of bike-sharing observed in Figure 3 can be explained by the successful implementation of programs as Velo’v in Lyon, France, in 2005 and Vélib’ in Paris in 2007. The astonishing trend seems to continue in the most recent years, as it can be evidenced in the U.S., where the total trips taken in station-based bikeshare have increased from 321 thousand in 2010 to 36.5 million in 2018 (NACTO, 2018).

According to Metrobike’s bike-Sharing Blog, as of May 2018, 1,608 bike-sharing programs were operative worldwide, with a global fleet of 18,200,000 public bikes. As of May of 2018, the global fleet of public bicycles was 19 times larger than in 2014, and there were 12 times more programs than in 2008.

Figure 4 Bikeshare programs worldwide (STATISTA, 2018)

According to Credence Research (2018), the global bikeshare services market is expected to expand at a CAGR (Compound Annual Growth Rate) of 12.5% during the forecasted period 2018-2026. Factors as global warming, traffic congestion, and high-cost shuttles, along with notable popularity and acceptance among people, contribute to the choice of the users to use the bikeshare services and therefore to the growth of the market.

3. PREVIOUS EXPERIENCES

3.1. Station-based and Free-Floating bikeshare comparison

According to the Institute for Transportation & Development Policy (ITDP) (2018), the main strengths of the Station-based system for the city are; the longevity of the used infrastructure; a well-managed public space, with a direct involvement of the city in the siting of stations; the possibility to exploit the advertisement within the space provided by the physical stations, which can generate significant revenues for the system. For users the main advantages would be; the affordability, as the long-term users could benefit from pricing schemes as the annual memberships; smartphone alternatives, as the stations provide wayfinding instructions for those users without a smartphone; reliability, as users can find a bike even without an internet connection.

On the other hand, the main strengths of the Free-Floating bikeshare for the city would be; low capital costs, as the upfront costs are reduced without the need for stations; scalability, as more bikes can generate more trips; robust trip/usage data, onboard GPS can generate valuable trip data. For users, the main benefits would be; flexibility, as a trip can end anywhere inside the service area; convenience, as it is possible to locate and reserve a bike from the app (ITDP, 2018).

For the Station-based bikeshare the main weaknesses for the city are; high capital costs for station infrastructure and maintenance; high operating costs, as rebalancing accounts for 50% of the operation costs. For users the main disadvantages are; accessibility, as it is only a viable option for those who live or work close to the service area; Bikes/docks non- availability, as the user could find a full or empty station (ITDP, 2018).

On the other hand, the Free-Floating’s main weaknesses for the city are; public space impacts, as users have the possibility to leave the bikes everywhere including the sidewalks; inconsistent availability, bikes could end up concentrated in the downtown core, with fewer

23 available in other areas. For users the main disadvantages are; it is expensive for consistent riders, as multiple-operator, per trip-only pricing model limits the provision of annual memberships; accessibility, it may be difficult to find and unlock a bike without a smartphone (ITDP, 2018).

Even though it seems more convenient for a user the Free-Floating system, users need to locate the bike every time as they will not be in the same place, having to rely on the use of the smartphone and the internet connection. On occasions, bikes could be located at far walking distances enough to disincentive the trip on the bike. To this fact it should be added that the bike found could not be in a good state, (e.g. not working brakes) and in general there is not the possibility to choose another bike nearby in better conditions as it is possible with the Station-Based system. As well, Station-Based systems have the possibility to incorporate surveillance cameras in docking-stations and in general permit better control of bikes, which is helpful against theft and vandalism. However, it is clear that Station-Based systems as well don’t escape from the theft and vandalism problems. As of July 2008, 3.000 Velib’ bicycles (14% of the fleet) were stolen, about twice as the initially estimated by the operator JCDecaux (The New York Times, 2008). In 2009, JCDecaux declared that 7.800 bicycles had been stolen and suggested that it could be the fault of the locking mechanism’s design, as users remained unsure of whether their bike was properly docked (The London Times, 2009).

In the U.S.A, according to NACTO (2018), people took 36.5 million trips on station-based bikeshare systems in 2018, while the dockless non-electric pedal bikes (which proliferated rapidly in 2017) have largely disappeared, with just 3 million trips in few cities in 2018. As well, the number is still expected to decrease in 2019 due to the disappearance of most dockless bikeshare programs in the U.S.A.

According to the information presented by NACTO (2018), it is possible to observe a steady growth of the station-based bikeshare, with a positive increase every year. As a reference, 321 thousand trips were made in 2010, then in 2014 the number increased to 18 million and in 2018 arrived at 36.5 million. Bike share systems as the ones of Washington D.C., Metro

Boston, and the Bay Area increased the number of stations of their systems, resulting in increased ridership. In total, as of 2018, there were 57,000 station-based bike share bikes in the U.S.A, representing an increase of 9% with respect to 2017. Meanwhile, the dockless bikeshare has had a strong fluctuation during 2017 and 2018, with most of the systems being quickly replaced by e-scooters.

In China, after the introduction of the Free-Floating systems at the end of 2016, there was a huge growth in two years, however, the number of shared bikes with disorderly parking, serious damage, and the over-supply became a new urban management issue (Sun, 2018). Free-floating experiences have shown that there is a very strong impact in the public space, as users have complete freedom to leave the bikes haphazardly.

According to Sun (2018), the implementation of Free-Floating bikeshare systems in China has shown great advantages in terms of flexibility for short trips, solving the commuter “last-mile” problem. However, it has also generated negative consequences such as blocked sidewalks and vandalism. As well, oversupply has led to a graveyard of bikes, and general concerns about quality control, maintenance, and management of these Free-Floating systems. In words of Sun (2018), “if there is no efficient way to avoid the bad treatment towards shared bikes and abasement of public space, it may be more of a curse than a blessing”.

The rapid growth and fierce competition of Free-Floating companies in China made that by the End of 2017, over six from over the 30 companies that operated, went bankrupt. As a consequence, more than USD 150 million in deposits could not be refunded to users (Sun, 2018).

The value contribution of the Station-based systems was over 86% in the global bike-sharing services market in 2017. Even though there was an increase in the Free-Floating bikes in the last few years, the station-based bikeshare accounts for over 78% of the total trips worldwide. Even if Free-Floating bikes seem to be more convenient for users, it gives major headaches to both local federal agencies (who must control and manage several bicycles on

25 pedestrian paths) and system operators (who must rebalance bikes to meet demand). As well, Free-Floating bikeshare posses challenges related to bikes durability, maintenance, and economic sustainability, that station-based systems can provide more effectively (Credence Research, 2018).

At the European level, the panorama of Free-Floating experiences has not been positive. In Italy, the Free-Floating service oBike arrived in Rome on December 2017. In August 2018 the main company in Singapur declared bankruptcy and at the end of 2018 operations already ceased in Rome. Gobee, a bikeshare operator from Hong Kong, arrived at the end of 2017, operating in the Italian cities of Florence, Roma, and Turin. The 15th February 2018, Gobee declared the cease of operations at the European level, including France and Belgium. In a final announcement, Gobee established that on average 60% of their European fleet suffered damages, vandalism and were an object of privatization. In a most recent case, OFO closed operations on the 1st of April of 2019 in Milan, after the municipality demanded the payment of the concession fees of 2019, without any success.

On the other hand, Station-Based systems such as Bicing in Barcelona and Velib’ in Paris, have proven to be successful models in Europe and nowadays, with more than 10 years of experience continue to operate. Bicing was inaugurated in March 2017, with 1.500 bikes and 96 stations. One year later, the system reached 6.000 bicycles and 384 stations (La Vanguardia, 2017). At the end of 2007, 15.000 users were expected, but subscriptions went over 100.000 (Montezuma, 2015). Bicing, as for 2019, is implementing an expansion plan, and by the end of the year the system will count with 6.000 mechanical bikes, 1.000 electrical and over 425 mixed docking-stations (Bicing, 2019). Velib opened in July 2007 with 10,648 bicycles and 750 stations (ITDP, 2007), as for 2019, it has about 1.350 docking stations and 40.100 docks (O'Brien, 2019).

Free-Floating is a model that has failed repeatedly in Europe, with improvised operators uncoordinated with the municipalities and poor management of bikes. It resembles the problems already lived with the Witte Fietsenplan, going back to the first generation of bikeshare systems. As well, negative experiences with Free-Floating systems can affect the

26 image of biking and leave a negative way of thinking about the impossibility to implement a working bikeshare system in the cities. In addition, Station-Based bikeshare has a bigger potential to exploit e-Bikes, as the bikes can be directly charged in the stations. In the next chapters, the potential of e-bikes will be presented.

3.2. Common features of successful bikeshare systems

According to ITDP (2013), most successful systems share some common aspects: • Bikes with specially designed parts that discourage theft and resale. • A dense network of stations in the service area, with an average spacing of 300m. • A fully automated locking system that permits users to take a bike and to lock it again easily in the bikeshare stations. • Real-time monitoring of station occupancy rates, through wireless communications. • A wireless tracking system, as the RFID (Radio Frequency Identification Device), that helps to identify the pick-up point, the return point, as well as the information of each user. • A pricing structure that incentivizes short trips. • Real-time user information, through smartphones, a web site, and on-site terminals.

As well, ITDP (2013) recommends a minimum system coverage area of 10 km2, but a much larger one is desirable for a successful system. Bicing, the bikeshare system operated in Barcelona by Clear Channel, as for 2018, had a service area of 53 km2, Ecobici in Mexico D.C. of 54 km2, Velib in Paris about 155 km2, Bikemi in Milan 53 km2, Citi Bike in New York City 129 km2, and dublinbikes in Dublin 15 km2 (ITDP, 2018).

On the other hand, an ideal station density is from ten to sixteen stations per square kilometer (ITDP, 2013). However, existing Station-Based bikeshare systems have a slightly lower density; Bicing has a station density of 8.8 stations/km2, Velib of 7.7 stations/km2, Bikemi of 5 stations/km2, Ecobici of 8.9 stations/km2, Citi Bike of 5.8 stations/km2, and dublinbikes of 6.8 stations/km2 (ITDP, 2018). It is important to mention that the density

27 might not be the same in the whole service area, with higher values in the high-density zones and lower in the less dense areas.

The station density parameter is quite relevant, as increasing it might result in increased market penetration (trips per resident). An appropriate value will assure the users that there will be a station at a convenient walking distance from them, for the origin as well as for the destination. A low station density can easily disincentivize the use of the bikeshare system. For example, as for 2018, Ecobici with its 8.9 stations/km2had a market penetration of 105 trips per 1.000 residents, while Bikemi with 5 stations/km2 just about 4 trips per 1.000 residents. As well, Velib with 7.7 stations/km2 had a market penetration of 35 trips per 1.000 residents, while some American cities as Madison (2.3 stations/km2), Boston (2.3 stations/km2 ) and Atlanta ( 2.4 stations/km2 ) had a significantly lower market penetration; 10 trips per 1.000 residents, 3.8 and 5 correspondingly (ITDP, 2018). Other factors might contribute to increased market penetration, but it is clear that a higher station density will play a significant role in it.

In a study of bikeshare usage barriers made in Brisbane and Melbourne, it was determined that the second reason for not using Citycycle was “docking stations are not close enough to my home” and the fifth reason was “docking stations are not close enough to my work” (Fishman, Washington, Haworth, & Mazzei, 2014), which is an aspect that can be improved directly by reducing walking distances with a higher station density.

As well, an adequate bikes/resident ratio is from 10 to 30 bikes for every 1.000 residents within the service area. This ratio should be large enough to meet demand, but not so much as to have less than 4 uses per bike per day (ITDP, 2013). In Mexico D.C., Ecobici has about 6.500 bikes in its service area, covering about 334.806 inhabitants. This results in a bikes/resident ratio of about 19 bikes per 1.000 residents and 4.6 daily trips per bike. Dublinbikes, as for 2018, had a ratio of 13 bikes per 1.000 residents with 5.6 daily trips per bike. Bicing had a lower ratio of 4 bikes per 1.000 residents with higher daily trips per bike (6.4) as Citi Bike with 6 bikes per 1.000 residents and 6.4 daily trips per bike (ITDP, 2018).

In addition, the docks per bike ratio should be between 2 and 2.5 docking spaces for every bike (ITDP, 2013). Dublinbikes, as for 2018, had a ratio of 2 docks per bike and Citi Bike in New York of 2.4. Other systems, as Bicing and Ecobici had a slightly inferior ratio of 1.7 docks per bike (ITDP, 2018). Having more docks than bikes is essential to ensure parking availability for users. A low value of the docks per bike ratio would likely result in a need to rebalance the stations more frequently, in order to avoid saturation. A much larger value could result in unnecessary use of space and costs.

As for the performance, an average of four to eight daily uses per bike is recommended for good system efficiency. A good market penetration would be on average one daily trip per 20 to 40 residents. As for the stations, a theft-proof locking mechanism, clear signage and use instructions, and an easy bike check-in and check-out process are the common characteristics of a good bikeshare system. As for bikes, they should be durable, attractive and utilitarian (ITDP, 2013).

3.3. The e-Bike

E-Bikes are pedal-assisted bikes that provide a battery-powered boost to riders as they pedal. It can improve user comfort as it reduces barriers to cycling, like sweating, fatigue, and long-distance trips. The maximum speed of e-Bikes is around 30 kilometers per hour (ITDP, 2018), which allows a better integration in contexts where bikes have to share the same lanes with cars.

E-bikes offer the potential to increase attractiveness to those who previously didn’t consider the use of the bike as an option. Long trips, difficult topography, excessive heat, and other factors related to physical effort can act as barriers to transport cycling (Heinen, van Wee, & Maat, 2010). As well, many cities with bikeshare have experienced rebalancing issues, as users ride downhill and show a reluctance to return bikes located at a higher elevation. Thus, e-Bikes may assist both users and operators and may be especially applicable in hilly, hot or disperse cities (Fishman, 2016).

According to NACTO (2018), across the U.S.A., e-bikes are used on average twice as frequently as pedal bikes in the cities that added e-bikes to their station-based fleets. As well, in New York’s bikeshare system, e-bikes are used up to 15 times a day during high ridership months, compared to 5 times a day for pedal bikes. With increasing popularity, cities are adding rapidly e-Bikes to their fleets. In San Francisco, e-Bikes comprised a third of the fleet by the end of 2018, Minneapolis plans to transition completely to e-bikes and New York is planning that one-third of its fleet become electric (NACTO, 2018).

Global trends seem to indicate the imminent rise of the e-Bike, with more cities every year doing a transition towards it. The Bicimad system in Madrid, with about 2.028 bikes, 4.116 docks and 165 stations operates 100% with e-bikes (Bicimad, 2019). In Mexico, ECOBICI has 480 stations and about 6.800 bicycles, from which about 28 stations and 340 bikes are part of the new e-bike generation (ECOBICI, 2019). Bicing in Barcelona, as for 2019, is planning to expand its current fleet of 6.000 pedal bikes and 300 e-bikes to 6.000 mechanical and 1.000 electrical bikes (Bicing, 2019). In Italy, BikeMi has 3.650 traditional bikes and 1.150 electrical operating in Milan (BikeMi, 2019).

According to Campbell et al. (2016), e-bikeshare takes more users from “sheltered modes” than conventional bikeshare. That means, that there are higher chances to have a modal shift from car to e-bike, than it would happen with conventional bikeshare. Normal bikeshare is more sensitive to measures of effort and comfort than e-bikeshare, so its demand is strongly negatively impacted by distance, temperature, poor air quality, and precipitation. On the contrary, E-bikeshare is less sensitive to longer trips, worse air quality, and bad weather, but still affected by precipitation.

Considering the increased attractiveness, a lower possibility of rebalancing problems due to the presence of hills in Rome, a normally higher ridership compared to normal bikes, the global trends and that Rome has pronounced slopes and high temperatures during summer, e-bikes might produce higher benefits in a new bikeshare program.

3.4. Rome’s case: Roma’n Bike and oBike

A bike-sharing pilot test started in Rome on June 13th, 2008, called Roma n’Bike and was conceived to last an initial period of six months. Operated by the Spanish society Cemusa, it consisted of a system of 19 stations inside the limited traffic zone, 271 docks and 200 bicycles (Roma'n Bike, 2009). The following figure shows the distribution of the stations.

Figure 5 Roma n’ Bike pilot test (Roma'n Bike, 2009)

The service time was every day from 7 am to 11 pm and there was no cost in the first half- hour of use, the second half-hour had a cost of 1 eu, the third one 2 eu and afterward 4 eu for each additional half-hour. As well, the check-in and check-out of the bikes were performed using a Smartcard, that could be obtained in the touristic information points, where it was necessary to leave a 30 eu caution deposit (Roma'n Bike, 2009). In the initial

31 agreement, the costs of activation, management, and maintenance were covered entirely by Cemusa.

The operation of Roma’n Bike concluded after a 3-month extension of the initial duration, that did not lead to a transitory or definitive contractual regulation of the relationship with the municipality of Rome.

Figure 6 Roma’n Bike service area – Buffer 300m

Using a buffer of 300m around each station, it can be determined that the service area of Roma’n Bike was 2.68 km2, representing a station density of 7.09 stations/km2, which is slightly lower to the one in Paris with 7.7 stations/km2. However, the service area was much lower than the recommended by ITDP (2013), of 10 km2 minimum. This might have limited the success of the program, as a larger area is needed to serve a representative amount of origin-destination trips.

An adequate coverage area is very important, as small programs do not work. Successful programs that produce real transportation, economic and health benefits depend on a high concentration of stations and widespread coverage. Frequently, financial viability increases with bigger programs (NYC Dept. City Planning, 2009). In general, the first phase has to be sufficiently large to ensure meaningful origins and destinations and sufficiently dense to ensure reliability and convenience to users. Small pilots are not convenient for bikeshare, as the small scale can limit the usability of the system because of poor coverage and the lack of bike availability, which may result in damages to the public perception of bikeshare as a viable mode of transport (ITDP, 2013).

Two examples of unsuccessful pilots were Washington D.C.’s Smartbike system and Rio de

Janeiro’s Samba bikeshare. Launched in August 2008 in Washington D.C., the Smartbike system consisted of 10 stations and 120 bikes. Because of the small number of bikes and stations, the long distances between stations, and the limited operating hours, the program was barely utilized and very unsuccessful (DePillis, 2010). In September 2010, Washington D.C. relaunched the system with Capital Bikeshare, with a larger size (1,100 bicycles and 116 stations) and more operating hours (24 hrs/day, 7 days/week) (ITDP, 2013). Nowadays, Capital Bikeshare continues to operate with 4.300 bikes and more than 500 stations across 7 jurisdictions (Capital Bikeshare, 2019).

By doing a quick analysis in ArcGIS it is possible to establish that the service area contained approximately 30.193 residents, so the bike/residents ratio was about 6.62 bikes per 1.000

33 residents, much lower than the ratio of Ecobici in Mexico (19 bikes per 1.000 residents) and lower than the recommendation of ITDP (2013) of 10-30 bikes per 1.000 residents. This might have limited the capacity of the bikeshare system to attend effectively the demand and as it has been studied, in Rome, the availability of bicycles at stations is the main factor influencing the choice of users to use or not the service (Tripodi & Persia, 2015). As well, the docks/bike ratio was 1.36, much lower than the recommended by ITDP (2013) of 2-2.5 docks/bike. This might be translated into possible difficulties of users to find a space to leave the bike or even having to go to another station in search of an available dock.

On another hand, oBike officially launched in Singapore in February 2017, with about 1.000 bikes. There was almost an immediate impact on public space due to the indiscriminately parked bikes, so the Land Transport Authority (LTA) of Singapore created about 750, marked in yellow, parking spaces. In October 2017, about 5 bikeshare companies signed a memorandum of understanding with the LTA, in which they would have to remove the incorrectly parked bikes within a half-day, as well as educating users and implementing geo-fence technologies. At that point, the company expanded rapidly to several cities around the world and in December 2017, oBike arrived in Rome, with about 1.200 free- floating bicycles.

During the operation time of oBike in Rome, several vandalism acts took part, as the removal from seats, the removal of tires and severe damage to bikes. One of the most known cases was when a bike was thrown by a young woman from the Ponte della Musica to the Tevere river. As well, as it happened in Singapore, there was indiscriminate parking with effects on public space. Some bikes were left in historical monuments, in the middle of the sidewalks or in the middle of parks.

In March 2018, the Parliament of Singapore passed the Parking Places (amendment) bill including measures as the review of the fleet every six months and a new licensing regime for bikeshare operators. By June 2018, the company announced its closure, as new regulations by the LTA started to arise; “oBike is announcing its decision to cease operation in Singapore as a result of difficulties foreseen to be experienced to fulfill the new 34 requirements and guidelines released by Land Transport Authority towards dockless bicycle sharing in Singapore”.

In other cities around the world, oBike was generating more troubles than solutions. In Melbourne, were initially 1.250 bikes were put in service, about 40 bikes were fished out of the Yarra River, showing this time not only a public space impact but a rather serious environmental problem. In June 2018, the company retired from Melbourne, as the city impounded the bikes, threatened with fines of $3.000 for bike littering and told residents not to use them (The Guardian, 2018). Afterward, there were massive complaints on social media about the refusal of the company to return the users' deposits.

After the cease of operations in Singapore, oBike Italia still had some funds to operate a couple of months while waiting for a new investor. However, by the end of 2018, it also announced its closure in Rome. As for June 2019, bicycles haven’t been retired completely from the streets, despite the several warnings of the Municipality of Rome to oBike.

Figure 7 oBike abandoned in Rome. Foto took in Rome the 06/04/2019 at 12:11 35

Failure of oBike in Rome was attributed by media and the general public mainly to the citizen’s numerous acts of vandalism, however, the fact that the company had difficulties at a broader scale (starting from its parent company in Singapore), was barely mentioned. Failure of oBike was not exclusive of Rome, it failed in numerous other cities contemporaneously, as in; Zurich, Munich, Melbourne, Kuala Lumpur, Brussels, and several others, where strong impacts to public space were also perceived. Thus, the final outcome of the oBike’s experience in Rome was a big damage to the image of biking and a remaining feeling of the impossibility for the implementation of a bikeshare program in the city.

4. ROME CONTEXT ANALYSIS

4.1. Security analysis

A first insight on the security level can be given by the theft rate of private vehicles and the robbery rates by country. In Table 1 the information of the United Nations Office on Drugs and Crime (2019) is resumed, taking into account that the rates refer to the number of police- recorded offenses and counts per 100,000 population. Theft and robbery differ in the sense that the latter implies the use of violence, force or intimidation.

Table 1 Theft rates of private cars and robbery rates by country COUNTRY Theft of private cars (2016) Robbery (2017) Mexico 90.57 2.24 USA 237.56 3.19 UK (England and Wales) 149.59 0.77 Italy 264.08 0.31 Spain 68.26 0.67 France 249.59 1 Colombia 81 1.2 Note. Data from (United Nations Office on Drugs and Crime, 2019)

From Table 1 it is possible to appreciate that the theft rate of private cars in 2016 in Italy (264.08) is quite significant, comparing to countries like Spain (68.26). By analyzing the information of the United Nations Office on Drugs and Crime, it is possible to establish that, as for 2016, Italy had the highest theft rate of private vehicles in Europe and the third worldwide after Bermuda and Uruguay. It is a very important information to consider, as the theft of private vehicles reflects a behavior that could be replied as well in the theft of public bicycles, therefore, serious measures should be taken for dealing with theft in a new bikeshare system in Rome.

As for the robbery rates, it is possible to appreciate from Table 1, that the rate is significantly lower in Italy (0.31) compared to countries as USA (3.19) and Mexico (2.24), that means, that

37 there would be a low probability that a person violently would take a bike from a user while in a trip, which is normally a much more difficult problem to control and to give immediate solutions. Thus, security should be focused on the locking-systems of stations and in the implementation of totems with integrated security cameras, for reducing the theft of bicycles in stations.

In Italy, as for 2018, Rome ranks fifth regarding the total number of thefts (after Rimini, Milan, Bologna, and Florence), with 3,251.81 thefts per 100.000 inhabitants. With respect to the automobile thefts, Rome ranks 6th in Italy with 391.37 thefts per 100.000 inhabitants (Ministero dell’Interno, 2018), which is even higher than the rate for Italy previously stated (264.08 in the year 2016). Regarding the same indicator, Milano ranked 11th with a rate of 286.61 automobile thefts per 100.000 inhabitants, Turin ranked 13th with 211.10 and Bologna ranked 36th with 83.06 (Ministero dell’Interno, 2018). New York, as for 2018, registered a vehicle theft rate of 99.05 per 100.000 inhabitants and Washington D.C a rate of 161.55 (National Insurance Crime Bureau, 2018). In Paris, as for 2014, 2.580 automobile thefts were reported, which is equivalent to a rate of 116 thefts per 100.000 inhabitants (Observatoire national de la délinquance et des réponses pénales, 2014). Thus, the automobile theft rate in Rome is significantly higher than the ones in cities as New York, Washington, and Paris.

As for 2009, JCDecaux declared that 7.800 bicycles had been stolen in Paris and suggested that it could be the fault of the locking mechanism’s design, as users remained unsure of whether their bike was properly docked (The London Times, 2009). Thus, for avoiding a similar effect in Rome because of the high theft rate, a proper locking and security systems with videocameras are essential, as well as a coordinated communication with the local police.

4.2. Mobility analysis

A first step to analyze the mobility in Rome is to analyze the Origin-Destination matrix of the trips in the morning peak hour. This information was provided by Roma Servizi per la Mobilità and refers to general trips, including public transport and private vehicles.

Figure 8 Trip Origins In this OD matrix, Rome is divided into 19 zones, which do not correspond to the division by “Municipi”. The total amount of trips generated inside the Comune di Roma is 555.408 in the morning peak hour. As well, it is possible to appreciate in Figure 8 that zone 31 is the area with the highest generation of trips in the morning peak hour, with 63.096 trips. This zone includes the Urban Zones of Centocelle, Gordiani, Torpignattara, Casilino, Quadraro, Tuscolano Sud, Tor Fiscale, Latino and portions of Appia Antica Nord, Appio-Claudio and Don Bosco. 39

Figure 9 Detail zone 31

Zones 64 and 32, have also an important amount of trips in the morning, with 49.942 and 48.636 correspondingly. However, it is possible to appreciate that the level of aggregation of zone 64 is much larger than the one of zones 31 and 32 and naturally will tend to sum up to a higher amount of trips.

Regarding the destinations, the total amount of trips is 647.756 inside the Comune di Roma. It is possible to appreciate in Figure 10 that zones 32 and 20 are the main attraction zones in the morning peak hour, due to the high presence of working places in these areas. The first one sums up an amount of 74.302 trips and the second one 73.742 trips in the morning peak hour.

Figure 10 Trip destinations

Zone 32 is composed of the urban zones of EUR, Valco S. Paolo, Garbatella, Navigatori, Ostiense, Marconi, Pian Due Torri, Portuense, Colli Portuensi and a fraction of Buon Pastore, Corviale and Trullo. Zone 20 is composed mainly of the urban zones of Nomentano, Università, San Lorenzo, Verano and a fraction of XX Settembre and Salario.

Figure 11 Detail zones 32 and 20

As well, an analysis of the most important OD pairs was realized through the construction of desire lines using the software ArcGIS. In Figure 12 is possible to observe the 15 OD pairs with the highest amount of trips during the morning peak hour in Rome, classified in five different categories. The internal trips of each zone are represented with dots, with bigger dots indicating a higher amount of trips.

Figure 12 Desire lines in Rome

According to the desire line analysis, the strongest relations are between zones 31 to zone 20 with 8.045 trips and from zone 30 to zone 20 with 7.598, confirming the importance of zone 20 as a trip attraction area. The next three OD couples are; zone 64 to zone 31 with 5.857 trips, zone 50 to zone 32 with 5.692 trips and zone 43 to zone 32 with 5.372 trips. However, it is important to highlight that some intra-zonal trips have a larger value than the before mentioned OD relations. Zone 32 has 10.038 internal trips, zone 50 has 9.641 trips, zone 31 has 9.386 and zone 64 has 9.350 trips.

Figure 13 Detail of desire lines in the city center

In the last twenty years, a dispersion phenomenon has taken place in Rome, with a significant part of the population displacing to the peripheric parts of the city. In the period 2004-2013, the number of trips from residents of the province to the municipality of Rome increased from 250.000 to 800.000, indicating the tendency of the population to relocate on the neighboring municipalities to Rome. In the same sense, the average trip distance home- work has increased, carrying along with increased congestion problems (Roma Servizi per la Mobilità, 2015).

In the period 2004-2013, there was a mobility reduction of about 23%, meaning a reduction of 1.4 million trips along the day. The daily amount of trips, as for 2013 reached 4.7 million units in the city of Rome. This reduction of mobility can be explained by two factors mainly: the economic crisis that influences directly in the amount of mobility and the strengthening

44 of the centrifugal tendency, that shifts the barycenter of the trips to the exterior (Roma Servizi per la Mobilità, 2015).

Figure 14 General trips behavior-Morning peak hour (Roma Servizi per la Mobilità, 2015)

From the total amount of trips generated in the morning peak hour in Rome, as for 2015, more than 50% had an origin and destination inside the Grande Raccordo Anulare (GRA), as it is possible to appreciate in Figure 14. As well, it is clear that the trips of the relation Provincia di Roma-Comune di Roma are dominant in the direction going inside the city (76.000 going inside, 14.000 going outside), as well as it happens in the relation outside GRA- inside GRA (100.000 trips going inside, 41.000 going outside) (Roma Servizi per la Mobilità, 2015).

The modal split in the city of Rome is; 15,5% Motorcycle, 50% Car, 28,9% public transport, 5% walk and just 0,6% bicycle. However, in the central zones of Rome the modal split changes significantly. In the zone defined by the “Anello ferroviario” the modal split of the originated trips is; 13% motorcycle, 45% car, 32% public transport and 9% walk-bicycle. Regarding the attracted trips, the modal split is; 23% motorcycle, 28% car, 43% public transport and 6% walk-bicycle (Roma Servizi per la Mobilità, 2015). That means that for the trips that arrive into the city center, there is a higher use of the public transport and a tendency to use less the car, which can be explained by the presence of the limited traffic zone and which would mean a higher potential for the implementation of a bikeshare system.

The average trip time using public transport is about 50 minutes compared to 44’ minutes by car. Regarding the trip distance, on average it is 11,6 km by PT and 12,8 km by private transport. As well, the commercial speed of trips by public transport (14 km/h) is inferior to the one of trips made by private transport (17,5 km/h) (Roma Servizi per la Mobilità, 2015). This serves to understand that the high choice of private vehicles (65%) for traveling in Rome is associated with the lack of competitivity of public transport, in terms of time and speed. Therefore, a new bikeshare system has to be competitive enough in terms of time, speed and costs to make possible a modal shift from private vehicles.

4.3. Potential barriers to Bikeshare in Rome

The identified potential barriers for a new bikeshare program implementation in Rome are:

• Lack of cycling infrastructure: Rome, as for 2019, has 241.45 km of cycling infrastructure, from which 112.41 km are rural and just 129.04 km are urban (Roma Servizi per la Mobilità, 2019). New York City, as for 2018, had 1,996 km (1,240 miles) of bike lanes installed and 772 km (480 miles) of protected bike lanes (New York City Government, 2019). Copenhaguen possesses a total of 454 km of cycle lanes (Centre for Public Impact, 2019), and Paris 901 km by 2019 (L'Institut Paris Region, 2019). This means that Rome has a much more reduced cycling infrastructure in terms of 46

kilometers per inhabitant, with 4,49 km/inhabitant compared to 32,1 km/inhabitant in New York, 75,35 km/inhabitant in Copenhaguen and 42,08 km/inhabitant in Paris.

• Strong culture for private vehicles: Rome has a strong culture with private vehicles, with a high motorization rate of 668.4 per 1,000 inhabitants, superior to the value in Italy of 609.5 (Istituto nazionale di statistica, 2019). Accordingly, 50% of the trips are made by car, 15.5% by motorcycle, 28.9% by public transport, 5% by walk and just 0,6% by bicycle (Roma Servizi per la Mobilità, 2015). The strong tradition to use private means for trips represents a challenge to attract users to a new bikeshare system.

• Low safety related to cycling: cycling fatalities per hundred million kilometers is significantly higher in Italy than in other European countries. The fatality rate in Italy is 5,1 (fatalities per hundred million km), compared to 0,9 in Denmark, 2,8 in France, 1,1 in Germany, 0,4 in Spain and 2,1 in the UK (The International Transport Forum, 2018).

With a modal split of 50% by car, 15.5% by motorcycle, 28.9% by PT, 5% by walk and just 0,6% by bicycle (Roma Servizi per la Mobilità, 2015), drivers of motorized vehicles are not used to interact with bicycles, displaying an aggressive driving behavior and representing a higher risk for cyclists. As well, the high speeds reached by cars in some streets without bike infrastructure can be enough reason for users to stop considering it as an alternative.

• High theft rate: As for 2016, theft of private cars in Italy was estimated with a rate of 264 per 100.000 inhabitants, much higher than the rate of 68 in Spain, 150 in the UK, 91 in Mexico and 250 in France (United Nations Office on Drugs and Crime, 2019). More recent estimations in 2018 present even a higher rate, with 391 thefts per 100.000 inhabitants (Ministero dell’Interno, 2018).

In France, with a similar theft rate of private vehicles (as for 2016), 3.000 bicycles were stolen after the first year of operation of Velib’, which corresponded to about 14% of the total fleet (The New York Times, 2008). A similar behavior could take place in Rome if poor security measures are implemented.

Current estimations of stolen bikes in Italy are poor and in Rome, there aren’t at all (official estimations are inexistent as well). In 2013 (last estimation), were calculated approximately 320.000 stolen bicycles from the total 4 million circulating bikes in Italy (Federazione Italiana Ambiente e Bicicletta, 2013), representing that 8% of the bikes are stolen every year in the country.

• Financial sustainability: financial sustainability is one of the main barriers to be faced. Politicians have to consider the initial costs and economic sustainability at times of scarce public resources. Normally, like other public transport systems, it is rarely a revenue-generating activity, thus, public resources might be needed (Velocittà, 2017).

• Lack of political will: political will is necessary to assure the program’s success on each one of its development stages. Strong political support is necessary to ensure funding, land use rights, and coordination between various city agencies (ITDP, 2018). Furthermore, bikeshare implementation requires some measures to implement the system and to enhance the safety of the cyclists, that require political support, as; parking replacement, implementation of new bicycle infrastructure, and city-wide road safety measures.

• Negative previous experiences: the image of bikeshare in Rome has been seriously affected by the failure of previous bikeshare programs. Small pilots, like the one previously done in Rome, may result in damages to the public perception of bikeshare as a viable mode of transport, due to poor coverage and the lack of bike availability (ITDP, 2013). As for the free-floating experience, it left a feeling of the impossibility of a bikeshare program implementation due to vandalism.

• Challenging topography: Rome is known for its seven hills beside the Tiber river; the Quirinal hill, the Viminal Hill, the Esquiline Hill, the Capitoline Hill, the Palatine Hill, the Caelian Hill, and the Aventine Hill. Thus, the hilly topography of Rome represents a challenge for anyone who wants to do a trip on a bike in an upward direction.

• Weather conditions: July and August are known to be considerably hot months in Rome, with an even higher temperature sensation due to the high humidity. The average maximum temperature is 30.6 degrees in July and 30.2 degrees in August. On another hand, in October precipitations are 103mm and in November 114mm (Climate-Data, 2019). According to Campbell and Cherry (2016), normal bikeshare demand is strongly negatively impacted by temperature and precipitations.

4.4. Possible strategies to deal with barriers

• Bikeshare marketing: marketing can be used as an effective instrument to support a new bikeshare system. One part of it is focused on changing attitudes towards cycling, and another one in giving information on new cycling infrastructure. This is essential, as the image of cycling plays a key role in stimulating it (CIVITAS, 2016).

In the case of Rome, the image of bikeshare has been negatively affected previously, thus, a proposal with an innovative concept that differentiates with the previous experiences can help to boost the image of bikeshare. In this sense, e-bikes have a great potential to give a new image to bike-sharing and to attract new users, who would find a fun, innovative and eco-friendly way of traveling.

• New infrastructure projects: In Rome, new bicycle infrastructure is planned to be built for the period 2019-2021, like; the GRAB (Grande Raccordo Anulare delle Biciclette) with an extension of 45 km (17 km in green areas, 9 km in existing bike paths and 19 km of new cycle paths), “La pista ciclabile Nomentana” with an 49

extension of 3.6 km that connects Porta Pia with the exiting cycle path of “via dei Campi Flegrei”, “la pista ciclabile Santa Bibiana” and “la pista ciclabile Tuscolana”with a length of 2.2 km (Roma Servizi per la Mobilità, 2019). Pairing the construction of new bicycle tracks with the launch of a bikeshare system can improve public acceptance and improve safety for users of the new system (ITDP, 2013). In an analysis performed in Brisbane, the first encouraging factor found for non-users of bikeshare was “More bike lanes & paths”.

• General bike-use campaigns: in a city with a clear predominance of the private modes, bike campaigns can serve as a way to encourage its use and to generate awareness in citizens about the externalities of private vehicles, like pollution. In the city of Bogotá D.C., the capital of Colombia, there is a massive program called “Ciclovía Recreativa” each Sunday and on public holidays, in which complete road sections (normally dedicated to private vehicles) are dedicated to the exclusive recreational use of bikes, roller skates, skates, and other eco-friendly modes. It operates from 07:00 to 14:00, with an extension of 126.29 km of exclusive streets (that might include 2 or 3 lanes streets), that are as well interconnected with the normal cycle paths (532 km as for 2018). The campaign was born in 1974 and it has been so successful that normally about 1.500.000 citizens join it every Sunday to perform any kind of physical activity (Instituto Distrital de Recreación y Deporte, 2019). In a nocturnal version of the “Ciclovía” (“Ciclovía Nocturna”), the 9th of August 2018, 2,401,583 users went out to the streets to participate of the recreational activities, cinema, and fireworks performed along with the 18 closed roads (Instituto Distrital de Recreación y Deporte, 2018). This campaign has been useful in Bogotá D.C. to demonstrate that the bicycle is an effective alternative to move, to show that there is a way to reduce the environmental impacts at least in one day a week and to close the big gap that there is between the never-use of the bike and the first use of it.

In Mexico City, the first step before implementing the bikeshare program was to generate awareness of the bicycle use with the program “Muévete en Bici”. It consisted of the closure of the 10 km of “Paseo de la Reforma” from 08:00 to 14:00,

for the exclusive use of cyclists, pedestrians and users of other non-motorized modes. The program, inspired in the “Ciclovía Recreativa” from Bogotá D.C., taught the citizens that the roads of the city could be used in a different way and that it was possible to move in bicycle in a quick way. Nowadays, it has about 48 km and it was the front door for the introduction of the ECOBICI bikeshare program (Delgado Peralta, 2016).

• Make bikeshare competitive with private modes: the strong culture for private modes in Rome makes it challenging to reach a modal change from private vehicles to the bike. For that reason, a new bikeshare system has to be competitive enough, not just in terms of price but in terms of trip time as well. In an analysis performed in the cities of Melbourne and Brisbane, it was found that the most influential barriers were related to the motorized travel being too convenient and that bikeshare stations were not close to home, work, and other significant destinations. The findings suggest that an increased ridership in the bikeshare programs can be reached by ensuring that travel times are competitive from those of the motorized vehicles. Some ways to do that would be through efficient bicycle routing, expanding docking station locations and prioritizing the bike mode (Fishman, Washington, Haworth, & Mazzei, 2014).

To compete with motorized vehicles, it is necessary to increase the convenience of the bikeshare. This one is quite relevant, as it was mentioned as the first reason for citizens to start using the bikeshare systems of Brisbane and Melbourne (Fishman, Washington, Haworth, & Mazzei, 2014). As well, in Mexico City, users stated that the main advantage of ECOBICI was that it is comfortable, practical and useful (38% of users). As the main disadvantage, it was remarked that ECOBICI didn’t arrive in other zones of the city (Secretaría del Medio Ambiente del Distrito Federal, 2015). The same survey shows that the current users of cars and motorcycles are the most reluctant to do a modal change (50% would not consider doing it) and, as a comparison, 76% of bus users would consider doing a modal change.

To increase the convenience of bikeshare, an increase in the density of stations would be a good strategy, as it reduces walking time to stations and thus overall trip times. Additionally, strategic locations of docking stations, easiness for signing up and integration with public transport can increase it as well (Fishman, Washington, Haworth, & Mazzei, 2014). The use of e-bikes presents a huge potential to increase the convenience of a bike-sharing system, as higher speeds can be reached (up to 25 km/h) and fewer physical factors as fatigue have an influence, thus increasing the competitivity of bikeshare against motorized modes. In this sense, it has been found that e-bikeshare has a greater potential to reach a modal change from “sheltered” modes than normal bikeshare (Campbell, Cherry, Ryerson, & Yang, 2016).

• Use of e-Bike: electrical bicycles have the potential to reduce significantly the barriers of challenging topography and extreme weather conditions, as less effort is required and a higher comfort can be reached. The demand of E-bikeshare is less sensitive to longer trips, worse air quality, and bad weather than with normal bikeshare (Campbell, Cherry, Ryerson, & Yang, 2016).

On another hand, e-bikes facilitate the use to a broader range of users, as non- athletes, that would normally not use a bike. Similarly, users that were before discouraged because of sweating and the difficulty to move with appropriate clothing to work would be more motivated to use the bikeshare, as well as those users which have a long enough trip that wouldn’t consider doing it with a normal bike. Thus, increased demand might be expected as in the New York City’s bikeshare, where the ridership of e-bikes was 3 times higher than the one of normal bikes (NACTO, 2018).

• Safety campaigns and road traffic measures: one of the biggest challenges of a bikeshare program is to earn the trust of the users and to guarantee their safety. A fatal accident can seriously affect the credibility of the program, especially in the early phases. In Brisbane, “I’m concerned for my safety riding in traffic” was established as the third reason for not using the bikeshare system (Fishman,

Washington, Haworth, & Mazzei, 2014). Some key factors for the success of Mexico City’s ECOBICI were; to develop appropriate signaling for cyclists, to reduce the speeds of private vehicles inside of the service area, to do an important communication campaign in the zone and to have constant training and supervision from the transit police. As well, ECOBICI had medical insurance for all of its users (Delgado Peralta, 2016).

In Madrid, an important road traffic measure was implemented in order to increase significantly the safety of the cyclists. It consisted of lanes called “Ciclocarril” with special horizontal marks that indicated that the lane was shared with bikes and that the maximum permitted speed was of 30 km/h (Delgado Peralta, 2016). In the case of a road with multiple lanes, the right lane was reserved for the “Ciclocarril” while the other allowed the normal circulation of vehicles. In the case of having a reserved lane for buses, the “Ciclocarril” was located in the middle of that one and the lanes for the normal circulation of private vehicles. With a speed rounding the 25 km/h, the circulation speeds of e-bikes and private vehicles were almost normalized, giving a higher safety feeling for cyclists and a higher level of awareness for private vehicle drivers. In Rome, due to the lack of cycling infrastructure, a similar measure is highly recommended to improve the safety of cyclists.

Figure 15 “Ciclocarril” in Madrid (Barahona, 2014) 53

• Anti-theft and anti-vandalism strategies: considering the high automobile thefts in Rome, it is essential to take into consideration some anti-theft and anti-vandalism strategies for a new bikeshare system. The first one is to have bikes with specially designed parts and sizes, the traditional 26-inch tire size should be avoided. As an example, the bikeshare from Mexico City (ECOBICI) uses a 20-inch front and a 24- inch rear wheel. Nuts and screws also should be designed so that they can only be opened with proprietary tools (ITDP, 2018). These measures can discourage the theft and the resale of parts.

In Paris, a huge amount of bikes were stolen in the first years because users remained unsure if the bike was correctly locked or not. Thieves take advantage of counterintuitive and complicated locking mechanisms. Simple, intuitive systems that indicate clearly when a bicycle is successfully returned, such as flashing green/red lights with a beep, are highly recommended (NYC Dept. City Planning, 2009).

Perhaps the best strategy against vandalism is a robust communication and marketing plan that generates widespread public acceptance of the bikeshare program and encourage locals to take ownership and pride of the system (ITDP, 2013). Reliable service and a positive image of the bikeshare program will tend to reduce the vandalism acts.

Additionally, GPS tracking might be a useful tool in order to discourage theft. Bicimad, the electrical bikeshare system of Madrid, has in each bike a GPS in order to; locate it, know the running speed in an accident and know their journeys (Barahona, 2014). After an abnormal time without the return of a bicycle, GPS tracking can help to locate and recover it. As well, some thieves could be discouraged just by knowing the fact that the bikes are easily traceable by GPS.

Complete identification of the users helps as well to deal with theft rates. In Mexico City, one of the lowest theft and vandalism rates in the world was reached, because

in order to start using the service it was necessary to provide a credit card, a debit card or a telephone bill (Delgado Peralta, 2016). A weak registration process would result in an impossibility to trace back the responsible ones in the case of a missing bike.

The use of cameras in bikeshare stations is highly recommended for a new bikeshare program in Rome. The cameras, along with a coordinated communication and response with the police department, could help with the reduction of bike thefts.

On another hand, an adequate station location might be helpful for improving the security levels of the bikeshare. In that sense, stations should be located in high- visibility areas, with street lighting and minimal tree coverage to have more natural sunlight. As well a location with a natural flow of pedestrians is advisable. The easy access and good visibility are key factors to success, as nobody wants to use a bikeshare station in a poorly-lit location or where users feel personally unsafe (NACTO, 2016).

As well, security deposits are used to control bike thefts. In Paris, in addition to paying the normal subscription fee, users have to leave a pre-authorized security deposit of 150 euros, to help guarantee the return of the bikes (ITDP, 2007). The security deposit shouldn’t be so low with respect to the price of the bicycle, but not so high as well as to discourage new subscriptions to the service.

• Secure financing: securing financing from more than one revenue source is essential to guarantee the financial sustainability of a bikeshare system. Some examples of alternative financing are; sponsorship agreements, advertisement, and grants at local, national or European level (Velocittà, 2017).

On another hand, political and executive support lead to strategic and financial support. Introducing the bikeshare into a city plan for cycling or into an urban

mobility plan encourages greater adoption of schemes from a political level (Velocittà, 2017).

• Build political will: ensuring political will during the whole lifetime of bikeshare is essential to guarantee its success, thus, more than one political party has to be involved to ensure the support over multiple election periods. To build political will it is necessary to educate political leaders on the benefits of bikeshare, through presentations and on-site visits to successfully implemented bikeshare programs. As well, speaking to other implementers can build the political will necessary to gain champions for the bikeshare in the city (ITDP, 2018).

In London, the determination of the mayor Boris Johnson was key for the success of the bikeshare program, as he supported strongly the development of new bicycle infrastructure and set the bikeshare as a top priority. The mayor even personally promoted the system to the residents, which ensured the success of the program (ITDP, 2018). A strong political will is necessary to make bikeshare a reality.

4.5. Compatibility with current transportation plans

The “Piano Urbano della Mobilità Sostenible” (PUMS) establishes several macro-objectives to be fulfilled in a time horizon of 10 years, orienting the mobility into sustainability. The proposed bikeshare aligns with 6 from the 11 macro-objectives of the plan (Dipartimento Mobilità e Trasporti, 2019), which are:

• Improve urban traffic by reducing congestion (ob. 2) • Reduce polluting emissions generated from transport and harmful for health (ob. 4) • Encourage sustainable mobility and pedestrian mobility on the whole urban territory (ob. 6) • Enhance the accessibility of public transport and the exchange with private modes (ob. 7) • Promote more rational use of the private car, of urban spaces, and parking areas (ob. 8) 56

• Encourage urban cycling, integrating it with other transport modes (ob. 11)

Complementarily, the PUMS expresses explicitly in some specific objectives the encouragement of bike-sharing, i.e. objective 4.4) Favor the development of shared mobility (car-sharing, carpooling, bike sharing) and 11.4) Enhance the inter-modality from bicycle- public transport (park bike, bike-sharing, on-board bicycle transport).

For the elaboration of the PUMS, two surveys were made to citizens in order to understand their needs in terms of mobility. A telephone survey was answered by 2.000 roman residents and a web survey was answered by 5.415 web users. The top 3 objectives proposed by the telephone survey were; 1) Reduce the accidentality rate, 2) Improve urban traffic, and 3) Reduce the polluting emissions. The top 3 from web users were; 1) Enhance public transport infrastructure, 2) Improve urban traffic, and 3) Promote urban cycling (Dipartimento Mobilità e Trasporti, 2019). This shows that the proposed bikeshare would respond to the users’ needs in terms of reducing the overall congestion of the city, reducing the air and noise pollution, and offering a service that promotes urban cycling.

On the other hand, according to the “Piano Generale del Traffico Urbano di Roma Capitale”(PGTU) the key topic/instrument is sharing, indicating that all actions of the plan refer to this concept, such as; mobility bonus, car and bike-sharing, mobility management, public transport, open data, and smart card. The general objective of the PGTU foresees a city with an efficient and more competitive public transport with respect to the cars and where moving by walking or by bicycle is easy and convenient (Roma Servizi per la Mobilità, 2015). Moreover, the proposed bikeshare scheme aligns with 2 from the 7 quantitative objectives that result from the general objective, which are:

• Reach a bicycle modal share of 2% in 2 years and 4% at the city level within 5 years (10% in the city center). • Reduce progressively the CO2 emissions generated by transportation.

5. INITIAL PLANNING

5.1. Service area determination

In order to determine the best service area location, a suitability examination was performed using georeferenced spatial analysis. Accordingly, diverse information represented in layers was evaluated and then with a multicriteria analysis a suitability map was generated, as done in other bikeshare feasibility studies as in; San Mateo (Alta Planning + Design, 2013), Memphis (Alta planning and design, 2013), and Dayton (Bike Miami Valley, 2013).

Five different criteria were taken into consideration to do the suitability analysis; 1) Medium - Low accessibility zones, 2) Cycle path infrastructure presence, 3) Population and employment density, 4) Origin and destination trips, and 5) Slope.

For the spatial analysis, it was used the coordinate system WGS 1984 with a projection UTM Zone 33N. As the diverse information normally is presented in a different coordinate system or projection, the respective transformations were done in each case.

5.1.1. Medium-Low accessibility zones

This criterion seeks to give a higher priority to those zones that have difficulty to be accessed, as bikeshare would have the potential to give a solution to the citizens that cannot arrive easily at those places. Bikeshare can be integrated into the existing high-capacity public transport network to help solve the last-mile problem, in which users have to begin (or finish) their trip with a long-distance walk that cannot be avoided.

Accordingly, two different aspects were taken into account in order to build this criterion; 1) zones inside the existing traffic limited zones (ZTL) in Rome and 2) zones distant more than 500m from the existing metro or tram stations. The ZTL, are zones in which the access with motorized vehicles is highly restricted, some of them apply during daytime and some

58 others during nighttime. Thus, bikeshare has the potential to gain easily demand from the trips that are originated or destinated from these zones and provide a more efficient and eco-friendly way to transport.

The georeferenced information from the six different ZTLs was obtained from the Open Data of Roma Servizi per la Mobilità, each one represented in a different shapefile. As well, the georeferenced information of the metro line A, metro line B, metro line C, metro stations (of each line), the six tram routes and their corresponding stations (each one in a separate shapefile) were obtained from the same official webpage. The ZTL and transport network georeferenced information was imported in ArcGIS and then joined into two different layers to do the required spatial analysis. In the next figure, the existing traffic-limited zones of Rome are represented.

Figure 16 Existing traffic-limited zones in Rome

On another hand, the spatial analysis was performed in order to find out the zones that are not easily served by the existing high-capacity public transport network. For that purpose, a buffer of 500m around the metro stations and tram stations was drawn and then this area was subtracted from the rest of Rome. The result is shown in Figure 17.

Figure 17 Medium-Low accessibility areas

Next, both of the aforementioned layers were combined and then scored, giving the maximum score (5) to those areas inside the ZTLs and far away from the metro and tram stations, and the minimum score to those zones that are considered to be served efficiently by the high-capacity public transport network (less than 500m from the stations).

Figure 18 Accessibility - Scoring

5.1.2. Cycle path infrastructure

The objective of this criteria is to give a priority to those zones that are served by bicycle infrastructure, in the sense that they can bring a higher level of safety in a new bikeshare system. In that sense, not only the current cycling network was taken into account but also the cycling path projects that are going to be realized in the upcoming years.

Three different scenarios of the cycling infrastructure are identified; existing infrastructure, new infrastructure according to the conceived Reference Scenario and new infrastructure according to the Scenario Phase II, conceived by the “Piano Urbano della Mobilità Sostenibile” (PUMS). The difference between the last two is that the cycle paths of the Reference Scenario have a higher priority and thus a higher probability to be constructed than those of the Scenario Phase II. The georeferenced information of the existing cycling 61 network was obtained from the Open Data of Roma Servizi per la Mobilità. On another hand, each one of the cycle paths from the Reference Scenario was drawn directly in ArcGis based on a pdf map sent also by the same organization, using as reference the satellite base map of Open Street Map. Finally, the shapefile of the cycle paths of the new Scenario Phase II was sent by the same organization and a transformation of coordinates was made using the conversion tool of the Instituto Geografico Militare to have consistency among the diverse coordinate systems.

For the spatial analysis, a buffer of 500 m was done around each one of the aforementioned scenarios and then the scoring was done. The maximum score (5) was given to the areas within a zone of influence (buffer) of 500m of the existing infrastructure and the cycle paths of the Reference Scenario. The areas lying inside the buffer of the Scenario Phase II were given a score of two (2), as they are assumed to have a minor probability to be completed in the next five years. The rest is given a score of zero (0). The results can be visualized in the following figure.

Figure 19 Cycle paths - Scoring

5.1.3. Population and Employment density

This criterion seeks to give higher importance to those areas that have a very high population density or employment density, as normally these are the ones that generate and attract a higher amount of trips.

Information about the population was gathered from the “15° Censimento della popolazione e delle abitazioni” 2011 of the Insituto Nazionale di Statistica (ISTAT) and the employment information from the “9° censimento generale dell’industria e dei servizi- Anno 2011”. Then, this information was imported in ArcGIS, using as well the

63 georeferenced territorial information from the ISTAT, in which Rome is divided into 13.656 polygons, used for the census.

Having all the necessary information it was possible to calculate the area of each one of the polygons and then the population density and the employment density of each one of these small areas. It is important to highlight that as the polygons are so small, they represent a very high amount of informatic data but as well a very high precision, which was very useful in doing the suitability analysis.

After calculating the densities, a scoring procedure was performed, in order to give high scores to higher density areas and low scores to low-density areas. For that scope, scores were given using percentiles as breakpoints. For example, those areas with a population density higher than the percentile 95 were given a score of five (5), which means that just 5% of the polygons with the highest density will have the maximum score. In the same sense, another score was given for the employment density, following the same rule, which shown in Table 2.

Table 2 Scoring rule for population density and employment density Upper limit Lower limit Score Inf. Percentile 95 5 Percentile 95 Percentile 90 4 Percentile 90 Percentile 80 3 Percentile 80 Percentile 60 2 Percentile 60 > 0 1 Density = 0 0

Table 3 Breakpoints for population and employment density scoring Break point Pop. Density value Empl. Density value Percentile 95 40.830 26.324 Percentile 90 30.915 14.889 Percentile 80 21.161 7.009 Percentile 60 10.322 2.278

The results of the population density scoring are shown in the following map.

Figure 20 Population density - Scoring

As it is possible to appreciate in Figure 20, there is a high concentration of high-density population areas at the east side of the city, between metro A and metro C. That includes the urban zones of Appio, Tuscolano Nord, Tuscolano Sud, Torpignattara, Gordiani, Centocelle, and Don Bosco.

Figure 21 Population density – Detail on the east side of Rome

On the other side, the scoring of the employment density is presented in Figure 22.

Figure 22 Employment density - Scoring

On Figure 22 it is possible to appreciate that the highest concentration of employment is found in the city center and its surroundings, especially the urban zones of Centro Storico, Della Vittoria, Prati, XX Settembre, Salario and Esquilino

Figure 23 Employment density- Detail on the city center

After scoring each one of the polygons, a final global score for this criterion was made, equivalent to the maximum from the score of population and the score of employment density in each one of the cases. This was made with the purpose of not affecting those zones that might have a high population density but a low employment density and vice versa. These zones are still very useful for a bikeshare system, with some of them acting as high trip generation areas and zone others as high trip attraction areas. The results are shown in the next figure.

Figure 24 Density -Population and Employment final scoring

5.1.4. Origin and destination trips

A new bikeshare system has to be located into high trip generation and attraction zones in order to serve a high amount of trips and become successful, thus, this criterion was intended to give a higher priority to those zones.

As a source of information, it was used the Origin-Destination matrix from the morning peak hour of Rome, provided by Roma Servizi per la Mobilità. In that matrix, Rome is divided into just 19 different zones, thus, the level of precision is not as high as the one of the criteria of population-employment density. However, an O-D matrix gives a direct estimation of the trips and information from where they are made and where they arrive, so it was considered as well as valuable information for the multicriteria analysis.

It’s important to highlight that the size of some zones was much bigger than some others, thus, representing a higher amount of trips just for that fact. Thus, in order to avoid this heterogeneity, a normalization procedure was done, dividing the number of trips by the area of each one of the zones. With this operation, a much more reliable comparison of the 19 different zones could be performed.

Table 4 Scoring rule for trip origins-destinations Upper limit Lower limit Score Inf. Percentile 90 5 Percentile 90 Percentile 80 4 Percentile 80 Percentile 65 3 Percentile 65 Percentile 50 2 Percentile 50 > 0 1

Table 5 Breakpoints for trip origins-destinations scoring Originated trip Destination trip density value density value Break point (trips/km2) (trips/km2) Percentile 90 2.179 4.311 Percentile 80 1.786 2.526 Percentile 65 1.509 1.556 Percentile 50 709 822

A first score was given to the zones according to the originated trip density, according to the rule shown in Table 4 and the results are shown in the next figure.

Figure 25 Origin trips density – Scoring

From Figure 25 is possible to appreciate that the zones with higher density of originating trips are found in the north and east of the city center, including the urban zones of Trieste, Nomentano, Salario, Universita, San Lorenzo, Torpignattara, Gordiani, Casilino, Centocelle, Tuscolano Sud, Quadraro and Don Bosco, as it is possible to appreciate in the next figure.

Figure 26 Detail on origin trips density scoring

Similarly, a scoring procedure was done according to the destination trips density of each one of the 19 zones, using the same rule as with the originating trips. The results are shown in the following figure.

Figure 27 Trip destinations density – Scoring

As for the destination trips, it is possible to appreciate that the highest attraction zones are the city center and the surrounding north neighborhoods, including the urban zones of Centro Storico, Trastevere, XX Settembre, Salario, Trieste, Nomentano, Universita, San Lorenzo, and Verano.

Figure 28 Detail on destination trips density scoring

Finally, the global scoring of this criterion was calculated as the maximum of the scores obtained from the trip origins density and the trip destinations density. The result can be appreciated in the following figure, resulting in three zones with the highest score.

Figure 29 Trips – Origins and Destinations final scoring

5.1.5. Slope

The slope criterion aims to give a higher score to those areas that have a lower slope and vice versa, as a new bikeshare system would be ideally located in areas that require less physical effort for the cyclists.

In order to generate a slope map, it was necessary to obtain at first the contour lines from Rome, from the Open data of Regione Lazio. The dataset was originally divided into about 10 different zones of the province of Rome, so they were joined in Arcgis and then clipped in order to just have the contour lines corresponding to the city of Rome.

Figure 30 Contour lines Rome - 5m

With the contour lines every five meters, a special spatial analysis was performed in order to calculate the slopes. Then the slopes were categorized into five groups and a score was assigned to each one of them. Slopes from 0 to 3% were given the maximum score (5), from 3% to 6% a score of four (4), from 6% to 9% a score of three (3), from 9% to 12% a score of four (4) and those with a slope superior to 12% were given the minimum score (1). This scoring was done taking into account that slopes up to 3% are slightly uphill but not challenging, from 3%to 6% it is a manageable gradient that can cause fatigue over a long period of time, from 6% to 9% it is very challenging for new climbers and starts to become uncomfortable for seasoned riders, more than 9% represents a painful gradient, especially when maintained for a long time (The Climbing Cyclist, 2019).

Figure 31 Contour lines and slopes

It is possible to appreciate from Figure 31 that the most representative slopes coincide with the areas that have very close contour lines (indicating a fast elevation change in a short distance), which permits us to understand that the slopes calculation procedure was done correctly. The results are shown in Figure 32 and Figure 33.

Figure 32 Slope- Scoring

Figure 33 Slope - General view of Rome

5.1.6. Multicriteria analysis – Analytical Hierarchy Process (AHP)

In order to integrate the results of the five criteria into a single global indicator, a multicriteria analysis was performed using the Analytical Hierarchy Process (AHP). The AHP is a multicriteria decision-making methodology that represents the problem in a hierarchical structure, with diverse criteria laying on different importance levels.

One of the main objectives of the AHP is to determine the weight of each one of the criteria. This is done by doing first a pairwise comparison of each one of the criteria using the importance scale of Saaty, that goes from 1 to 9, with 1 indicating an equal importance between the criteria and 9 indicating that the first criterion A is extremely more important 79 than criterion B. To indicate that criterion A is extremely less important than criterion B, it is denoted 1/9.

In this work, the AHP was used in order to determine the weights of each criterion and to determine the global suitability score. The procedure is as follows (Manikrao & Chakraborty, 2010):

1) Build the pairwise comparison matrix using the Satty’s importance scale. In this process, a criterion compared with itself will have a score of 1. With M criteria, the

resulting is a square matrix (퐴1), in which the element 푎푖푗 denotes the importance of 1 criteria i with respect to criteria j. When i=j, then 푎푖푗 = 1 and in general 푎푖푗 = . 푎푗푖

2) Determine the normalized criteria vector 푤푗, trough the calculation of the geometric mean from row i and trough the normalization of the geometric means in the comparison matrix.

1/푀 푀

퐺푀푗 = [∏ 푎푖푗] 푗=1

퐺푀푗 푤푗 = 푀 ∑푗=1 퐺푀푗

3) Calculate matrices 퐴3 and 퐴4:

푇 퐴2 = [푤1, 푤2, … , 푤푀]

퐴3 = 퐴1푥 퐴2

퐴4 = 퐴3/퐴2

4) Calculate the maximum eigenvalue 휆푚푎푥 5) Determine the consistency index:

휆푚푎푥 − 푛 퐶퐼 = 푛 − 1

6) Determine the consistency ratio (CR). A CR equal or minor to 0.1 is considered acceptable: 퐶퐼 퐶푅 = ( ) 퐶푙푟 퐶푙푟 𝑖푠 푡ℎ푒 푟푎푛푑표푚 푐표푛푠𝑖푠푡푒푛푐푦 푣푎푙푢푒, 푡ℎ푎푡 푣푎푟𝑖푒푠 푎푐푐표푟푑𝑖푛푔 푡표 푡ℎ푒 푠𝑖푧푒 표푓 푡ℎ푒 푚푎푡푟𝑖푥

The criteria, in this case, were organized on the same hierarchical level, as it’s possible to appreciate in Figure 34. Then the pairwise comparison was performed, as shown in Table 6, using the scale of Satty. Then the procedure described before was performed, obtaining a CR lower than 0.1 (indicating that the procedure was consistent) and the weights of each criterion, indicated in Table 7.

Figure 34 AHP scheme

Table 6 Pairwise criteria comparison

# 1 2 3 4 5

Pop. and Origin- Med-Low Cycle path CRITERIA empl. Destination Slope Access. infras. density trips

Medium-Low 1 1 1/2 1/2 1/2 1 accessibility zones

Cycle path 2 2 1 1 1 2 infrastructure

Population and 3 2 1 1 1 2 employment density

Origin and Destination 4 2 1 1 1 2 trips

5 Slope 1 1/2 1/2 1/2 1

Table 7 Criteria weights CRITERIA Weight 1 Medium-Low accessibility zones 12,5% 2 Cycle path infrastructure 25% 3 Population and employment density 25% 4 Origin and Destination trips 25% 5 Slope 12,5%

5.1.7. Global suitability

After finding the weights of each one of the five different criteria, the information of all the layers was intersected and weighted accordingly, obtaining the suitability map for a new bikeshare system. This suitability map reflects the notion that the ideal area for a new bikeshare system location should be; a medium-low accessibility area, with the presence of

82 bike infrastructure, with a very high population density or employment density, a high generation or attraction of trips and a very low or non-existent slope.

Figure 35 Suitability map for bikeshare

As it is possible to appreciate in Figure 35, the urban zones of Centro Storico, Prati, Della Vittoria, Trastevere, XX Settembre, Salario, San Lorenzo, Testaccio, Tuscolano Sud, Torpignattara, Gordiani, Quadraro, Don Bosco and Appio-Claudio are highly attractive for a new bikeshare system implementation.

5.1.8. Service Area

The service area was determined taking into account the location of the polygons with the highest suitability scores (in blue) and considering the planning guidelines of the Bikeshare

83 planning Guide 2013 and 2018, where it is stated that a service area should have a minimum of 10 km2. This minimum area is intended to avoid what happened with the Smartbike system (launched in August 2008 in Washington D.C.), in which due to the small size of the system (just 10 stations), the program was poorly utilized and largely unsuccessful (ITDP, 2013).

However, a much larger area than 10 km2 is desirable, as the service area has to contain a significant amount of Origin-Destination couples in order to serve a higher amount of trips. Successful systems as Bicing in Barcelona and Ecobici in Mexico have a much larger area, 53 km2, and 54 km2 accordingly. As a reference, the station-based bikeshare system that operates in Milan has as well a service area of 53 km2, others, with a longer tradition in bike- sharing as Velib’ in Paris, have a service area of 155 km2 (ITDP, 2018).

In order to draw the Service Area, it was taken into account the form of the outlying streets, the slopes, the form of the diverse bikeshare infrastructure, the presence of boundaries as rail tracks and the natural boundaries as the parks and rivers (e.g. Parco della Caffarella).

Figure 36 Service Area

The service area of Figure 36 has an area of 44,75 km2, containing the following urban zones (or a portion of them); Della Vittoria, Prati, Flaminio, Centro Storico, Trastevere, Testaccio, Aventino, Zona Archeologica, Esquilino, XX Settembre, Salario, Trieste, Sacco Pastore, Nomentano, Universita, San Lorenzo, Celio, Appio, Latino, Tuscolano Nord, Tuscolano Sud, Torpignattara, Quadraro, Gordiani, Casilino, Centocelle, Quadraro, Centro. Direz. Centocelle, Don Bosco and Appio Claudio.

5.2. Bikeshare system type description

Taking into account the global trends, the impacts on public space, the previous experiences in Europe, the possibility to integrate more easily e-bikes, as well as the potential barriers to

85 bikeshare in Rome previously studied, a Station-Based system is proposed for a new bikeshare system in Rome.

It is planned an electrical bikeshare system, with bikes that are charged in the stations every time they are locked-in and with a fully automated functionality. Each station would have a terminal (or totem) that allows; the realization of electronic payments, giving information about the stations’ location and current occupation, registering incidents related to bikes’/docks’/stations’ state, subscribing for the first time to the service, consulting the user-related information (as the remaining time of the subscription or remaining trips) and giving road safety recommendations, which would be specially useful for tourists coming for the first time to the city.

Figure 37 Velib’s automated stations (Velib' Metropole, 2019)

Stations would be permanent-type (not modular), as a connection to the power source is needed to allow the charging capabilities of the docks. As for the docking styles, the docking spaces are chosen over the bike parking areas, as the first ones fit better into the urban landscape and the latter ones require a secure area that is fenced or walled off (ITDP, 2018).

Figure 38 Bikemi’s terminal and smartcard (Bikemi, 2019)

As for the information technology system and payment mechanism, two types of users would be served; long-term users and casual users, as tourists. Long-term users would make use of smart cards to check-in and check-out bicycles easily and casual users would make use of the system through credit cards and security deposit mechanisms, as placing a hold on the card to act as a guarantee if the bike is not returned. As well, a call center, an online portal, services centers, station’s terminals, and a smartphone app should be provided in order to guarantee a strong communication system for users.

The bicycle used is the e-bike, which is battery-equipped, allows reaching a speed up to 25 km/h and to be used as a normal one if the user desires to do more physical effort. The level of pedal-assistance of this bike can be graduated into three different levels (low, medium, high) depending on the slope conditions and the user preferences. These bikes get charged automatically when connected to the docks and can be configured to just charge the battery until a certain level (e.g. 80%) to increase the lifetime of the batteries. As well, these bikes should include a GPS tracker in order to locate them, know their running speed in an accident and collect data for OD trips analysis.

Figure 39 E-bike from Bicimad (Bicimad, 2019)

6. SYSTEM PLANNING

In this chapter, a second step into the planning of the bikeshare system is performed. For that scope, a station-siting procedure is done, based on the recommendations given by the Bikeshare Planning Guides from ITDP and the NACTO’s Bikeshare station siting guide. Next, a system size calculation is performed in order to determine the number of bikes. Then, by knowing the number of stations, a procedure is performed with Thiessen Polygons in order to determine the size of each one of them. After that, a general view of parking replacement is given and finally, demand-boost strategies will be purposed.

As well, in this section, a station density parameter is defined in order to determine the number of stations on every square kilometer. It is a relevant parameter since there is a direct correlation between a higher station density and higher market penetration, i.e. the number of trips per 1.000 residents, as it was explained in Chapter 3.2.

6.1. Location of stations

For the location of the stations, it was taken into account the recommendations given by the NACTO’s Bike share station siting guide, were it is stated that these stations should be; accessible and convenient, in a way that they can be found easily by pedestrians and cyclists at any time; designed for safety, being part of the city’s traffic calming measures; operationally feasible, as the balancing and maintenance vehicles need to have an easy access to the them; adequate to enhance the pedestrian realm, as stations should enhance the quality of the surrounding pedestrian environment; part of the streetscape hierarchy, as they should not impede elements such as bus/transit stops (NACTO, 2016).

As well, the recommendations stated on the Bikeshare Planning Guide (2018) were followed for the ideal station location characteristics, that would be; sunny with minimal tree cover; close to intersections; close to public transit stations; highly visible and with street lighting; adjacent to bicycle infrastructure; have an easy access for users as well as for maintenance and rebalancing vehicles.

Additionally, a grid approach was followed, in which the service area is divided into spaces of 1km x 1km and the stations are placed evenly following a station density parameter. In this case, initially, a density of 9 stations/km2 was used.

Taking into account all the previously stated recommendations, the following procedure was used:

1) Locate first stations near an exit of the metro stations included inside the service area 2) Locate stations near the tram stations included inside the service area. Consecutive stations are placed on opposite sides of the road. 3) Perform a 200m buffer around the already placed stations, to avoid locating another station at a short distance from the ones already placed. 4) Locate stations on high trip generation/attraction points (e.g. Universities) 5) Locate stations based on the aforementioned recommendations for station sitting (e.g. close to intersections, easy access). As well, parking lots on the right side of the street (slow lane) were preferred for greater safety. Accordingly, stations were located close to cycle infrastructure, in places with high visibility and in high-density population-employment locations (very low-density zones with score 0 or 1 in the density criterion were avoided in general). 6) Each square of 1kmx 1km was completed according to the density parameter stablished. 7) Optimize the number of stations, using buffers of 250m and identifying notoriously overlapped stations. 8) Generate a buffer of 500m to assure all the service area is covered.

Figure 40 Location of stations near metro station’s exits

Figure 41 Location after the buffer of 200m was performed, near cycle infrastructure

In the first iteration, the result was 418 stations. After performing the optimization of stations, the resulting number was 377 bikeshare stations. This procedure, shown in Figure 42, was done in order to increase the efficiency of the system, as naturally, a higher amount of stations will represent as well higher costs.

Figure 42 Optimization of stations

In Figure 43 is possible to appreciate the result of the applied procedure, with 377 stations. With an area of 44,75 km2, the resulting station’s density is of 8.42 stations/km2. Successful bikeshare systems as Bicing in Barcelona have a density of 8.8 stations/km2, Ecobici in Mexico 8.9 stations/km2 and Velib’ in Paris of 7.7 stations/km2 (NACTO, 2018), thus, the resulting density is considered to be enough to reach a high market penetration.

Figure 43 Bikeshare station location

6.2. System size

To determine the system size is necessary to calculate the number of stations and the number of bikes from the bikeshare system. The first one was previously found, with 377 stations. For the second one, it is necessary to determine the benefited population for the bikeshare system and then calculate the number of bikes with a chosen ratio bikes/service area population.

In order to determine the served population, a buffer of 500m around the stations was performed, as it is possible to appreciate in Figure 44. Then an intersect procedure was done with the Population layer, to determine the benefited population.

Figure 44 Buffer 500m around stations

According to the bikeshare planning guide 2018, a ratio of 10-30 bikes per 1.000 residents is recommended in order to meet demand, but as well, establishes that it has to be not so large as to have less than four daily uses per bike. Observing different existing bikeshare systems it has been noticed that this parameter is highly variable, with 4 bikes per 1.000 residents (in SA) in Barcelona, 13 bikes per 1.000 residents in Dublin, 19 bikes per 1.000 residents in Mexico City and 3 bikes per 1.000 residents in Milano. Those with a higher ratio, as the bikeshare from Dublin and Mexico, reached a higher market penetration, with 75 trips per 1.000 residents in the first case and 105 trips per 1.000 residents in the second case, superior to 27 trips per 1.000 residents in Barcelona and 4 trips per 1.000 residents in Milan (ITDP, 2018). However, it is important to notice as well that a large ratio will increase significantly the investment costs of bikes and docks. Thus, the recommendation from the bikeshare planning guide from ITDP was followed, with a value of 10 bikes per 1.000 residents.

With the procedure it was estimated that 679,277 citizens would be benefited from the purposed bikeshare scheme, thus, with a ratio of 10 bikes per 1.000 residents, the number of

94 bikes would be (rounding up) 6.800 in order to attend demand properly and reach a significant market penetration.

6.3. Station sizing

In order to determine the size of each one of the stations, it was necessary to determine the amount of population and employment associated with each one of them. For that scope the first step was to create Thyssen Polygons, which are the result of a geometric procedure, that associates any location to its closest point (in this case the bikeshare stations) and build polygons. In that sense, any spot within a Thiessen polygon is closer to its associated station than to any other station. The results of the first Thiessen procedure is shown in Figure 45.

Figure 45 First Thiessen polygons

After doing the first Thiessen polygons, it is possible to observe that the territory is correctly divided with respect to each one of the stations, however, in some cases, the associated points of the Polygons can be at very high distances, thus, it is necessary to limit the distance to a realistic value. For that scope, a buffer of 500m was calculated around the stations and then used to cut the Thiessen polygons. The result of this procedure is shown in the following figure.

Figure 46 Second Thiessen polygons

After having the territory correctly divided with the stations, it was necessary to associate the amount of population and employment to each polygon. For that scope, an intersect procedure was performed between the Thiessen polygons layer and the Population- Employment density layer. The results of this procedure can be found in the corresponding annex section. 96

Next, the stations were ranked according to the population, so that the 10% with higher assigned value was set as a station of big size and the lower 10% was set as a small-sized station according to the population. In the same way, the stations were classified with the density of employment, with the upper 10% set as big-sized and the lower 10% set as small- sized by employment. Following, the final classification was made with the two previously obtained results, taking into account the following rules; small-size by population and medium-size by employment is set as small-sized station (and vice versa); big-sized by population and medium-sized by employment is set as big-sized (and vice versa); small- sized by population and big-sized by employment is set as medium-sized; small-small, medium-medium and big-big are set naturally as small, medium and big-sized, correspondingly. This is made in order to deal with the extreme cases, where a station would more probably need a higher amount of docks or the opposite case, where due to the low amount of population or employment a not so high amount of docks is necessary.

A number of 20 docks were assigned to the small-sized stations, 30 docks to the medium- sized and 40 docks to the big-sized. Therefore, the total number of docks is 11.660, which gives a dock/bike ratio of 1,71. Comparatively, Bicing in Barcelona has a docks/bike ratio of 1,7 and Ecobici in Mexico a ratio of 1,7, thus, the value obtained is considered to be appropriate for the purposed bikeshare system. In Figure 47 are shown the final results after the station-sizing procedure. Finally, a resume of the calculated parameters in shown in Table 8.

Figure 47 Bikeshare stations sizing

Table 8 Resumed information Bikeshare Item Value Stations 377 SA Population 679.277 Bikes / 1000 residents in SA 10 Total bikes 6.800 Docks 11.570 Docks/bike ratio 1,70 Avg docks/station 30,7

6.4. Parking replacement

In many cities the public space for the allocation of bikeshare stations is not readily available, thus, one of the most common solutions is to replace the existing parking spaces for the new bikeshare stations. New York City committed to replacing 350 parking places as part of the expansion in Manhattan, Brooklyn, and Queens. As well, Barcelona converted 98 about 1.200 parking spaces for the allocation of the Bicing’s bikeshare stations (ITDP, 2018). In Paris, more than 1.450 on-street parking spaces were replaced to create space for 4.000 bikes of the Velib’ bikeshare system (ITDP, 2013). Currently, Rome possesses 74.625 parking spaces by payment and 16.816 free parking places with a 3-hour limit for non-residents (Dipartimento Mobilità e Trasporti, 2019).

In Figure 48 are shown the situations before and after the implementation of the electrical bikeshare system Bicimad in Madrid. The pictures show “Plaza del Sol”, the historical center point of Madrid and also how the former parking spaces for motorcycles were replaced for a 48-docks bikeshare station.

Figure 48 Parking replacement in Madrid. Comparison years 2014 and 2018 Source: Google Street View

Figure 49 shows the replacement of about four parking lots in order to allocate a 24-docks station in Madrid. As well it is possible to appreciate the “Ciclocarril” in the right lane, which indicates that it is shared with cyclists and limits the speed to 30 kilometers per hour.

Figure 49 Parking replacement in Madrid. Comparison years 2008 and 2018 Source: Google Street View

Figure 50 shows a similar measure, done for the bikeshare system Ecobici in Mexico D.C., which nowadays is one of the most successful systems in terms of market penetration (trips/1.000 inhabitants).

Figure 50 Parking replacement in Mexico D.C. Comparison years 2011 and 2017 Source: Google Street View

Figure 51 shows that as well in Barcelona the same measure was taken in order to give space to the bike-sharing system of Bicing. About four parking lots were replaced to give space to a station of more than 25 docks.

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Figure 51 Parking replacement in Barcelona. Comparison years 2014 and 2018 Source: Google Street View

Parking replacement has been used in several previous experiences to give space to new bikeshare systems and could be used as well, to discourage the use of the car and encourage the use of the public bicycle. In Rome, the high amount of street parking lots would allow implementing a similar measure and in most cases, it would be necessary, as the readily available public space is not high and a reduction of the available space for pedestrians should be completely avoided.

6.5. Demand boosting strategies

In this chapter are included some additional strategies that would help to increase the demand of users of the bikeshare system.

• Make bikeshare visible in trip-planning platforms: a product with high-visibility has a bigger chance to be bought. Making the bikeshare alternative visible in at least the main trip-planning apps as Moovit and Google maps would increase the attractiveness of the service. A non-bike user by looking the first time the list of alternatives of the apps might not choose the bikeshare as an option, but probably will start considering it as an option after several times of looking that bikeshare represent a good alternative in terms of low trip-times and low costs per trip. This simple action might contribute greatly to achieve the desired modal split.

• Make stations highly visible: a system that has low visibility will tend to attract a lower demand, as users probably will not realize the existence of the program or

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simply ignore it as an efficient alternative to complete their trips. Users have to perceive that the bikeshare stations are present in their common trip destinations and realize how other citizens make use of it in order to gain confidence in the service and consider it as an alternative for future trips.

• Tie-in with public transport passes: An integration with public transport means is very important in order to present in overall a better alternative than private transport modes and to deal with the last-mile problem. In Milan, 70% of the users integrate Bikemi with the metro network, 55% with tram and 40% with buses (Università degli studi di Milano, 2007).

In a study of barriers to bikeshare conducted in the cities of Melbourne and Brisbane, it was found that the second factor (after “more bike lanes and paths”) that would encourage a non-user of bikeshare to become a CityCycle member would be “Automatically open to Go Card holders” (the public transport card in Brisbane) (Fishman, Washington, Haworth, & Mazzei, 2014). Just the verification process (that anyway is very important for security reasons) can be enough justification for users to avoid doing the registration to the bikeshare, so if this process is simplified, the attracted users might be higher. Doing a tie-in with annual public transport passes in Rome should be highly considered. Complementarily, a discount on the annual memberships could be offered to the annual public transport users, as it is done currently in Madrid (instead of paying 25 euros, they pay 15 for the annual membership).

• Signal the connection possibility to bikeshare: indicating clearly at the exit of the metro and tram stations that there is the possibility to do a connection with the bikeshare can help to boost the demand and to deal with the last-mile problem. With an easy way to find the stations, it is more likely that a user will do a trip using bikeshare.

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• Marketing campaigns: a part of the yearly costs has to be destined for marketing campaigns, for the citizens to get to know what the bikeshare is offering and what it is about. The more familiar the citizens are to the system, the more likely it is for them to use it. As well, usage of the bikeshare system should be as simple as possible or at least very clear, as a new user could find as a barrier not knowing how the system works. The confusion about system usage should be reduced as much as possible.

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7. COST-BENEFIT ANALYSIS

In this chapter, the cost-benefit analysis is presented, taking into account the system size determined previously (number of stations, docks, and bicycles). For that scope, the electrical bikeshare system of Madrid (Bicimad) was analyzed, regarding the investment needed, the exploitation costs and revenues forecasts (Barahona, 2014), then the costs per unit were adjusted and the number of units of each one of the items was calculated, taking into account the difference of the sizes of both systems. As well, the Bikeshare Business and Implementation Plan from the City of Baton Rouge (Baton Rouge Area Foundation, 2016) , the Memphis bikeshare feasibility study (Alta planning and design, 2013) and the Bikeshare Opportunities in New York document (NYC Dept. City Planning, 2009) were taken into account for doing the financial analysis in this chapter.

As a first step, it is necessary to estimate the number of OD trips that are possible according to the area served by the bikeshare system. In that sense, the zones contained inside a buffer of 500m around the stations were identified. However, the area served might not cover completely a determined OD zone (e.g. can cover just 50% of the zone), thus, considering completely the trips originated and attracted in that zone would lead to an over-estimation of the trips. As an example, in Figure 52 is possible to see that the area served in Zone 30 is reduced and that considering all the trips from the OD zone would affect strongly the estimation of OD trips.

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Figure 52 Area served in zone 30

For solving this issue, a reduction factor was applied to each Origin-Destination couple. For the origins, the reduction factor was done according to the proportion of the population of that zone served by the bikeshare. For the destinations, the reduction factor was applied according to the proportion of employment of that zone, covered by the bikeshare system. In Table 9 the reduction factors are presented, for each one of the relevant OD Zones. As an example, the OD couple Zone 30-Zone 10 has 3.384 trips in the morning peak hour, but as just the 7.32% of the population of Zone 30 is served by the bikeshare and 99.91% of the employment of Zone 10 is served, the relevant trips are estimated as 247.56.

Table 9 Reduction factors for OD trips VAS Total Area served by bikeshare Proportion Zone Population Employment Population Employment Population Employment 10 49.345 159.462 49.249 159.319 99,80% 99,91% 20 129.294 175.461 125.809 174.516 97,30% 99,46% 21 97.426 86.226 95.266 84.209 97,78% 97,66% 22 158.591 173.273 64.151 114.877 40,45% 66,30% 30 221.076 79.657 16.185 5.004 7,32% 6,28% 31 299.791 76.627 297.765 74.683 99,32% 97,46% 32 228.209 144.980 3.899 2.958 1,71% 2,04% 33 154.658 68.014 3.668 5.208 2,37% 7,66% 42 164.971 71.108 23.977 6.322 14,53% 8,89% 105

By performing the calculation of the OD trips with their respective proportions, it can be determined that 70.258 OD trips are contained inside the area served by the bikeshare. Accordingly, knowing that the peak hour corresponds to 10% of the daily trips, it can be estimated a total of 702.582 trips per day.

Table 10 OD trips estimation Item Value Note Total estimated OD trips that are contained in the area served Trips peak hour 70.258 by the bikeshare Daily trips made in 10% Proportion of trips made in peak hour and the whole day peak hour Estimated trips that are contained in the area served by the Daily trips 702.582 bikeshare, daily

As recommended by The Bikeshare Planning Guide (ITDP, 2018), uptake rates can be used to do an initial demand estimation, which are the percentage of the total trips that would be done using the bikeshare system. Normally, a 3% uptake rate is considered for the Conservative scenario, 6% for the Intermediate and 9% for the Optimistic scenario of bikeshare demand. However, due to the barriers identified in the previous chapters and in order to keep a conservative estimation of the bikeshare demand in Rome, an uptake rate of 5% is assumed for the Intermediate Scenario and 7% for the Optimistic.

Rome is well known for being a capital of tourism, thus, it is also important to take into account the tourists that would make use of the bikeshare system. As for 2017, 18.028.393 tourists arrived in the province of Rome with a total of 42.230.518 nights spent. From this amount, a number of 12.403.488 tourists arrived in the city of Rome, with 29.293.952 nights spent, which translates into an average of 2,36 nights spent by tourist (Ente Bilaterale Turismo Lazio, 2017). Taking as a reference the “Bike-Share Opportunities in New York City” document (NYC Dept. City Planning, 2009), it can be assumed a number of 1 trip per each night spent by a tourist (close as well to the 1,03 trips/day generated by residents in the Service Area), resulting in 29.293.952 trips per year. For tourists, lower uptake rates will be assumed, as the fee proposed is higher than the one that residents would have to pay.

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Thus, an uptake ratio of 1% is assumed for the Conservative Scenario, 2% for the Intermediate and 3% for the Optimistic.

The uptake rates for tourists could be in a real implementation even higher. In the year 2007, in Paris, another very important city in terms of tourism, about 28 million overnight visitors were received, as well, in the same year, Velib’ sold 2.5 million one day tourist passes in its first six months, which represents an uptake rate of about 18% (NYC Dept. City Planning, 2009). Thus, the contemplated uptake rates are considered to be highly possible and even conservative for the potential of this segment of users. Table 11 resumes the most relevant information on the considered scenarios.

Table 11 Considered scenarios Residents Tourists SCENARIO Daily trips Daily trips Uptake rate Uptake rate Bikeshare Bikeshare Conservative 3% 21.077 1% 803 Intermediate 5% 35.129 2% 1.605 Optimistic 7% 49.181 3% 2.408

7.1. Costs estimation

After defining each one of the scenarios, it is also necessary to determine the investment costs and the yearly costs of Operation and Maintenance (O & M). The costs per unit are based on the Cost-Benefit analysis done for Bicimad in Madrid, which is considered to be similar regarding the bike’s and station’s technology. The capital investments are resumed in Table 12, including the purchase of the vehicles needed for the rebalancing and maintenance operations. For the latter ones, the cost per unit was determined taking into account reference prices in the market.

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Table 12 Capital investment Lifespan Investment Equipment Cost per unit (€) Units (years) (€) Bikes 4 1.000 6.800 6.800.000 Docks 6 785 11.570 9.083.838 Stations setting- 12 8.264 377 3.115.701 up Terminals 6 5.372 377 2.025.206 (totems) Vehicles Light vehicles 12 10.500 6 63.000 Motorbikes 12 1.500 9 13.500 Vans 12 18.000 30 540.000 Box cars 12 10.200 9 91.800

In Table 13 are shown the yearly costs of the necessary staff to run operations, which in total sum up to 5.6 million euros each year. The organizational structure is based as well on the cost-benefit done for Bicimad.

Table 13 Cost of staff Cost per unit Annual cost Cost of staff N. Workers N. of unit times (€) (€) Administration and

management Director 389 1 220 85.624 Service manager 311 1 220 68.477 Equipment manager 168 4 880 147.963 Administrative staff 103 6 1.320 135.630 Maintenance 135 60 13.200 1.775.532 Transport Staff 142 108 23.760 3.369.643 Clothing 50.400 Total personal cost 5.633.270

Finally, additional operation and management costs were determined, as the fuel, materials and other management costs. The costs for bike spares were established by calculating the cost per unit in the Bicimad system (total costs over bike units of Bicimad) and then expanding it according to the size of the proposed system in Rome. Similarly, the station spares unitary costs were determined (total costs over station units of Bicimad) and then expanded for the current system. 108

Table 14 Operation and Management costs

Cost N° Equipment Cost per unit (€) Annual cost (€)

Fuel 1 50.000 50.000 Materials Workshop hire 1 55.000 55.000 Civil responsibility insurance 1 240.000 240.000 Bike spares 6.800 26 180.122 Station spares 377 103 38.944 Other management costs 0 Marketing campaign 1 150.000 150.000 Total cost 714.066

Initial investment sum up to 21.7 million euros and total yearly Operation and Management costs sum up to 6.3 million euros, which is close to the 6 million euro that Clear Channel invests in Milano for the costs of Operation and Maintenance of Bikemi every year (Ilsole24ore, 2017).

7.2. Revenues estimation

In this chapter, the revenues estimation was performed based on the procedure done in the “Bikeshare Business and Implementation Plan from the City of Baton Rouge” (Baton Rouge Area Foundation, 2016). In that sense, a pricing scheme was assumed, based on the one of Madrid, as it is one of the only bikeshare systems that is working completely with e-bikes. In Table 15 the proposed revenue scheme is presented, with just a slight reduction in the price for non-members, comparing to the one in Madrid.

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Table 15 Proposed revenue scheme

PROPOSED REVENUE SCHEME

Members

Annual membership 25 €

First 45 min 0,5 €

Following 30 min 0,6 €

Non-Members

First 45 min 2 € Following 30 min 3 €

Then, in order to do an estimation on the number of annual members, the relation daily trips per member was studied in the bikeshare systems of Barcelona, Madrid, Mexico D.C. and New York. Due to the similar amount of stations and number of bikes, and in order to do a conservative estimation on the number of annual members, the ratio 0.38 daily trips/member of Barcelona’s Bicing was used, the results are presented in Table 16.

Table 16 Annual income for memberships Daily member Annual Annual income SCENARIO trips memberships (€) Conservative 20.656 55.000 1.375.000

Intermediate 34.427 90.000 2.250.000

Optimistic 48.197 125.000 3.125.000

Next, the trip’s statistics from the year 2016 to 2019 of Bicimad were analyzed to determine a ratio between the Non-member and Member trips, which is approximately 2% as shown in Table 17. The same ratio was applied in order to determine the revenue type for the proposed system (the great majority of trips are performed by members). As well it is assumed that 80% of the trips would be done in the first price range (for member and non- members), which would be a conservative estimation, as in Madrid it is about 70%, meaning that the other 30% pay higher fees for the extra-time. The revenue type, the annual income

110 estimation and the daily use for each one of the considered scenarios are presented in Table 18.

Table 17 Trips and members statistics from Bicimad-Madrid Statistic/Year 2016 2017 2018 2019 Average Average daily trips - members 7.524 8.972 9.612 10.344 9.113 Average daily trips - non- 163 206 180 148 174 members Average total trips 7.686 9.179 9.791 10.491 9.287 Ratio non-member/member 2,2% 2,3% 1,9% 1,4% 1,9% trips Average members 65.436 64.017 65.593 66.528 65.394

Table 18 Revenues estimation according to each scenario INTERMEDIATE CONSERVATIVE SCENARIO OPTIMISTIC SCENARIO REVENUE SCENARIO CATEGORY Annual Annual Annual Daily use Daily use Daily use income (€) income (€) income (€) Members: 16.525 3.015.763 27.541 5.026.272 38.558 7.036.780 First 45 min Members: 4.131 1.658.670 6.885 2.764.449 9.639 3.870.229 Following 30 min Non-members: 337 246.185 562 410.308 787 574.431 RESIDENTS First 45 min Non-members: 84 153.865 141 256.442 197 359.019 Following 30min

Non-members: 642 468.703 1284 937.406 1926 1.406.110 First 45 min Non-members: 161 292.940 321 585.879 482 878.819 TOURISTS Following 30min

TOTAL 21.880 5.836.126 36.734 9.980.757 51.588 14.125.388

7.3. Net Present Value estimation

The Net Present Value (NPV) is used to determine the profitability of the proposed bikeshare system. It is defined as the difference between the present value of cash inflows and the present value of cash outflows over a period of time (Investopedia, 2019). NPV is calculated as follows:

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푇 푉푡 푁푃푉 = ∑ − 푉 (1 + 푟)푡 0 푡=1

푊ℎ푒푟푒: 푉푡𝑖푠 푡ℎ푒 푛푒푡 푐푎푠ℎ 𝑖푛푓푙표푤 − 표푢푡푓푙표푤푠 푑푢푟𝑖푛푔 푎 푠𝑖푛푔푙푒 푝푒푟𝑖표푑 푡 푟 𝑖푠 푡ℎ푒 푑𝑖푠푐표푢푛푡 푟푎푡푒 푇 𝑖푠 푡ℎ푒 푛푢푚푏푒푟 표푓 푝푒푟𝑖표푑푠

A positive NPV indicates that the project will be profitable and a negative NPV indicates that the project will result in a net loss.

The operation and management costs and the revenues in each type of scenario are resumed in Table 19. A 4% discount rate is used, following the recommendation of the European Commission for the period 2014-2020, that establishes this discount rate value as a reference for the capital’s real cost of opportunity in the long term (Commissione Europea, 2014).

Table 19 Revenues and Costs resume table Item Value (€/year) Operation and management costs 6.347.335 Revenues conservative scenario 7.211.126 Revenues intermediate scenario 12.230.757 Revenues optimistic scenario 17.250.388 Discount rate 4,00% Sponsorships/Advertisements revenues 4.500.000

In the following tables, the results of the NPV for the Conservative, Intermediate and Optimistic Scenario are presented. Regarding the capital purchases, it is assumed that every four years a replacement of 75% of the total bikes has to be done, every six years a purchase of new dockings equivalent to the 50% of the initial ones and ever six years a purchase of new terminals also equivalent to 50% of the initial number. Revenues and O & M costs are assumed to remain steady in the considered period. As well, a 12 year period is considered, as it coincides with the lifespan of the stations and is a common factor of the lifespan of bikes and docks.

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Table 20 Net Present Value – Conservative scenario CONSERVATIVE SCENARIO CAPITAL O&M REVENUES OTHER PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) REV. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 7.211.126 863.790 830.568 2 -6.347.335 7.211.126 863.790 798.623 3 -6.347.335 7.211.126 863.790 767.906 4 -6.347.335 7.211.126 863.790 738.372 5 -5.100.000 -6.347.335 7.211.126 -4.236.210 -3.481.856 6 -6.347.335 7.211.126 863.790 682.666 7 -5.554.522 -6.347.335 7.211.126 -4.690.732 -3.564.571 8 -6.347.335 7.211.126 863.790 631.163 9 -5.100.000 -6.347.335 7.211.126 -4.236.210 -2.976.305 10 -6.347.335 7.211.126 863.790 583.546 11 -6.347.335 7.211.126 863.790 561.102 12 -6.347.335 7.211.126 863.790 539.521 NPV -25.622.311

Table 21 Net Present Value – Intermediate scenario INTERMEDIATE SCENARIO CAPITAL O&M REVENUES OTHER PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) REV. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 12.230.757 5.883.422 5.657.136 2 -6.347.335 12.230.757 5.883.422 5.439.554 3 -6.347.335 12.230.757 5.883.422 5.230.340 4 -6.347.335 12.230.757 5.883.422 5.029.173 5 -5.100.000 -6.347.335 12.230.757 783.422 643.915 6 -6.347.335 12.230.757 5.883.422 4.649.754 7 -5.554.522 -6.347.335 12.230.757 328.899 249.936 8 -6.347.335 12.230.757 5.883.422 4.298.959 9 -5.100.000 -6.347.335 12.230.757 783.422 550.422 10 -6.347.335 12.230.757 5.883.422 3.974.629 11 -6.347.335 12.230.757 5.883.422 3.821.758 12 -6.347.335 12.230.757 5.883.422 3.674.768 NPV 21.487.299

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Table 22 Net Present Value – Optimistic scenario OPTIMISTIC SCENARIO CAPITAL O&M REVENUES OTHER PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) REV. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 17.250.388 10.903.053 10.483.705 2 -6.347.335 17.250.388 10.903.053 10.080.485 3 -6.347.335 17.250.388 10.903.053 9.692.774 4 -6.347.335 17.250.388 10.903.053 9.319.975 5 -5.100.000 -6.347.335 17.250.388 5.803.053 4.769.686 6 -6.347.335 17.250.388 10.903.053 8.616.841 7 -5.554.522 -6.347.335 17.250.388 5.348.531 4.064.444 8 -6.347.335 17.250.388 10.903.053 7.966.754 9 -5.100.000 -6.347.335 17.250.388 5.803.053 4.077.148 10 -6.347.335 17.250.388 10.903.053 7.365.712 11 -6.347.335 17.250.388 10.903.053 7.082.415 12 -6.347.335 17.250.388 10.903.053 6.810.015 NPV 68.596.908

As it is possible to appreciate in Table 20, in the conservative scenario a negative NPV would be obtained (- 25,62 M€ ) indicating that the investment made would result in losses. In the Intermediate scenario, shown in Table 21, the NPV is positive (21,48 M€), indicating that the project would be profitable in the considered period. In the Optimistic scenario, shown in Table 22, is possible to see that the profitability would be much higher due to a higher NPV (68,59 M€). The uptake rates’ break-value to obtain a cero NPV is estimated to be a combination of 4,103%for residents and 1,5% for tourists.

In order to understand how additional funding would influence on the financial feasibility of the project, the next three scenarios include additional revenues from Sponsorships and/or Advertisements. In Milan, Bikemi makes up to 4 million euros in advertisement yearly, from personalized announcements and brands in all useful spaces, as in racks, wheel covers, and graphics on the pavement of stations (Ilsole24ore, 2017).

In New York, the City Group, which runs Citibank, paid 41 million dollars to be the lead sponsor of the bikeshare program for five years. MasterCard paid an additional 6.5 million dollars to place their logo on each one of the docks for the same period of time (The New York Times, 2012).

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Taking into account that Bikemi has 282 stations and the proposed system in Rome 377 stations and assuming as well proportionality between bikeshare system’s size and advertisement revenues, it can be estimated that the potential Sponsorships/Advertisements revenues are about 5.3 million euros. However, in order to do a conservative calculation, revenue of 4.5 million euros (12.5 % more) is assumed yearly.

Table 23 Net Present Value – Conservative scenario with sponsorships/advertisements CONSERVATIVE SCENARIO-WITH SPONSORHIPS/ADVERTISEMENTS CAPITAL O&M REVENUES OTHER PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) REV. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 7.211.126 4.500.000 5.363.790 5.157.491 2 -6.347.335 7.211.126 4.500.000 5.363.790 4.959.126 3 -6.347.335 7.211.126 4.500.000 5.363.790 4.768.390 4 -6.347.335 7.211.126 4.500.000 5.363.790 4.584.990 5 -5.100.000 -6.347.335 7.211.126 4.500.000 263.790 216.816 6 -6.347.335 7.211.126 4.500.000 5.363.790 4.239.081 7 -5.554.522 -6.347.335 7.211.126 4.500.000 -190.732 -144.941 8 -6.347.335 7.211.126 4.500.000 5.363.790 3.919.269 9 -5.100.000 -6.347.335 7.211.126 4.500.000 263.790 185.336 10 -6.347.335 7.211.126 4.500.000 5.363.790 3.623.585 11 -6.347.335 7.211.126 4.500.000 5.363.790 3.484.216 12 -6.347.335 7.211.126 4.500.000 5.363.790 3.350.208 NPV 16.610.521

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Table 24 Net Present Value – Intermediate scenario with sponsorships/advertisements INTERMEDIATE SCENARIO-WITH SPONSORHIPS/ADVERTISEMENTS CAPITAL O&M REVENUES OTHER PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) REV. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 12.230.757 4.500.000 10.383.422 9.984.059 2 -6.347.335 12.230.757 4.500.000 10.383.422 9.600.057 3 -6.347.335 12.230.757 4.500.000 10.383.422 9.230.824 4 -6.347.335 12.230.757 4.500.000 10.383.422 8.875.792 5 -5.100.000 -6.347.335 12.230.757 4.500.000 5.283.422 4.342.587 6 -6.347.335 12.230.757 4.500.000 10.383.422 8.206.169 7 -5.554.522 -6.347.335 12.230.757 4.500.000 4.828.899 3.669.567 8 -6.347.335 12.230.757 4.500.000 10.383.422 7.587.064 9 -5.100.000 -6.347.335 12.230.757 4.500.000 5.283.422 3.712.062 10 -6.347.335 12.230.757 4.500.000 10.383.422 7.014.668 11 -6.347.335 12.230.757 4.500.000 10.383.422 6.744.873 12 -6.347.335 12.230.757 4.500.000 10.383.422 6.485.455 NPV 63.720.130

Table 25 Net Present Value – Optimistic scenario with sponsorships/advertisements OPTIMISTIC SCENARIO-WITH SPONSORHIPS/ADVERTISEMENTS CAPITAL O&M REVENUES OTHER PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) REV. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 17.250.388 4.500.000 15.403.053 14.810.628 2 -6.347.335 17.250.388 4.500.000 15.403.053 14.240.988 3 -6.347.335 17.250.388 4.500.000 15.403.053 13.693.258 4 -6.347.335 17.250.388 4.500.000 15.403.053 13.166.594 5 -5.100.000 -6.347.335 17.250.388 4.500.000 10.303.053 8.468.358 6 -6.347.335 17.250.388 4.500.000 15.403.053 12.173.256 7 -5.554.522 -6.347.335 17.250.388 4.500.000 9.848.531 7.484.074 8 -6.347.335 17.250.388 4.500.000 15.403.053 11.254.860 9 -5.100.000 -6.347.335 17.250.388 4.500.000 10.303.053 7.238.788 10 -6.347.335 17.250.388 4.500.000 15.403.053 10.405.751 11 -6.347.335 17.250.388 4.500.000 15.403.053 10.005.529 12 -6.347.335 17.250.388 4.500.000 15.403.053 9.620.701 NPV 110.829.740

As it is possible to appreciate in Table 23, the additional revenues would make the project profitable in the Conservative scenario (NPV of 16,61 M€), with a discount rate of 4%. As well the profitability of the Intermediate and Optimistic scenarios would increase significantly (NPVs of 63,72 M€ and 110,83 M€), which as well reduces the risk of having 116 losses in the project and having the need to increase the fare in order to cover expenses (as happened in Barcelona’s Bicing), which at the end results in benefits for the users. It is clear that an increase in the fare might also result in a decrease of the bikeshare use and in the global benefits from users taking a more sustainable mode of transport.

With a yearly value of 2,75 M€ of alternative funding, the resulting NPV would still be positive, thus, it can be considered as the minimum value of funding in order to guarantee the profitability of the project (in the conservative scenario). Advertisements and Sponsorships have been proposed as additional revenue sources, however, public funding can be used as an alternative.

7.4. Sensitivity analysis

In order to understand the sensitivity of the obtained results, a variation of the discount rate was made. The results are resumed in Table 26.

Table 26 Sensitivity Analysis - NPV variation with changes in Discount Rate Net Present Value (M €) Scenario/Discount Rate r = 2,5% r = 3% r = 3,5% r = 4% r = 4,5% r = 5% r = 5,5% Conservative -26,14 -25,96 -25,79 -25,62 -25,46 -25,31 -25,16 Intermediate 25,35 24,01 22,72 21,49 20,31 19,18 18,10 Optimistic 76,84 73,97 71,22 68,60 66,08 63,67 61,36 Conservative- 20,02 18,83 17,70 16,61 15,57 14,58 13,62 Spons./Advert. Intermediate- 71,51 68,80 66,20 63,72 61,34 59,07 56,89 Spons./Advert. Optimistic-Spons./Advert. 123,00 118,76 114,71 110,83 107,12 103,56 100,15

As it is possible to appreciate Table 26, the resulting NPVs vary very slightly with respect to changes in the discount rate. As an example, with a discount rate of 5,5%, the NPV of the Conservative Scenario is still negative and the one of the Intermediate Scenario (without additional funding) remains positive. With the variation made on the discount rate, the analysis made remains valid.

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7.5. External costs/benefits of bikeshare

External costs arise when the social or economic activities of a person (or group) generate impacts on other users, that are not accounted for or compensated by the first person (or group) (European Commission, 2019). For example, the impact on human health is not taken into account or compensated by a user who decides to fulfill a trip using the car. In this chapter, additional benefits/costs of a higher level of cycling and reduction in the use of motorized private modes are analyzed.

The external costs/benefits considered in this chapter are; accidents, air pollution, climate change, noise, congestion and, public health. The Handbook on the external costs of transport from the European Commission (version 2019) is used to calculate the monetary value of the considered externalities, except the Health benefits, which were determined using the Health Economic Assessment Tool (HEAT) of the World Health Organization (WHO).

As a first step, it is necessary to analyze the possible redistribution of the trips under each one of the considered scenarios. For that, it was assumed that the proportion of change on each one of the modes would be equivalent to the proportion of modal change determined for residents in the study of (Tripodi & Persia, 2015) due to an implementation of a new bikeshare system in Rome. The redistribution of trips under each one of the considered scenarios is resumed in Table 27.

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Table 27 Modal shift by scenario

Transport CURRENT CONSERVATIVE INTERMEDIATE OPTIMISTIC mode Trips Modal Split Trips Modal Split Diff. Trips Modal Split Diff. Trips Modal Split Diff.

Car 2.777.040 50,00% 2.766.225 49,81% -0,19% 2.759.015 49,68% -0,32% 2.751.806 49,55% -0,45%

Motorcycle 860.882 15,50% 860.590 15,49% -0,01% 860.395 15,49% -0,01% 860.200 15,49% -0,01%

Bus 945.094 17,02% 939.513 16,92% -0,10% 935.793 16,85% -0,17% 932.073 16,78% -0,23%

Light rail 73.206 1,32% 72.774 1,31% -0,01% 72.486 1,31% -0,01% 72.198 1,30% -0,02%

Train 586.829 10,57% 584.950 10,53% -0,03% 583.697 10,51% -0,06% 582.444 10,49% -0,08%

Walk 277.704 5,00% 275.625 4,96% -0,04% 274.240 4,94% -0,06% 272.854 4,91% -0,09%

Bicycle 33.324 0,60% 54.402 0,98% 0,38% 68.454 1,23% 0,63% 82.505 1,49% 0,89%

TOTAL 5.554.080 100,0% 5.554.080 100,00% 0,00% 5.554.080 100,00% 0,00% 5.554.080 100,00% 0,00%

Then, considering that the average trip distance in private modes is 12,8 km and in public transport is 11,6 km in Rome (Roma Servizi per la Mobilità, 2015), the overall traveled distance in a year was calculated, for each one of the scenarios and each one of the relevant modes.

Table 28 Passenger kilometers per year by scenario Traveled distance per year (pkm/year in millions) Transport Mode Current Conservative Intermediate Optimistic Car 12.974 12.924 12.890 12.856 Motorcycle 4.022 4.021 4.020 4.019 Bus 4.002 3.978 3.962 3.946 Light rail 310 308 307 306 Train 2.485 2.477 2.471 2.466

7.5.1. Accidents

External accident costs are defined as the social costs of traffic accidents that are not covered by risk-oriented insurance premiums, including; human costs, medical costs, administrative costs, production losses, and material damage. The total external cost per casualty considered by the Handbook in Italy amounts to 3,25 million euros, which includes human costs, production loss, medical costs, and administrative costs. (European Commission, 2019).

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The average external accidents costs for land-based modes are taken from the Handbook on external costs of transport from the European Commission, however, these don’t include the accidentality costs for bikes. Thus, to do this estimation, a fatality rate in Italy of 5,1 cyclists per hundred million km was used (The International Transport Forum, 2018). The fatalities estimation is presented in Table 30.

Table 29 Average external accident costs for land-based modes for Italy (European Commission, 2019) Transport Mode €-cent per pkm Passenger car 4,40 Motorcycle 5,90 Bus/Coach 0,50 Conventional passenger train 0,34

Table 30 Cyclist fatalities estimation Item/Scenario Current Conservative Intermediate Optimistic Distance traveled 49,87 81,41 102,44 123,47 (million pkm/year) Fatalities per year 2,54 4,15 5,22 6,30

In Table 31 the accident costs are given by scenario, with their corresponding differences with respect to the current scenario. The global costs in accidentality result in an increase in each one of the scenarios, due to higher accidentality rates related to the cycling activity with respect to other modes of transport. It is important to highlight that pedestrians and cyclists are the most vulnerable users on the Road, thus, it is more likely that an accident results in a fatality.

Table 31 Accident costs by scenario

Cost (M €/year) Difference (M €/year) Transport Mode Current Conservative Intermediate Optimistic Conservative Intermediate Optimistic Car 570,87 568,65 567,17 565,68 -2,22 -3,71 -5,19 Motorcycle 237,30 237,22 237,17 237,11 -0,08 -0,13 -0,19 Bus/Coach 20,01 19,89 19,81 19,73 -0,12 -0,20 -0,28 Conventional passenger train 9,50 9,47 9,45 9,42 -0,03 -0,06 -0,08 Bicycle 8,26 13,49 16,97 20,45 5,23 8,71 12,19 TOTAL 845,94 848,71 850,56 852,40 +2,77 +4,62 +6,46

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7.5.2. Air pollution

The Handbook of the European Commission associates the air pollution costs to four main impacts; health effects, crop losses, material, and building damage, and biodiversity loss. The most well-known and relevant impact, the health effects, are related to the increased risk of having respiratory and cardiovascular diseases due to the inhalation of air pollutants such as nitrogen oxides (푁푂푥), 푃푀10, and 푃푀2.5 particles, which subsequently generate medical costs, production loss at work and fatalities (European Commission, 2019).

Table 32 Average air pollution costs for land-based modes for Italy (European Commission, 2019) Transport Mode €-cent per pkm Passenger car-petrol 0,35 Passenger car-diesel 1,22 Motorcycle 1,35 Bus 0,78 Passenger train electric 0,01

Table 33 resumes the air pollution costs according to each one of the scenarios, and the reduction of costs obtained in each one of the cases.

Table 33 Air pollution costs by scenario

Cost (M €/year) Difference (M €/year) Transport Mode Current Conservative Intermediate Optimistic Conservative Intermediate Optimistic

Car-petrol 25,45 25,35 25,28 25,22 -0,10 -0,17 -0,23 Car-diesel 61,15 60,91 60,75 60,59 -0,24 -0,40 -0,56 Motorcycle 54,39 54,37 54,36 54,35 -0,02 -0,03 -0,04 Bus 31,22 31,04 30,91 30,79 -0,18 -0,31 -0,43 Passenger train electric 0,18 0,18 0,18 0,18 0,00 0,00 0,00 TOTAL 172,39 171,85 171,49 171,13 -0,54 -0,90 -1,26

7.5.3. Climate change

It is well known that transport results in the emission of greenhouse gases as 퐶푂2, 푁2푂 and,

퐶퐻4(methane), that contribute to climate change. Thus, the costs related to climate change are defined by the Handbook as the ones associated with all the effects of global warming, 121 such as biodiversity loss, frequent weather changes, sea-level rise and crop failures (European Commission, 2019).

Table 34 Average climate change costs for land-based modes for Italy (European Commission, 2019) Transport Mode €-cent per pkm Passenger car-petrol 1,14 Passenger car-diesel 1,11 Motorcycle 0,83 Bus 0,41

Table 35 resumes the climate change costs on each one of the scenarios, and the related reduction obtained, in millions of euros per year.

Table 35 Climate change costs by scenario

Cost (M €/year) Difference (M €/year) Transport Mode Current Conservative Intermediate Optimistic Conservative Intermediate Optimistic

Car-petrol 84,01 83,69 83,47 83,25 -0,33 -0,55 -0,76 Car-diesel 55,34 55,12 54,98 54,83 -0,22 -0,36 -0,50 Motorcycle 33,29 33,28 33,27 33,26 -0,01 -0,02 -0,03 Bus 16,36 16,27 16,20 16,14 -0,10 -0,16 -0,23 TOTAL 189,00 188,35 187,92 187,49 -0,65 -1,08 -1,52

7.5.4. Noise

Noise is defined as unwanted sounds of varying duration or intensity that cause physical or psychological harm to humans. Exposure to transport noise can cause health issues as; ischaemic heart disease, stroke, dementia, hypertension, and annoyance, that as well generate costs for the medical treatment. Table 36 gives the average noise costs generated by each one of the relevant transport modes, in terms of euro cents per passenger-kilometer.

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Table 36 Average noise costs for land-based modes for Italy (European Commission, 2019) Transport Mode €-cent per pkm Passenger car-petrol 0,89 Passenger car-diesel 0,92 Motorcycle 12,99 Bus 0,69 Passenger train electric 1,62

Table 37 gives the external costs generated by noise on each one of the considered scenarios, and the reductions obtained. Table 37 Noise costs by scenario

Cost (M €/year) Difference (M €/year) Transport Mode Current Conservative Intermediate Optimistic Conservative Intermediate Optimistic

Car-petrol 65,26 65,00 64,83 64,66 -0,25 -0,42 -0,59 Car-diesel 45,76 45,58 45,46 45,34 -0,18 -0,30 -0,42 Motorcycle 522,36 522,18 522,06 521,94 -0,18 -0,30 -0,41 Bus 27,79 27,62 27,51 27,40 -0,16 -0,27 -0,38 Passenger train electric 45,20 45,04 44,94 44,83 -0,16 -0,26 -0,37 TOTAL 706,36 705,42 704,80 704,18 -0,93 -1,55 -2,17

7.5.5. Congestion

Congestion is a condition where vehicles are delayed when completing a trip. These delays generate significant costs in terms of the time lost, which could be used in other productive activities. The road congestion costs presented in the Handbook, are outputs of a model designed to estimate the overall magnitude of this externality at the European level, from which representative averages are derived (European Commission, 2019). Thus, in order to improve the estimation, the specific congestion costs for Italy were taken, as shown in Table 38.

Table 38 Average congestion costs for Italy (European Commission, 2019) Transport Mode €-cent per Vkm €-cent per pkm Car 3,90 2,44 Bus 7,80 0,40

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The congestion costs by scenario are presented in Table 39, where it is possible to appreciate that the costs generated by cars are much higher than those generated by the buses, on each one of the scenarios, as it is expected. Table 39 Congestion costs by scenario

Cost (M €/year) Difference (M €/year) Transport Mode Current Conservative Intermediate Optimistic Conservative Intermediate Optimistic

Car 316,57 315,33 314,51 313,69 -1,23 -2,05 -2,88 Bus 15,92 15,83 15,77 15,70 -0,09 -0,16 -0,22 TOTAL 332,49 331,16 330,28 329,39 -1,33 -2,21 -3,10

7.5.6. Health: reduction in mortality

Physical inactivity is a significant public health problem, approximately 3.2 million deaths are attributable to it every year. Regular physical activity is a protective factor for the prevention and treatment of noncommunicable diseases (NCDs) as; heart disease, stroke, diabetes, breast cancer, and colon cancer. As well prevents other NCD risk factors as hypertension, overweight, and obesity, and contributes to an improvement in mental health, a delay in the onset of dementia and improved quality of life (World Health Organization, 2019).

Nowadays it might be difficult for some groups of citizens (as workers) to make fit in their schedules a regular physical activity, thus, cycling provides not only an efficient and ecological way of transport but a way to increase physical activity levels while completing the everyday trips.

The benefits in health were calculated using the Health Economic Assessment Tool (HEAT) of the World Health Organization (WHO), which considers the increase of time cycling and calculates the reduction on fatalities during a certain period of time, based on the reduced mortality risk given the higher levels of physical activity. In Figure 53 is included the list of parameters used for the calculation and in Figure 54 is shown an example of the results obtained (for the intermediate scenario).

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Figure 53 Calculation parameters - HEAT

Figure 54 HEAT assessment example – Intermediate scenario

As a result of increased cycling, in the conservative scenario of bikeshare, 43 premature deaths would be prevented, in the intermediate scenario 71 premature deaths and in the optimistic Scenario an equivalent of 100 premature deaths in a 12 year period.

7.5.7. Financial analysis with external benefits

Taking into account all the external benefits of the bikeshare, the total benefit per year was calculated for each one of the considered scenarios, as shown in Table 40.

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Table 40 External cost reductions resume Cost reduction (M €/year) Category Conservative Intermediate Optimistic Accidents -2,77 -4,62 -6,46 Air pollution 0,54 0,90 1,26 Climate change 0,65 1,08 1,52 Noise 0,93 1,55 2,17 Congestion 1,33 2,21 3,10 Health: mortality 11,64 19,22 27,07 reduction TOTAL 12,32 20,35 28,65

Naturally, the scenario with the higher benefits is the optimistic, as it implies the higher level of cycling and replacement of trips made by motorized modes. It is possible to appreciate also, that the health benefits are the most relevant ones on each one of the scenarios. The second biggest benefit is the reduction in the costs related to congestion. As well, it is important to highlight that several road safety measures should be taken to avoid an increasing number of fatalities related to cycling.

Table 41 Resume of external benefits by scenario and NPVs TOTAL DISCOUNTED NPV WITH SCENARIO EXTERNAL BENEFITS TO EXTERNAL BENEFITS 2019 (M€) (M€) Conservative 115,62 90,00 Intermediate 190,99 212,47 Optimistic 268,88 337,48

As it is possible to appreciate in Table 41, the total discounted external benefits (to 2019) during a 12-year period would be superior to 115 million euros in the conservative scenario, around 190 million euros for the intermediate scenario and approximately 270 million euros in the optimistic scenario. As well, the NPV (without considering additional revenues, such as advertisements) on each one of the scenarios is superior to cero, indicating the profitability of the project. These estimates help to understand that public funding, if necessary, is highly justified due to the monetary value of the external benefits obtained.

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8. CONCLUSIONS

This study has analyzed the feasibility of a new bikeshare system implementation in Rome. The analysis contributes to existing knowledge by identifying the potential barriers to bikeshare in Rome, suggesting strategies to deal with them, proposing the characteristics of a new bike-sharing program, establishing a suitable service area, determining the system size, and calculating its financial feasibility, with and without the external benefits/costs of the bikeshare.

The adopted methodology consisted of doing first a literature review about the bikeshare context and the previous experiences from a global and a local perspective in Rome. Common features of successful bikeshare systems were studied. The examination showed that a high station density (stations per km2), bike density (bikes per 1.000 residents), and a sufficiently large service area (km2) would result in an increased market penetration (trips per 1.000 residents). The first one would reduce the walking distances to stations and provide a level of redundancy in the system, the second would ensure an adequate offer to meet demand, and the third would guarantee that there is a sufficiently high number of OD trips to be satisfied.

Contextual analysis in Rome was performed to study the potential barriers for a new bikeshare implementation in the city, and strategies were proposed to deal with them. The main identified barriers for a new bikeshare implementation in Rome are; lack of cycling infrastructure, strong culture for private vehicles, low safety related to cycling, financial sustainability and lack of political will. The main proposed strategies to deal with barriers are; use of e-bike, ensuring new infrastructure projects, organizing general bike-use campaigns, making bikeshare more competitive with private modes, generating safety campaigns and road traffic measures, securing financing and building political will. E-bikes have the potential to deal better with physical limitations, strong slopes, harsh weather conditions, and long trips. As well, they reduce the probability of rebalancing problems and normally result in higher ridership, due to its increased attractiveness. Furthermore, they

127 can reach a higher modal shift from motorized modes than normal bicycles. As well, competitivity of bikeshare in terms of time is essential to make possible a modal shift, as private modes are considered to be traditionally more convenient because of lower trip times. On another hand, it was found that there has to be a security emphasis in stations to reduce theft. An intuitive and safe locking system should be provided, as well as security cameras and an appropriate station location. By doing a review on the current transportation plans, it is determined that the proposed bikeshare would contribute to reaching the objectives established in them, i.e. 6 from the 11 macro-objectives of the PUMS (Piano Urbano della Mobilità Sostenibile) and 2 from the 7 quantitative objectives of the PGTU (Piano Generale del Traffico Urbano di Roma Capitale”).

A combination of georeferenced spatial analysis, territorial analysis, and multi-criteria evaluation represented a powerful tool to build a suitability map for a new bikeshare location and decide the best one for the first phase of the system, based on five different criteria. Service area of the proposed bikeshare system is determined to be 44,75 km2, which would benefit 679.277 residents (inside a buffer of 500m) and contain a total of 702.582 possible daily OD trips, from which 21.077 are to be served in a conservative scenario (3%), 35.129 in an intermediate scenario (5%) and 49.181 in an optimistic scenario (7%). Considering the global trends, the impacts on public space and urban landscape, the previous experiences in Europe, as well as the potential barriers to bikeshare in Rome, it is determined that an electrical station-based system is the more suitable for a new bikeshare implementation in Rome.

In order to perform more detailed planning of the system, the guidelines of the “Bikeshare planning guide” of ITDP (2013 and 2018) were followed and the international experience was reviewed. Thus, it could be determined that to reach a high-market penetration the bikeshare system should be composed of; 377 stations, 6.800 bikes, and 11.570 docks. Following the guidelines of the NACTO’s “Bikeshare station sitting guide”, the IDTP’s “Bikeshare Planning Guide” and using territorial analysis it was possible to determine the specific location of the stations. Then, using Thiessen polygons and georeferenced spatial

128 data it was possible to give a size to each one of the bikeshare stations inside the service area.

In order to study the financial feasibility of the proposed bikeshare system, a cost-benefit analysis was done considering six different scenarios and the Net Present Value (NPV) was used as the main indicator. The initial investment is estimated at 21.7 million euros and total yearly operation and management costs at 6.35 million euros for the proposed bikeshare system. Annual incomes are estimated to be 7,21 M€, 12,23 M€ and 17,25 M€ for the conservative, intermediate and optimistic scenario correspondingly. Revenues from tourists are found to have a high potential, with more than 12 million tourists arriving in the city of Rome and more than 29 million nights spent per year. Thus, one day and weekly passes could be included in the membership options. Financial analysis shows that a negative NPV (-25,62 M€) would be obtained for the conservative scenario and positive for the intermediate and optimistic (21,49 M€ and 68,60 M€), indicating that the program would not be profitable just in the first conservative scenario. Financial analysis with additional revenue streams as sponsorships/advertisements result in a positive NPV in each one of the scenarios; 16,61 M€ in the conservative, 63,72 M€ in the intermediate and 110,83 M€ in the optimistic. Thus, these additional revenue streams are highly recommended, not only to obtain positive profitability but to reduce in general the financial risk of the program’s implementation. As an alternative to sponsorships/advertisements, public funding could be used. A value of 2,75 M€ per year of alternative revenue streams is estimated as the profitability break-point under the conservative scenario, on which higher revenues would result in positive profitability and lower ones in negative profitability. The uptake rates’ break-value to obtain a zero NPV is estimated to be 4,103% for residents and 1,5% for tourists (a combination of them).

An additional financial analysis was done taking into account the external costs/benefits of bikeshare, which were calculated based on the “Handbook on the external costs of transport” of the European Commission and the Health Economic Assessment Tool (HEAT) of the World Health Organization (WHO). Total discounted external benefits of Bikeshare (to 2019) during a 12-year period would be equivalent to 116 million euros in the

129 conservative scenario, 191 million euros in the intermediate and approximately 269 million euros in the optimistic scenario, with NPVs positive in each one of the cases, indicating the profitability of the project considering the external costs/benefits of the system. Important public health benefits would be obtained in each one of the scenarios due to a higher level of physical activity in citizens.

The present study proposes a service area that corresponds to the first phase of a bikeshare system in Rome, further phases should be studied. According to the created suitability map, a second phase could include urban zones of; Ostiense, Marconi, Garbatella, Eur, Gianicolense, Pian due Torri, Parioli, Conca d’Oro, Val Melaina, Casal Bertone, and Pietralata. This would permit satisfying a higher number of OD trips in the city of Rome. Further studies should address more deeply the relation of the price-scheme and the demand for the diverse segments of users, to determine which one would fit more to boost the bikeshare demand. Higher levels of cycling could be translated into a higher number of fatalities due to the greater vulnerability of cyclists with respect to other motorized modes. Thus, it is considered strictly necessary to provide road safety measures along with the program, such as; reduced speed lanes, traffic calming measures, safety campaigns, appropriate signaling for cyclists, communication campaigns of the program and supervision from the transit police.

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10. APPENDIX

Net Present Value- Conservative scenario with external benefits

CONSERVATIVE SCENARIO-WITH EXTERNAL BENEFITS CAPITAL O&M REVENUES EXTERNAL PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) BENEF. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 7.211.126 16.970.000 17.833.790 17.147.875 2 -6.347.335 7.211.126 16.970.000 17.833.790 16.488.342 3 -6.347.335 7.211.126 16.970.000 17.833.790 15.854.175 4 -6.347.335 7.211.126 16.970.000 17.833.790 15.244.399 5 -5.100.000 -6.347.335 7.211.126 16.970.000 12.733.790 10.466.247 6 -6.347.335 7.211.126 16.970.000 17.833.790 14.094.304 7 -5.554.522 -6.347.335 7.211.126 16.970.000 12.279.268 9.331.234 8 -6.347.335 7.211.126 16.970.000 17.833.790 13.030.976 9 -5.100.000 -6.347.335 7.211.126 16.970.000 12.733.790 8.946.592 10 -6.347.335 7.211.126 16.970.000 17.833.790 12.047.870 11 -6.347.335 7.211.126 16.970.000 17.833.790 11.584.490 12 -6.347.335 7.211.126 16.970.000 17.833.790 11.138.933 NPV 133.642.390

Net Present Value- Intermediate scenario with external benefits

INTERMEDIATE SCENARIO-WITH EXTERNAL BENEFITS CAPITAL O&M REVENUES EXTERNAL PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) BENEF. (€) VALUE (€) 0 -21.733.046 -21.733.046 -21.733.046 1 -6.347.335 12.230.757 28.130.000 34.013.422 32.705.213 2 -6.347.335 12.230.757 28.130.000 34.013.422 31.447.320 3 -6.347.335 12.230.757 28.130.000 34.013.422 30.237.808 4 -6.347.335 12.230.757 28.130.000 34.013.422 29.074.815 5 -5.100.000 -6.347.335 12.230.757 28.130.000 28.913.422 23.764.725 6 -6.347.335 12.230.757 28.130.000 34.013.422 26.881.301 7 -5.554.522 -6.347.335 12.230.757 28.130.000 28.458.899 21.626.424 8 -6.347.335 12.230.757 28.130.000 34.013.422 24.853.274 9 -5.100.000 -6.347.335 12.230.757 28.130.000 28.913.422 20.314.187 10 -6.347.335 12.230.757 28.130.000 34.013.422 22.978.249 11 -6.347.335 12.230.757 28.130.000 34.013.422 22.094.470 12 -6.347.335 12.230.757 28.130.000 34.013.422 21.244.683 NPV 285.489.423

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Net Present Value- Optimistic scenario with external benefits

OPTIMISTIC SCENARIO-WITH EXTERNAL BENEFITS CAPITAL O&M REVENUES EXTERNAL PRESENT YEAR TOTAL (€) PURCHASE (€) COSTS (€) (€) BENEF. (€) VALUE (€) 0 -21.733.046 0 -21.733.046 -21.733.046 1 -6.347.335 17.250.388 39.520.000 50.423.053 48.483.705 2 -6.347.335 17.250.388 39.520.000 50.423.053 46.618.947 3 -6.347.335 17.250.388 39.520.000 50.423.053 44.825.910 4 -6.347.335 17.250.388 39.520.000 50.423.053 43.101.837 5 -5.100.000 -6.347.335 17.250.388 39.520.000 45.323.053 37.252.246 6 -6.347.335 17.250.388 39.520.000 50.423.053 39.850.071 7 -5.554.522 -6.347.335 17.250.388 39.520.000 44.868.531 34.096.396 8 -6.347.335 17.250.388 39.520.000 50.423.053 36.843.631 9 -5.100.000 -6.347.335 17.250.388 39.520.000 45.323.053 31.843.376 10 -6.347.335 17.250.388 39.520.000 50.423.053 34.064.008 11 -6.347.335 17.250.388 39.520.000 50.423.053 32.753.854 12 -6.347.335 17.250.388 39.520.000 50.423.053 31.494.090 NPV 439.495.023

Docks by station

Code Type Associated Population Associated Employment Docks 1 Metro 2034,07 1455,75 30 2 Metro 1140,70 1060,72 30 3 Metro 2983,17 1680,44 30 4 Metro 4759,37 896,82 40 5 Metro 3760,16 963,01 40 6 Metro 3848,78 1101,90 40 7 Metro 2800,22 723,68 30 8 Metro 755,61 328,29 20 9 Metro 4032,06 1259,72 40 10 Metro 3840,01 975,25 40 11 Metro 2416,69 1393,75 30 12 Metro 1682,56 1514,37 30 13 Metro 1409,64 1552,04 30 14 Metro 2104,59 3196,96 30 15 Metro 187,87 3590,82 20 16 Metro 76,03 1240,80 40 17 Metro 360,04 4167,53 40 18 Metro 638,19 4695,10 40 19 Metro 891,78 3118,36 30 20 Metro 778,14 4245,29 40 21 Metro 611,12 1617,78 30 22 Metro 2336,27 1973,16 30 23 Metro 2904,95 993,22 30 24 Metro 1992,37 438,16 30 25 Metro 3198,77 630,44 30 138

Code Type Associated Population Associated Employment Docks 26 Metro 2647,11 423,30 30 27 Metro 3561,79 626,14 30 28 Metro 3304,08 992,02 30 29 Metro 2016,85 525,46 30 30 Metro 5459,79 6108,74 40 31 Metro 325,52 782,84 40 32 Metro 828,07 949,83 30 33 Metro 315,19 6392,39 30 34 Metro 564,75 2886,73 30 35 Metro 3262,70 1798,87 30 36 Metro 1791,48 661,25 40 37 Metro 1518,78 815,24 30 38 Metro 2752,67 1450,75 30 39 Tram 3881,99 1313,54 40 40 Tram 2684,74 436,68 30 41 Tram 1991,81 543,92 30 42 Tram 2205,47 414,99 30 43 Tram 248,87 188,49 20 44 Tram 2872,80 333,45 20 45 Tram 2417,95 371,51 30 46 Tram 678,03 562,16 30 47 Tram 2235,19 591,37 30 48 Tram 2644,05 515,98 30 49 Tram 1882,12 401,43 30 50 Tram 1919,98 441,45 30 51 Tram 2070,15 517,78 30 52 Tram 968,17 417,03 30 53 Tram 2041,77 551,59 30 54 Tram 2009,53 595,07 30 55 Tram 1658,22 1230,72 30 56 Tram 1699,93 483,81 30 57 Tram 2016,21 2277,29 30 58 Tram 1392,34 2187,91 30 59 Tram 663,89 1456,84 40 60 Tram 1199,05 499,86 30 61 Tram 1055,45 224,39 30 62 Tram 955,22 631,79 30 63 Tram 37,47 792,45 30 64 Tram 25,84 1277,25 30 65 Tram 43,33 4141,84 30 66 Tram 218,11 2064,92 30 67 Tram 742,63 1904,68 30 68 Tram 1254,28 1166,55 30 69 Tram 1822,16 3397,68 30 70 Tram 1940,81 3491,58 30 71 Tram 1468,42 1674,94 30 72 Tram 508,99 3604,48 30 73 Tram 454,77 1958,61 30 74 Tram 1175,98 2411,71 30 75 Tram 1221,21 2642,89 30 76 Tram 2456,86 2927,41 30 77 Tram 1365,88 1357,56 30 78 Tram 1051,85 1233,78 30 79 Tram 394,83 1146,69 30 80 Tram 24,19 290,29 20 81 Tram 458,33 531,86 30 82 Tram 2130,39 2151,80 30

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Code Type Associated Population Associated Employment Docks 83 Tram 1252,39 1275,15 30 84 Tram 1851,71 732,21 30 85 Tram 493,24 605,12 30 86 Tram 5505,62 989,00 40 87 Tram 1870,05 2197,59 30 88 Tram 2099,14 2582,40 30 89 Tram 7477,47 3298,40 40 90 Tram 1424,44 3284,79 30 91 Tram 520,99 1841,02 30 92 Tram 1205,03 3963,18 40 93 Tram 5196,87 1989,61 40 94 Tram 2783,81 3749,39 40 95 Tram 1430,10 800,02 30 96 Tram 2438,12 1230,05 30 97 Tram 1516,54 5159,53 40 98 Other 958,63 1009,65 30 99 Other 1901,85 612,26 30 100 Other 3279,04 1304,33 30 101 Other 3324,05 668,00 30 102 Other 4354,73 747,55 40 103 Other 4344,44 183,67 30 104 Other 5193,95 2601,61 40 105 Other 948,29 575,10 30 106 Other 5407,34 689,99 40 107 Other 879,03 160,33 20 108 Other 2622,17 656,26 30 109 Other 6572,76 765,82 40 110 Other 3617,83 569,15 40 111 Other 2836,96 580,17 30 112 Other 4470,33 684,12 40 113 Other 4004,66 589,92 40 114 Other 3356,59 490,33 30 115 Other 2767,96 362,65 30 116 Other 1421,91 244,81 20 117 Other 1117,51 291,41 20 118 Other 2336,10 254,84 20 119 Other 2927,66 1064,49 30 120 Other 225,78 471,49 20 121 Other 344,55 349,03 30 122 Other 2495,45 707,39 30 123 Other 2982,51 421,01 30 124 Other 3153,28 405,85 30 125 Other 3650,93 687,14 40 126 Other 3617,07 357,44 40 127 Other 2437,71 307,53 20 128 Other 1202,44 355,46 30 129 Other 330,37 64,78 20 130 Other 2370,19 490,58 30 131 Other 1387,56 68,60 20 132 Other 1897,81 106,98 20 133 Other 1806,80 582,62 30 134 Other 1335,00 248,07 20 135 Other 1490,74 394,82 30 136 Other 492,39 231,87 20 137 Other 758,69 135,55 20 138 Other 660,72 114,30 20 139 Other 2586,76 648,52 30

140

Code Type Associated Population Associated Employment Docks 140 Other 664,25 263,69 20 141 Other 4051,55 364,41 40 142 Other 5158,13 956,96 40 143 Other 1884,07 353,22 30 144 Other 2908,47 755,45 30 145 Other 2831,92 262,94 20 146 Other 3631,83 1069,90 40 147 Other 2492,70 470,76 30 148 Other 5897,10 1205,44 40 149 Other 2048,16 567,49 30 150 Other 2380,35 508,56 30 151 Other 1365,65 288,74 20 152 Other 2494,45 837,13 30 153 Other 1805,88 378,49 30 154 Other 429,34 426,20 30 155 Other 474,42 205,23 20 156 Other 604,91 616,34 30 157 Other 3727,11 592,46 40 158 Other 2796,80 610,84 30 159 Other 3158,43 419,37 30 160 Other 1780,10 343,06 30 161 Other 2715,71 1027,53 30 162 Other 1315,44 247,20 20 163 Other 3311,98 427,61 30 164 Other 1480,93 294,72 20 165 Other 643,71 172,67 20 166 Other 2617,42 239,09 20 167 Other 1060,34 439,11 30 168 Other 646,93 398,89 30 169 Other 919,79 225,25 20 170 Other 854,30 378,20 30 171 Other 1303,15 667,02 30 172 Other 1697,42 394,37 30 173 Other 2349,23 544,13 30 174 Other 3356,91 1109,28 30 175 Other 1910,14 661,51 30 176 Other 1147,58 452,61 30 177 Other 1532,46 228,23 20 178 Other 2740,81 407,66 30 179 Other 1340,94 824,66 30 180 Other 1044,18 369,56 30 181 Other 2935,72 437,67 30 182 Other 382,79 584,34 30 183 Other 1325,39 524,54 30 184 Other 1959,80 726,92 30 185 Other 1747,11 786,08 30 186 Other 1593,91 1579,32 30 187 Other 2358,26 673,89 30 188 Other 2816,47 614,69 30 189 Other 5129,71 2148,43 40 190 Other 956,83 896,42 30 191 Other 2781,67 580,67 30 192 Other 1666,67 900,74 30 193 Other 2186,70 943,25 30 194 Other 3911,94 999,76 40 195 Other 3119,47 1493,98 30 196 Other 3907,70 973,63 40

141

Code Type Associated Population Associated Employment Docks 197 Other 2351,44 638,29 30 198 Other 2146,01 823,98 30 199 Other 1910,55 1754,75 30 200 Other 2257,57 749,02 30 201 Other 2049,99 781,97 30 202 Other 1748,61 573,13 30 203 Other 1336,46 304,34 20 204 Other 2675,07 1067,88 30 205 Other 2254,08 417,72 30 206 Other 1812,65 788,14 30 207 Other 1660,13 530,76 30 208 Other 3255,65 984,31 30 209 Other 1566,29 511,47 30 210 Other 5825,63 1154,66 40 211 Other 3674,94 1298,40 40 212 Other 2135,80 486,36 30 213 Other 2172,40 305,48 20 214 Other 2398,41 701,38 30 215 Other 1895,80 1002,88 30 216 Other 2369,55 477,08 30 217 Other 1477,61 744,30 30 218 Other 609,01 803,25 30 219 Other 867,79 352,23 30 220 Other 539,50 1073,95 30 221 Other 2099,84 1209,56 30 222 Other 1111,40 1108,75 30 223 Other 814,55 517,99 30 224 Other 1853,16 456,81 30 225 Other 1207,47 1950,39 30 226 Other 2861,74 1224,29 30 227 Other 3609,93 4350,16 40 228 Other 1021,10 518,94 30 229 Other 1908,19 1000,60 30 230 Other 1011,11 1677,30 30 231 Other 869,14 695,48 30 232 Other 1358,64 678,74 30 233 Other 3647,58 6897,42 40 234 Other 554,17 4354,36 40 235 Other 748,43 5377,06 40 236 Other 865,66 2589,72 30 237 Other 389,08 3514,94 30 238 Other 403,07 3319,18 30 239 Other 1407,74 4025,45 40 240 Other 1086,98 3134,42 30 241 Other 958,30 663,72 30 242 Other 159,85 10851,92 30 243 Other 1303,46 2734,53 30 244 Other 937,06 2407,76 30 245 Other 740,09 3141,98 30 246 Other 801,34 2666,46 30 247 Other 1243,51 1617,32 30 248 Other 1258,40 2542,81 30 249 Other 2084,43 805,69 30 250 Other 2513,43 1449,14 30 251 Other 2585,25 733,88 30 252 Other 2853,95 2066,43 30 253 Other 3739,90 1169,60 40

142

Code Type Associated Population Associated Employment Docks 254 Other 2391,10 1093,44 30 255 Other 258,48 1471,68 40 256 Other 59,11 1056,60 30 257 Other 33,86 1744,15 30 258 Other 7,59 1867,51 40 259 Other 117,87 553,12 20 260 Other 826,99 1683,61 30 261 Other 719,17 7842,44 40 262 Other 797,79 3368,03 30 263 Other 36,92 2040,88 20 264 Other 874,24 1565,43 30 265 Other 329,08 1435,16 20 266 Other 691,26 8029,78 40 267 Other 478,16 4426,81 40 268 Other 247,43 5914,90 30 269 Other 327,11 2756,49 20 270 Other 773,66 3720,72 40 271 Other 625,89 8988,84 40 272 Other 901,33 3959,24 40 273 Other 256,00 5347,56 30 274 Other 472,55 3779,32 40 275 Other 603,69 4746,30 40 276 Other 231,11 3196,29 20 277 Other 840,02 4105,11 40 278 Other 513,10 3021,05 30 279 Other 1099,14 2243,24 30 280 Other 1535,16 2311,29 30 281 Other 1078,08 3579,75 30 282 Other 580,18 2109,50 30 283 Other 974,68 2088,42 30 284 Other 483,14 812,75 30 285 Other 708,77 2223,17 30 286 Other 39,03 469,88 30 287 Other 1272,55 844,95 30 288 Other 1280,05 2296,60 30 289 Other 2016,22 2953,70 30 290 Other 596,92 3620,12 30 291 Other 2496,82 5395,07 40 292 Other 6707,08 4172,18 40 293 Other 1455,67 3714,68 40 294 Other 3525,93 8219,23 40 295 Other 4426,85 3710,06 40 296 Other 848,19 9526,68 40 297 Other 1179,59 2155,69 30 298 Other 2415,82 4942,52 40 299 Other 1512,74 3456,53 30 300 Other 2033,33 2322,92 30 301 Other 3474,89 2849,76 30 302 Other 3183,53 2602,04 30 303 Other 383,00 2110,94 30 304 Other 60,67 351,98 30 305 Other 883,73 1157,26 30 306 Other 564,98 2301,95 30 307 Other 805,54 2013,68 30 308 Other 878,14 2322,75 30 309 Other 549,86 1368,30 30 310 Other 1562,12 3459,92 30

143

Code Type Associated Population Associated Employment Docks 311 Other 1539,83 5751,46 40 312 Other 562,61 1768,19 30 313 Other 609,25 1297,22 30 314 Other 2515,29 1266,05 30 315 Other 1165,14 21567,83 40 316 Other 703,59 719,86 30 317 Other 1188,30 1332,58 30 318 Other 1524,28 1709,75 30 319 Other 1001,05 2436,01 30 320 Other 433,11 12356,99 40 321 Other 54,53 7499,68 30 322 Other 463,64 709,29 30 323 Other 3425,30 2170,54 30 324 Other 604,15 1493,70 30 325 Other 75,65 1302,79 20 326 Other 287,77 1356,65 20 327 Other 1360,32 2146,52 30 328 Other 1399,54 1775,10 30 329 Other 116,41 210,59 20 330 Other 150,53 272,26 30 331 Other 976,28 2218,95 30 332 Other 1849,46 1719,60 30 333 Other 3524,29 2398,51 30 334 Other 2277,96 1244,82 30 335 Other 1246,93 2546,87 30 336 Other 1383,45 1426,76 30 337 Other 1244,63 967,37 30 338 Other 2755,86 1395,93 30 339 Other 2786,88 919,64 30 340 Other 1214,27 357,87 30 341 Other 2189,06 1431,40 30 342 Other 1358,35 424,97 30 343 Other 3304,69 783,53 30 344 Other 1733,16 570,80 30 345 Other 1167,66 984,56 30 346 Other 4113,65 1167,53 40 347 Other 3075,03 655,17 30 348 Other 3729,21 1114,40 40 349 Other 2861,66 783,91 30 350 Other 2521,39 1992,98 30 351 Other 3036,35 788,63 30 352 Other 3082,98 737,83 30 353 Other 3577,31 739,15 30 354 Other 3215,57 1627,04 30 355 Other 3247,66 633,34 30 356 Other 4190,73 1304,49 40 357 Other 112,81 255,82 20 358 Other 343,43 2092,06 30 359 Other 1932,93 688,15 30 360 Other 2897,63 1184,18 30 361 Other 2296,85 2748,20 30 362 Other 1225,18 905,48 30 363 Other 1173,17 2838,96 30 364 Other 119,54 200,61 20 365 Other 111,35 388,96 20 366 Other 1028,58 554,79 30 367 Other 33,85 91,91 30

144

Code Type Associated Population Associated Employment Docks 368 Other 61,42 978,39 20 369 Other 639,32 760,04 30 370 Other 261,93 152,47 20 371 Other 71,98 86,40 20 372 Other 1091,47 2581,85 30 373 Other 3383,03 835,44 30 374 Other 1580,58 576,54 30 375 Other 568,38 1943,81 30 376 Other 426,75 5192,81 40 377 Other 1672,23 1753,04 30

145

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148

149

150

151

152

153

154

155

156

157

158