TOWARDS THE 15 MINUTES WALKABLE CITY

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WALKABLE CITY: REINVENTING MOBILITY AND URBANISM

SEPTIEMBRE 2022

Juan Múgica González

DIRECTOR DEL TRABAJO FIN DE MASTER:

Pablo Garrido Martínez-Llop

J u a n M ú g ic a Go n z á lez

TRABAJO FIN DE MASTER PARA LA OBTENCIÓN DEL

TÍTULO DE MASTER EN

INGENIERÍA INDUSTRIAL

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This order is all composed of movement and change, and although it is life, not art, we may fancifully call it the art form of the city and liken it to the dance. Not to a simple-minded precision dance with everyone kicking up at the same time, twirling in unison and bowing off en masse, but to an intricate ballet in which the individual dances and ensembles all have distinctive parts which miraculously reinforce each other and compose an olderly whole.”

- Jane Jacobs, The Death and Life of Great American Cities

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First and foremost, I would like to express my gratitude to Kent Larson, for giving me the opportunity to work with the City Science Group and to work in the MIT Media Lab. You have been an unlimited source of inspiration and learning, helping me to identify which are the unique, scalable and magic innovative projects, and how to reach my highest performing version.

Thank you Luis Alonso, for being a mentor from the very first minute, guiding me in both pro- fessional and personal levels to give my best. You quickly identified my strengths and weaknesses and you have helped me grow.

Thank you Pablo Garrido, for trusting my proposals throughout the project, supporting me and contributing with your expertise. It would have been much more difficult without your support.

Thank you Naroa Coretti, for teaching me the City Science Group’s way of working. You dedicated to me so much time until I was able to start contributing, even from the other side of the ocean. I could not have made this without your patience and time.

Thank you to all the City Science Group members, from which I have learned beautiful things.

You have taught me the connections among Media, Art and Science, enriching my skills and preparing me for the future.

And finally, and most importantly, thank you ait´a, mam´a and Mar´ıa, for believing in me and my decisions, for all the love you transmit every day. Because I would never be who I am without you, and because I keep you with me no matter the kilometers away we can be. I love you.

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EXECUTIVE SUMMARY

The world is facing unprecedented challenges. A recent pandemic, shrinking natural resources, environmental damage or social inequalities are just some of the duels that humans aim and need to solve in the short, mid and long term.

In an era when more than 50% of the world’s population lives in cities and more than 80% of the global GDP is generated in urban centers, urban areas are becoming even more essential in society [1]. These urban areas are evolving very fast to cope with populations’ exponential growth and rapid changing climate conditions [2]. By 2050, it is expected that 7 out of 10 people in the world live in cities [3]. Cities are growing in size and complexity, with an increasing rhythm of change in order to address the citizens’ needs [4].

The City Science (CS) Group at the MIT Media Lab focuses on understanding urban performance and live-work balance of communities. In parallel, the CS Group develops interventions that foster more livable urban ecosystems. The CS Group envisions cities composed of resilient communities, designed to address today’s great challenges (e.g., public health, global warming, equity, sustainability [5]). There is a potential to overcome these challenges by designing well- balanced communities, which the CS Group believes are based on three main aspects [5]: (1) live-work symmetry, with the availability of housing matching the local jobs in a community, vastly reducing transportation needed, (2) local access to assets, so people in the community find everything they need in a walkable distance and transport can be covered by ultra-lightweight vehicles, and (3) local production of resources, with communities being able to generate renewable energy, process waste, purify water and produce fresh food.

These concepts come together in the idea of the 15 minutes walkable city, already publicly proposed by Kent Larson (director of the City Science Group) in 2012 [6]. In order to meet those three conditions, innovative technologies, designs, and public policies are to be implemented in communities [5]. Those implementations are domain of study for the CS Group, which directs its research in three main lines: urban planning, mobility and architecture.

Disruptive paradigms are used at the MIT Media Lab to explore innovative ideas, so unpredict- ability is a constant factor. The MIT follows a methodology based on learning by doing, with ideas being rapidly iterated to learn about their viability and performance. Thus, this master’s thesis adapts and reflects this methodology.

In the figure 0.1 the structure of the project is outlined. Starting with theCS Group’s mission (1), this master’s thesis focuses on building the path towards the 15 minutes high-performing walkable city. Contributions are made in two of the main lines of research.

Mobility (2): there is a need to reinvent the classical definition of mobility. This definition must meet the needs to move people, goods and perform utility services in those novel high-performing urban scenarios. The main drivers reshaping the future of urban mobility are predicted to come from electrification, vehicle sharing, and autonomy. [7]. Although these trends are expected to alleviate many of the urban mobility-related problems [8–11], studies suggest that this might not be enough in order to meet those climate change mitigation targets related to mobility [12, 13].

Thus a drastic reduction in vehicle miles traveled will be needed [12, 13].

An scenario with vehicle autonomy will give vehicles the ability to communicate between each other. Thanks to this ability, mobility will work as a collaborative system. In this sense, vehicle platooning can be seen as the first step towards joint mobility, since platooning fosters the coordinated movement of autonomous vehicles to improve safety and reduce congestion

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Figure 0.1: Diagram representing the different contributions of this master’s thesis in the CS Group’s research context. Outlined in blue, the mission of the CS Group. In green, two of the main lines of research of the CS Group. These lines organize the CS Group’s research towards its mission. In purple, the contributions of this master’s thesis towards the aforementioned mission.

Finally, in orange, those contributions for which an article is already published.

and fuel consumption [14]. However, in the future, interaction among vehicles can be much richer than communicating to coordinate. In nature there are many inspiring examples of collaboration; for instance, in insect colonies local and simple interactions lead to complex system-level behaviors [15]. The behavior of these natural swarms has been transferred to artificial systems by researchers working in the field of swarm robotics, proving to make systems more flexible, scalable, and robust [16].

Therefore, inspired by nature and supported by swarm robotics, afuture mobility framework (3) is proposed. In this future mobility, shared, electric, and autonomous vehicles would be multi-functional and behave as a collaborative system in high-performing walkable districts.

Fleets of multi-functional vehicles would complete different tasks collaboratively and giving a response to the different mobility needs of the city. The local and direct interactions between autonomous vehicles in this proposal has been named vehicle clustering. When vehiclescluster, they connect either physically or virtually, being able to share data, provide services, share computational power, transfer energy [17] or even potentially transfer cargo. An article has been published detailing this framework [18].

To achieve this urban mobility framework, there is a need of new ultra-lightweight vehicles, new infrastructure and new operation:

• New ultra-lightweight vehicles -Lane-following algorithm for the MIT Autonom- ous Bicycle (4): the CS Group has been working these last years in two innovative vehicles, the CityCar [19] and the Persuasive Electric Vehicle (PEV) [20]. The last contribution, called the MIT Autonomous Bicycle, offers an efficient, ecological, inexpensive,

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and reliable mobility choice to transport people, contributing to a lower pollution and congestion in their daily routines [11]. The contribution with this master’s thesis towards this project was an algorithm so the bicycle could navigate autonomously in a bike lane. A proof-of-concept algorithm was developed, showcasing how a lightweight mobility vehicle such as the MIT Autonomous bicycle could circulate inside a lane.

• New infrastructure -V2V Dynamic Charging (5): the second contribution towards this mobility framework constitutes a solution to allow vehicles share energy with other vehicles while moving. Energy is transmitted through a connection combining inductive charging with an electromagnet. Although EVs have numerous benefits over ICEvs, charging issues such as limited driving range or lack of convenient charging infrastructure pose significant obstacles preventing EVs from populating the roads [21]. This lightweight infrastructure solution will minimize costs and infrastructure availability problems. With the proposed solution, instead of following their own rulesets, vehicles would do what is more efficient for the system as a whole, and vehicles will access energy anywhere and anytime. Vehicle downtime and the miles traveled related to charging would also be reduced, increasing vehicle usage efficiency. The proposed connection is easy to connect-disconnect, tunable, durable, bidirectional, safe, and efficient. A proof of concept prototype has been developed, which validates that the proposed connection allows for easy attaching/de- taching and wirelessly energy sharing while moving. An article has been published detailing this solution [17].

• New operation - Simulation to validate new mobility scenarios (6): the third contribution focuses on understanding how fleets of collaborative vehicles will operate.

The beauty of natural swarms resides in how characteristics such as self-organization, decentralization or local interactions lead to scalable, robust and flexible systems, with greater skills than the sum of the different agents’ skills. Those characteristics are then being replicated in a mobility system to study its viability and measure its performance.

As a first iteration, an agent-based simulation has been built. This simulation studies how, when re-balancing vehicles in a shared mobility, a pheromone-based system can outperform current solutions.

Urban Planning (7): there is an urgent need of reinventing how urban planners intervene in cities, moving from the classic urban sprawl, car scale, to a high-dense, human scale, urbanism. This need requires to understand how cities “work”, in addition to the well-being of their inhabitants and the possible impact of various urban interventions [1].

Urban transformation usually imply complex conversations among stakeholders, which com- monly involve administrative bodies, technical professionals, and community members. Finding consensus is then a difficult task, due to facts as different levels of expertise or different interests among stakeholders [22]. Moreover, community people are usually included in the last stage of the process [4].

The City Science Group has focused these last years on developing tools around the City Scope platform [1]. This platform gathers those tools in order to rapid design and evaluate urban interventions. The City Scope focus on human-machine interaction, with intuitive and data- driven tools that facilitate consensus in urban planning processes. Besides, those tools are built to be community-extended.

It is a historical challenge for stakeholders to promote solutions in the complexity of urban scenarios, addressing multiple objectives (e.g. socio, environmental, economic). The City Matrix [4], one of the City Scope’s modules, performs human-intelligence augmentation using AI, helping users to optimize their urban planning developments.

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Human-intelligence augmentation tools may fall in the optimization task and users will blindly follow their suggestions, not understanding the reason of the output. This can diminish the consensus process. There is a need for a transparent and inspirational tool to help stakeholders reach consensus without guiding them towards a unique and optimized solution.

This master’s thesis proposes a novel City Scope module, thegenerative 2D tool (8). This tool assists urban planners in the multi-objective optimization process of urban planning. By using evolutionary algorithms, a transparent guidance (human-intelligence augmentation) is provided to users. The tool is designed so it is the urban planners who guide the design optimization, helping to preserve the consensus process. A strong focus is made on human-machine interaction.

The first iteration of the tool has been tested with both experienced and non-experienced users.

The tools shows the potential for inspiring users towards a performing solution that complies with their objectives.

In the CS Group urban planning research is being re-directed to hyper-performing and resilient communities can grow in a vertical city. Vertical cities are those urban scenarios where the pulse of the city, its vibrancy (usually located in the ground level), is replicated in various heights. Vertical urban planning is considered the solution to allocate the population growth while fostering sustainability and communities’ well-being [23]. Current solutions, composed by unconnected skyscrappers, are far from meeting those objectives [2].

Thus, the CS Group is working on the Vertical City Scope (10), a platform to design and evaluate vertical urban scenarios. This master’s thesis focuses on two main contributions:

• Generative 3D tool (9): second version of the generative 2D tool, more robust and prepared to deal with the creation of 3D scenarios

• Network structure (11): module to parse urban planning CAD files, transforming them into a nodes & links network, so urban analysis can be based on data.

Keywords: urban mobility, future cities, autonomous vehicles, swarm systems, electric vehicles, electromagnets, inductive power transmission, mobile robots, power transmission control, agent-based simulation, urban planning, genetic algorithm, multi-objective optimization, vertical city

UNESCO Codes: 1203 Computer Sciences, 1206 Numerical analysis, 1208 Prob- ability, 1209 Statistics, 2202 Electro-magnetism, 2203 Electronics, 3304 Computer technology, 3306 Electrical technology and engineering, 3307 Electronic technology, 3312 Materials technology, 3317 Motor vehicle technology, 3322 Power technology, 3327 Transportations systems technology, 3329 Urban Planning, 6201 Architecture.

Plantilla en LaTeX acorde con la Normativa para la elaboraci´on de in- formes de TFT de la ETSII (UPM)” by Javier Soto P´erez-Olivares is licensed under a Creative Commons Attribution 4.0 International License.

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TABLE INDEX

4.1 Variations of the configuration parameters . . . 73

4.2 Results with optimal evaporation and exploitation rates, including: Average wait time in the Pheromone-based scenario in minutes, percentage of change comparing the Pheromone-based to the Nominal and Random movement scenarios, and distance traveled with rebalancing divided by the distance traveled without rebalancing. . . 74

7.1 Hardware cost for the realization of this master’s thesis . . . 108

7.2 Software cost for the realization of this master’s thesis . . . 108

7.3 Labor costs for the realization of this master’s thesis . . . 109

7.4 Indirect costs for the realization of this master’s thesis . . . 109

7.5 Global budget for the realization of this master’s thesis . . . 110

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FIGURE INDEX

0.1 Diagram representing the different contributions of this master’s thesis in the CS Group’s research context. Outlined in blue, the mission of the CS Group. In green, two of the main lines of research of the CS Group. These lines organize the CS Group’s research towards its mission. In purple, the contributions of this master’s thesis towards the aforementioned mission. Finally, in orange, those contributions for which an article is already published. . . vi 1.1 City Science group logo . . . 2 1.2 Sustainable Development Goals [24] . . . 4 1.3 Diagram representing the different contributions of this master’s thesis in the CS

Group’s research context. Outlined in blue, the mission of the CS Group. In green, two of the main lines of research of the CS Group. These lines organize the CS Group’s research towards its mission. In purple, the contributions of this master’s thesis towards the aforementioned mission. Finally, in orange, those contributions for which an article is already published. . . 6 1.4 Swarmanoids, example of swarm robotics. . . 8 1.5 The MIT Autonomous Bicycle . . . 9 1.6 City of the Future by Harvey Corbett (1913), proposing horizontal mobility at

multiple levels within the city [25] . . . 13 3.1 Example of mutualism in nature between the Nile Crocodile and the Egyptian

plover bird. [26] . . . 16 3.2 Lane detection project for the Udacity Infosys Connect Self driving Car Nanode-

gree. [27] . . . 20 3.3 Lane detection project to construct a reduced size driverless car [28] . . . 21 3.4 Mobile charger solution “Mobi EV Charger” by Freewire. [29] . . . 22 3.5 Scheme of a catenary power system and the drivetrain of an overhead catenary

truck. [30] . . . 23 3.6 “Urp” project (1996-2001), a TUI systems showing urban interventions’ effect on

shadows and wind . . . 27 3.7 City Science Group work on quantifying in indexes urban performance and cor-

relating it with urban form . . . 28 3.8 CityScope Architecture, with CityIO as the central server [31] . . . 30 3.9 A conceptual framework to integrate design optimization with machine learning

(the groups in grey color are expected to run in the background) [32] . . . 34 3.10 Theorem of 1909 [33] . . . 36 3.11 Highrise of Homes by James Wines in 1981 [34] . . . 37

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3.12 MVRDV, Rustic Farms, Waddinxveen, 1997 [35] . . . 37

3.13 Left: Kyonori Kikutake [36], Tree-shaped Community. Right: Future Systems, Coexistence Tower [37] . . . 38

3.14 Left: Paolo Soleri,Mesa City [38]. Right: MVRDV,The Lifted Village [39] . . . 38

3.15 Cosmo Park in Jakarta [40] . . . 39

3.16 An example of vertically integrated multi-functional and mixed land uses in Hong Kong [41] . . . 40

3.17 Pedestrian Skyways in Hong Kong, product of urban growth . . . 40

3.18 Crossover procedure for land use MOEA proposed by Cao et al. [42] . . . 44

3.19 Classical mutation procedure for land use MOEA. Image from Cao et al. [42] . . 44

4.1 Persuasive Electric Vehicle (PEV), example of a multifunctional vehicle, able to cover mobility of people and mobility of goods [43] . . . 46

4.2 Trip length coverage of different mobility modes including autonomous personal mobility devices (PMDs) and autonomous pods, illustrating how clustering could extend the trip lengths covered. Adapted from [44] . . . 49

4.3 Initial set-up to prototype the lane detection algorithm. Lane . . . 52

4.4 Initial set-up for prototyping. Camera positioning . . . 52

4.5 Image captured by the camera entering to the lane detection pipeline . . . 53

4.6 RGB −>HSV transformation . . . 53

4.7 Image once color mask is applied . . . 53

4.8 Edges resulting from applying Canny Edge Detection function . . . 54

4.9 Edges after isolating the region of interest . . . 54

4.10 Detected segments colored over the initial image . . . 55

4.11 Final two lines detected colored over the initial image . . . 55

4.12 Computed Heading Line together with the Detected Lane Lines . . . 56

4.13 MIT Autonomous Bicycle placed in the circuit prepared to test the first iteration of the lane following algorithm . . . 59

4.14 High-level diagram for the proposed connection which combines IPT with an electromagnet . . . 60

4.15 High-level diagram for the proposed prototype . . . 62

4.16 Images of the developed prototype. On the left, the vehicle sharing power (Vehicle A). On the right, the vehicle receiving power (Vehicle B). The upper image shows the vehicles detached and the lower image shows the vehicles attached. . . 63

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4.17 Circuits design for Vehicle A . . . 63

4.18 Circuits design for Vehicle B. The real circuits constitute a simplification of this design, as it was no power and it will be composed by the left side of the circuits or passive part . . . 64

4.19 Transformer scheme [45] . . . 65

4.20 B-H DC curve of a ferrite core [46] . . . 66

4.21 Diagram depicting the users’ behavior as a finite-state machine . . . 70

4.22 Diagram depicting the vehicles’ behavior as a finite-state machine . . . 71

4.23 Visualization of the agent-based simulation in GAMA GUI . . . 73

4.24 Results for the different configuration parameters: a) Average wait time in the ’Pheromone-based Rebalancing’ scenario in minutes, b) Percentage reduction in average wait time of the ’Pheromone-based Rebalancing’ scenario over the ’Nom- inal’ scenario, c) Percentage reduction in average wait time of the ’Pheromone- based Rebalancing’ scenario over the ’Random Rebalancing’ scenario. . . 75

4.25 Diagram of the elements composing the 2D Genetic Tool and their interactions . 77 4.26 Global view of the tool with its different elements tagged . . . 78

4.27 Close view of the different pieces composing the interactive table of the 2D genetic city tool . . . 79

4.28 Genetic algorithm’s crossover representation . . . 84

4.29 Genetic algorithm’s mutation representation . . . 85

4.30 Example of both sides for the Lego pieces. The back side contains a sticked Aruco ID. . . 85

4.31 View inside the interactive table for the 2D Genetic City . . . 86

4.32 Context of Volpe. [47] . . . 87

4.33 Radar plots before and after running the genetic algorithm . . . 90

4.34 Diagram of the different steps composing the vertical city tool. The colored blocks constitute the contributions of this master’s thesis to this work . . . 92

4.35 Visualization of an elevator (red square) together with an alley constituting the entrance (entering purple line) . . . 93

4.36 Visualization of the initial CAD model, composed by lines representing different entities . . . 95

4.37 Network composed of nodes and links corresponding to the parsed CAD . . . 95

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4.38 Boundaries of the Volpe model extracted from CAD. In orange, those boundaries defined by points in the CAD model. In blue, the (minx,miny), (maxx,miny), (maxx,maxy), (minx,maxy) points. . . 97 4.39 Voxelized model of Volpe. Each of the voxels is represented as an orange dot. . . 97 4.40 Voxelized model from CAD file. Each of the voxels represents a land use. Different

colors are assigned to different land uses. . . 98

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1 GENERAL INTRODUCTION

1.1 Project Context

1.1.1 The MIT Media Lab (ML)

The MIT Media Lab is a research laboratory at the Massachusetts Institute of Technology, growing out of MIT’s Architecture Machine Group in the School of Architecture. Founded in 1985, the ML is a research and academic organisation. It is is a unique innovation environment where people are constantly pushed out of their comfort zones.

The ML is an interdisciplinary environment, where designers, engineers, artists, and scientists are gathered together and asked to work on projects not directly related to their background.

Examples are architects coding as if they were computer scientists or computer scientists working in designing urban planing as if they were architects. This culture enables an astonishing level of innovation, materialized in the creation of technologies and experiences that improve peoples’

lives, communities, and environments. Art, science and technology are the ingredients building every project coming from the ML, solving challenges in novel and unique approaches.

The ML is mainly supported by leading organizations (private companies, public institutions), named member companies. With various activities from electronics to fashion, they constitute the majority of the lab’s funding.

Various research groups (currently 20) compose the ML. In those groups, faculty, students, and researchers work across multiple and diverse disciplines such as social robotics, tangible interfaces, sustainable cities or hyper-performant cameras.

1.1.2 The City Science Group (CS Group) and the 15 minutes walkable city One of the forming groups is the City Science Group, with which the author collaborated during the period of September 2020 - July 2022, first as a research collaborator (until February 2022) and then as a Visiting Student. The CS Group focuses on understanding urban performance and the work-life balance of their communities. In parallel, the CS Group develops interventions that foster more livable urban ecosystems.

In an era when more than 50% of the world’s population lives in cities and more than 80% of the global GDP is generated in urban centers, urban areas are becoming even more essential in society [1]. Urban scenarios are evolving very fast to cope with populations’ exponential growth and rapid changing climate conditions [2]. By 2050, it is expected that 7 out of 10 people in the world live in cities [3]. Cities are growing in size and complexity, and the rhythm of change is increasing [4]. There is a need to understand how these urban centers perform and evolve, as well as how their inhabitants behave. Besides, with a proper understanding, these is also a need to find ways to intervene in these urban areas, redirecting them to a better performance.

The main objective of the CS Group is to enable dynamic, evolving places that respond to the complexities of life. These interventions are being developed and tested together with other labs composing the international City Science Network. In this network, cities as Ho Chi Mihn in Vietnam, Guadalajara in Mexico or the country of Andorra collaborate with the CS Group and test various of the new developments, showing promising results with data. These collaborators constitute as well the main source of funding for the group.

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Figure 1.1: City Science group logo

The CS Group believes that cities should be composed by resilient communities, able to address the great challenges humans are experiencing. Examples of those great challenges are public health, global warming and equity [5]. There is potential for this matter in well-built communities, which the CS Group believes are based on three main aspects [5]:

• Live-work symmetry: this represents the availability of diverse, equitable, affordable and quality housing, matching to the local jobs in a community. Thus, a community offering housing to the workforce, students and elderly people working in that community. If fully realized, it would result in net zero commuting, vastly reducing energy and time wasted in transportation.

• Local access to assets: ideally, people in the community should have walkable, or with ultra light-weight mobility modes, access to all the amenities they need for their day-to-day life, including school, shopping, health care, job, recreation, culture.

• Local production of resources: every community should be able to deploy distributed systems in order to locally generate renewable energy, process waste, purify water and produce fresh food.

These concepts come together in the idea of the 15 minutes walkable city, already publicly proposed by Kent Larson (director of the City Science Group) in 2012 [6]. To meet these conditions, communities should be built by implementing innovative technologies, designs and public policies [5]. New designs of mobility, urban planning and architecture, implementing new technologies and fostered by new public policies are the current needs in urban areas and domain of study for the City Science Group.

In order to contribute towards those cities, the CS Group structure their research projects in three main directions: urban planning, mobility and architecture.

First, inurban planning[48], projects evolve around the City Scope platform. The City Scope platform, as it will be detailed throughout this master’s thesis, gathers those projects focused on developing tangible, digital, data-driven tools. These tools are used by those stakeholders involved in urban planning to propose designs and find consensus. Moreover, these tools open the field of urban planning to new stakeholders, mainly those community members which are traditionally included in the last stage of the process [1]. This is done through intuitive tangible user interfaces, together with data-driven indicators quantifying the impact of the proposed interventions. Participants coming from different domains and with different experience in those domains can then express their ideas and concerns in a quantified and standardized manner.

Second, in mobility [49], projects look for efficient, equitable, inexpensive and reliable access to transportation, while reducing or even eliminating dependency on fossil fuels and private vehicles. In these 15 minutes walkable districts, the CS Group believes that mobility needs

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will be provided by fleets of multi-functional shared, electric, and autonomous vehicles that will behave as a collaborative system [18]. In this future, vehicles are regarded as parts of a system rather than individual entities. Within the system, the relationships and interactions between vehicles will be based on collaboration.

Finally, in architecture [50], projects aim for a more affordable, productive, enjoyable, and creative urban living, through hyper-efficient, technology enabled spaces. With projects such as the CityHome, which then evolved into the current StartUp ORI [51], the CS Group has proven how smart architectural robotics can transform the spaces where people live. Those new spaces make people feel their home-work space is two or even three times bigger than it actually is. These transformable spaces projects are complemented with others as the TerMITes or the BlindEye. In those two projects, decentralized networks of sensors capture data from the environment so the intelligence spaced can react. An example could be a bedroom being transformed into a home-office, by automatically hiding the bed and unfolding a big desk while the person is taking a shower in the morning.

1.1.3 Sustainable Development Goals (SDGs)

The world is facing unprecedent challenges. A recent pandemic, shrinking natural resources, enviromental damage or social inequalities are just some of the duels humans aim and need to solve in the short, mid and long term. In 2016, the United Nations (UN) published the Sustainable Development Goals, an agenda with the objective of unifying the world towards these challenges and build a more prosperous, healthy, and equitable future.

“The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries - developed and developing - in a global partnership.” [24]

This agenda, composed by 17 goals, comes together with a follow-up and review mechanism to help in its implementation, with benchmarks and measures for the different SDGs [52].

The MIT Media Lab, in its fourth decade since is was founded in 1985, is focusing on solving the unprecedented global challenges, social and technological, at the level of systems such as cities, human networks, brains or bodies. More specifically, the CS Group has been working for years in understanding the inter-dependencies in cities that foster a vibrant economy, health, safety and security, objectives that are directly related to the SDGs.

Giving some examples, in the CS Group, objectives as Industry, Innovation and Infrastructure orSustainable Cities and Communities are directly connected to projects pursuing sustainable urban planning and mobility [1, 11]. Other SDGs such as No Poverty orZero Hunger have also been addressed with projects as The Power of Without [53]. It is important to mention as well the effort of the CS Group related to the SDG (17) Partnerships for the Goals, establishing the City Science Network to collaborate with other institutions towards building sustainable urban scenarios [54].

Studying cities performance and developing tools and systems to improve their communities well-being, the main goal of the CS Group, is becoming more and more essential in achieving global sustainability. With 55% of the world’s population living in cities and 80% of global

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Figure 1.2: Sustainable Development Goals [24]

GDP generated in cities, urbanization can definitely impact sustainable growth if fostering productivity and innovation, empowering the emerging of new ideas [3]. The physical form of cities and their land use distributions, influence various generations. A wrong planning can result in unsustainable sprawl. The speed of evolution in cities, with an expectation of doubling their current population by 2050, poses important challenges. An example of these challenges is energy consumption and emissions, as cities currently consume two thirds of global energy consumption and emit more than 70% of greenhouse gas emissions [3]. Beyond emissions, cities may tackle climate change, as their exposure to climate disasters risks are growing as well, with 20% of their current population living in coastal areas and exposed to a sea level rise [3].

Apart from the United Nations, there are other well-known organizations more focused on sustainability through urban management. Among these organizations, one can can find 100 resilient cities, Inter American Development Bank (IDB), UN-HABITAT, The Urban China Initiative or The Global Platform for Sustainable Cities. Each of these organizations proposes different metrics or key indicators to asses urban performance. In the City Science Group, there is been an ongoing effort for the last years to find a common ground among all these indicators and build a complete framework that includes the different metrics and allows to measure urban performance.

Besides, urban transformation needs of capital investment. Therefore, it is important to materi- alize theODS or other proposed metrics for an investor to understand the impact of their actions, as this is proven to be related to a long-term financial success [55].Goldman Sachs, BlackRock, JP Morgan, BNP Paribus, Bridgew, Vanguard, and the Norwegian Sovereign wealth funds are among the $23 Trillion [56] committed to investing in companies that offer measurable impacts towards stakeholder-driven, societal end environmental outcomes.

Adding confidence to stakeholders through advanced modeling and simulation of how interventions will impact their surroundings, provides insights on developments risks as well long-term profitability (return of the investment) for the developer. This conscious planning contributes as well to a promising community well-being. So, the CS Group has been also focusing on the development and testing of various tools to facilitate the urban planning process, together with the mobility needed for those plans, and convey them towards those urban wellbeing objectives.

These tools are based on metrics, assessed by expanding on research in the area of data-driven and evidence-based analysis tools that simulate and highlight the physical, social, economic, and

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environmental outcomes of urban transformation.

Related to SDGs, Environmental, Social and Governance metrics (ESGs) have gained much relevance in urban developments as they quantify the social and sustainability impact of in- vestments in companies or businesses [55]. However, ESG metrics are not enough to asses the impact of interventions in communities’ health. Other more classical evaluations as LEED or ISO ratings also miss many developer benefits coming from well functioning communities, which ultimately impact profitability.

The CS Group understands that urban performance is measured by the balance of Density, Prox- imity (including accessibility), Diversity, Energy (including both resources waste, understood as urban metabolism), Governance and Safety (such as empowering leadership from community members or security within cities). This classification can be also understood as what the Group calls ESG+CR or Community Resilience metrics, highlighting a mutually beneficial relationship between the real estate community and the different stakeholders.

Through urban planning, mobility, and architecture projects, the CS Group aims to drive cities towards high-performing scenarios in any of those metrics. Examples are those tools developed around the City Scope platform, which leverage the current abundance of available data, computational systems, and algorithms, to offer insights into the urban subtleties and human activities.

These insights come at an unprecedented scale and level of detail, key to evaluate the performance or urban areas and their possible evolution when applying interventions.

This master’s thesis will contribute towards the improvement of the CS Group metrics in urban scenarios and, ultimately, it will help in this path towards meeting the benchmarks of the Sustainability Development Goals. More specifically, it will focus on building a high-performing 15 minutes walkable city, directly addressing the following SDGs: (3) good health and well- being, (7) affordable and clean energy, (8) decent work and economic growth, (9) industry, innovation and infrastructure, (10) reduced inequalities, (11) sustainable cities and communities, (12) responsible consumption and production, (13) climate action.

1.1.4 Contributions of this master’s thesis

In the figure 1.3 the structure of the project is outlined. Starting the theCS Group’s mission (1), this master’s thesis focuses on building the path towards the 15 minutes high-performing walkable city. There are, among others, two key needs for those cities to happen: a new mobility paradigm (2) andnew urban planning processes (7).

First, traditional mobility must be reinvented. The needs to move people, goods and perform utility services will change in high-performing, walkable urban scenarios. These urban scenarios need of a newmobility framework (3), which has been defined. An article outlining that framework has been published [18]. This new mobility framework comes with: (4)the need of new vehicles, for which a contribution with driverless skills for the MIT Autonomous Bicycle has been developed. (5) The need of new infrastructure, for which anovel solution for electric vehicle charging has been defined and built. An article has been published outlining that solution [17]. Finally, in this mobility framework vehicles will operate in new manners (6), and so a simulation has been built to validate how performing is the new operation.

Second, urban planners need to change their interventions in cities. The CS Group has been working for years in the City Scope, a platform to design and evaluate urban designs. A contri-

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Figure 1.3: Diagram representing the different contributions of this master’s thesis in the CS Group’s research context. Outlined in blue, the mission of the CS Group. In green, two of the main lines of research of the CS Group. These lines organize the CS Group’s research towards its mission. In purple, the contributions of this master’s thesis towards the aforementioned mission.

Finally, in orange, those contributions for which an article is already published.

bution to this tool has been made, with agenerative 2D tool (8)that assists urban planners for an easy creation and optimization of designs. The CS Group is now transitioning towards a high-dense, vertical, human-scale, urbanism. This vertical urbanism replicates the vibrancy usually located at the ground level to the upper-levels. Thus, the author has been participating in the re-definition of theCity Scope platform (10), with two additional contributions: first, with the network structure (11)module, an algorithm to parse those CAD files traditionally provided by urban planners and architects, to extract the data inside for a quantitative analysis.

Second, with a new version of thegenerative tool (9), this time able to deal with the creation and optimization of vertical urban planning [3D].

Disruptive paradigms are used at the MIT Media Lab to explore innovative ideas, so unpredict- ability is a constant factor. The MIT Methodology followed is based on learning by doing, with ideas being rapidly iterated to learn about their viability and performance. Thus, this master’s thesis adapts and reflects this methodology.

To outline these contributions, the reminder of the document is organized as follows. A more extended introduction to the aforementioned contributions will be given below. Section 3 contains an overview of the previous and present work in the fields where the contributions take place.

Section 4 aims to give a detailed description of the developments proposed for these future cities.

Section 5 will show the conclusions extracted from the work on those developments. Finally, Section 6 will introduce the proposed work to continue progressing in this idea of a 15 minutes high-performing walkable city.

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1.2 A new urban mobility in the 15 minutes walkable city

1.2.1 A new framework for urban mobility

The transportation sector has been the largest contributor to greenhouse gas emissions in the US since 2017 [57]; it accounted for 29 % of the emissions in 2019 and, despite the measures taken to reduce the environmental impact of mobility, transportation-related emissions are still increasing [57]. Moreover, predicted global urbanization and population growth rates [58] further confirm the urgency to find mobility solutions that provide an efficient and ecological service in cities.

The main drivers for change in the urban mobility landscape in the coming years are predicted to come from electrification, vehicle sharing, and autonomy [7]. From the climate change perspective, vehicleelectrification combined with a deep decarbonization of the grid is predicted to be key to meet the mid-century emission reduction targets [12]. In fact, many countries- United States, Japan, India, China, and most countries in the European Union - have set ambitious electrification goals for 2050 [59]. Vehicle-sharing is expected to reduce the number of vehicles on the roads [60–62] by offering a service that combines the convenience of private vehicles without the costs and responsibilities of ownership [63]. Vehicle sharing can also promote the use of electric vehicles, which have a high purchasing cost but low operating costs [10, 64]. Finally, autonomy is expected to increase efficiency of fleets and, while it could also increase travel demand, research indicates that if combined with vehicle sharing, autonomy would reduce overall emissions through increased efficiency and vehicle utilization rates [9, 65].

These trends are expected to alleviate many of the urban mobility-related problems [8–11].

Nevertheless, studies suggest that this might not be enough in order to meet climate change mitigation target and that a total reduction in vehicle miles traveled will be critical [12, 13]. For instance, according to Alarfaj et al. [12], in order to meet a 80 % carbon emission reduction target by 2050 with the expected travel demand, the electricity grid would have to be zero- carbon by 2050. However, if the vehicle-miles traveled were reduced to a third, that carbon targets could be met at an electricity carbon intensity of 100 g CO2/kWh, which is a quarter of the current carbon intensity in the US, but still above the emissions from other countries such as Sweden, France, Finland, or Austria [12, 66, 67].

Vehicle miles traveled depend on human behavior, and this behavior depends on the built environment. Studies have shown that destination accessibility and job-house balance can reduce the vehicle miles traveled by car [68]. A great solution for this can be those human scale cities proposed bu the CS Group. These cities, being composed of dense and diverse high-performing districts, would have all the resources and amenities needed in daily life in a walkable distance, drastically reducing the need for commuting [1]. In these districts, most of the trips would be one-person, short distance, and low speed, reducing car dependency and favoring more sustainable modes, such as walking and shared micro-mobility [1, 43, 69]. Other mobility needs such as package and goods delivery would also be served at a local neighborhood scale by fleets of shared, electric, and autonomous micro-mobility systems [70].

The fact that vehicles are becoming autonomous is opening many new opportunities, especially regarding the interactions among vehicles and how these interactions will impact system-level behavior. Autonomy will provide vehicles with the ability to communicate with other vehicles, humans, and the infrastructure, as well as the intelligence to make decisions based on this communication [71]. These communications skills are opening the door to a future in which mobility would work as a collaborative system. In this sense, vehicle platooning can be seen as

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the first step towards joint mobility, since it studies the coordinated movement of autonomous vehicles to improve safety and reduce congestion and fuel consumption [14]. However, the interaction between vehicles could be much richer than communicating to coordinate. In nature there are many inspiring examples of collaboration; for instance, in insect colonies local and simple interactions lead to complex system-level behaviors [15]. The behavior of these natural swarms has been transferred to artificial systems by researchers working in the field of swarm robotics, proving to make systems more flexible, scalable, and robust [16].

Therefore, inspired by nature and supported by swarm robotics, this project works on a future mobility in which shared, electric, and autonomous vehicles would be multi-functional and behave as acollaborative system in high-performing walkable districts. Fleets of multi-functional vehicles would complete different tasks collaboratively and giving a response to the different mobility needs of the city, on-demand. The local and direct interactions between autonomous vehicles in this proposal has been namedvehicle clustering. When vehiclescluster, they connect either physically or virtually, being able to share data, provide services, share computational power, transfer energy [17] or even potentially transfer cargo.

Figure 1.4: Swarmanoids, example of swarm robotics.

This proposal does not intend to provide all the answers to how future urban mobility will operate, but rather contribute with a framework for future mobility based on current research and mobility trends. While the presented proposal borrows from current research, to the best of the CS Group knowledge, it is the first time that such concepts are integrated this way.

To achieve such urban mobility framework, there is a need, among others, of new ultra- lightweight vehicles,new infrastructure, and new operations.

1.2.2 New multi-functional ultra-lightweight vehicles for the future collaborative urban mobility

The aforementioned mobility system for these high-performing districts needs of new electric, shared, autonomous vehicles. Besides, these vehicles should be multi-functional and able to collaborate in order to meet the different mobility needs. The CS Group has worked during the last years in three main vehicles:

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• CityCar [19] [2001-2013]: the city car is an electric vehicle offering people and goods mobility while providing weather protection, climate control, comfort and crash protection.

Moreover, this is performed in a clean and economic way. This vehicle is capable of folding and turning its four wheels, so three or four CityCars can be parked in the length of a traditional parking bay.

• Persuasive Electric Vehicle (PEV) [20]: the PEV comes a a more agile, healthy, convenient and sustainable alternative to cars. The CS Group changed the perspective to autonomous vehicles, realizing that folding cars would no longer be useful as they would not be stored in high-value city centers [5]. This new fully driverless proposal is both for people and goods mobility. It can be either an electrically assisted tricycle for passengers or an autonomous carrier for goods delivery. Being operated in bike lanes, it can not only share space with pedestrians but also contribute to reduce congestion.

• MIT Autonomous Bicycle project [11]: this third proposal also offers an efficient, ecological, inexpensive, and reliable mobility choice to transport people, contributing to a lower pollution and congestion in their daily routines. This new proposal comes with some trade-offs with respect to the PEV. The main differences between both solutions, the PEV and the MIT Autonomous Bicycle, were the cost and level of intelligence. While the PEV constitutes a high-cost solution, being able to circulate and transport people and goods in cities being completely autonomous, the bicycle constitutes a low-cost solution for people transport that relies on a smart city to guide her in both routes and multiple events that might happen. An example of this could be traffic lights, where the PEV detects the color of the light same as the human eye, but the bicycle would rely on a more adapted technology such as radio transmitter to inform her about the traffic light status.

Figure 1.5: The MIT Autonomous Bicycle

This master’s thesis contributes to the MIT Autnomous Bicycle prototype, developing and implementing a lane following algorithm, so the bike can navigate autonomously in a bike lane.

In parallel, ideas were thought about low cost solutions that could be easily implemented in cities to make them smarter. This algorithm was thought as a first iteration to showcase how the MIT Autonomous Bicycle Project could evolve, which then could be further developed towards a more solid driverless skillset for this bicycle.

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1.2.3 New infrastructure and new systems for the future collaborative urban mobility

The proposed mobility framework also needs of new infrastructure that allow vehicles to be multi-functional and collaborate as a system. This master’s thesis proposes a solution to allow vehicle collaboration in one of the proposed and most useful forms: energy sharing among vehicles while moving.

Vehicles electrification, one of the trends that will transform mobility [7],have the potential to alleviate many of the environmental and urban mobility-related problems [8–11]. For example, according to Greenblatt et al. [9], in the US by 2030 each electric autonomous taxi implemented could reduce by 87-94% the greenhouse gas (GHG) emission per mile traveled below internal combustion engine vehicles (ICEVs), as a result of sustainable power generation, fleet size reduction, and more cost-effective high-performance electric cars. Since EVs have a high purchasing cost but low operating costs, vehicle-sharing might foster the adoption of EVs [64].

EVs have numerous benefits over ICEVs: they have a lower overall environmental impact, lower maintenance costs, higher reliability, reduced vibration and noise, and better accessibility in cities with vehicle access restrictions [72]. Moreover, an indirect but very relevant benefit of vehicle electrification is that EVs can help to balance demand and supply of the electric grid and compensate for the irregular supply of some renewable energy sources, which will favor the implementation of micro-grids [73–75].

However, charging issues such as limited driving range or lack of convenient charging infrastructure pose significant obstacles preventing EVs from populating the roads [21]. While there have been numerous proposals for EV charging in the last decade, they are either inconvenient or require expensive and complex infrastructure [76–83]. Due to the fast population growth, it is estimated that the global investment needed in infrastructure will be 4.7 trillion USD per year through 2035 [84]. As a consequence, solutions for future mobility should be based on lightweight distributed infrastructure.

With this idea in mind, and considering the future proposed in the aforementioned framework, in which mobility will work as a collaborative system [18], this master’s thesis contributes with a charging method in which vehicles would share battery with other vehicles while moving. This solution contributes with one of those necessary systems for the proposed future. Instead of following their own rulesets, vehicles would do what is more efficient for the system as a whole, and vehicles will access energy anywhere and anytime. This lightweight infrastructure solution will minimize costs and infrastructure availability problems. Vehicle downtime and the miles traveled related to charging would also be reduced, increasing vehicle usage efficiency.

1.2.4 Understanding and validating new scenarios and operation in the future collaborative urban mobility

The proposed on-demand mobility, collaborative and working as a system, needs of new operation, as it is completely detached from traditional mobility.

Transportation systems are complex systems in which the many elements that compose them interact direct and indirectly, with many feedback cycles, and that often lead to non-linear relationships [85]. The increasing complexity and dynamic nature of mobility make traditional planning fall short on the necessary flexibility and adaptability, and developing analytical optimization methods for transportation has not been straightforward already for several years [86].

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Nature shows many inspiring examples of systems that can dynamically respond to complex and unknown scenarios. For instance, in insect colonies local and simple interactions lead to complex system-level behaviors that go beyond the capabilities of each individual agent [15,87]. Based on collaboration natural swarms can divide tasks, cluster, build together, or transport cooperatively, among others [15]. For instance ants can coordinate in colonies of hundreds of thousands of members with only local tactile and chemical communication [88]. In some ant species, ants can find the food sources that are closest to the nest, as well as finding the minimum path between the nest and the food as a combination of the actions of many ants [15]. The behavior of these natural swarms has been transferred to artificial systems by researchers working in the field of swarm robotics, in systems that are simple, decentralized and self-organized. These characteristics have proved to make systems more flexible, scalable, and robust [16]; benefits that are also critical to urban mobility systems.

Autonomy will provide vehicles with the ability to communicate with other vehicles, humans, and the infrastructure, as well as the intelligence to make decisions based on this communication [71], which can lead to new ways of collective behavior. By imitating these nature-inspired processes, urban mobility could behave in a self-organized and demand-responsive way, serving community needs while showing those benefits. This project will contribute in replicating those characteristics seen in natural swarms in a mobility system, to study viability and measure performance of such as system, comparing it to current solutions. An agent-based simulation will be built to model this system in some specific use cases, which will be extended in the future towards a full understanding of how future mobility in high-performing urban scenarios should be designed and implemented.

The first iteration will focus on exploring the potential of these bio-inspired systems in addressing one of the main issues in shared micro-mobility services: the rebalancing problem. The rebalancing problem is a consequence of the unbalanced user travel patterns that cause vehicles accumulate in some areas while others get empty. In current systems, this requires system operators to redistribute the vehicles throughout the city in vans or trucks, which has a very high ecological impact. However, in the case of vehicles being autonomous this problem can be tackled in a different way. In the following, vehicle rebalancing is proposed as a process based on stigmergy: a bio-inspired form of indirect communication [89]. When vehicles identify user demand, they leave a pheromone trail. This pheromone trail will guide other vehicles towards that area of demand, potentially increasing vehicle availability and reducing wait times.

1.3 A new urban planning for the 15 minutes walkable city

1.3.1 Urban planners need of tools to assist them in creating and optimizing their models

Cities are growing in size and complexity, with an increasing rhythm of change [4]. Mature cities and new urban developments need to follow this changing rate, and there might be insufficient time [2]. Global socio-political developments may yield to immigrant waves, which will pose even more physical and social challenges in urban scenarios [90]. There is then a need to find ways of understanding the inter-dependencies of those elements composing the urban areas.

Besides, with a proper understanding, these is also a need to find ways to intervene in these urban areas and redirect communities to a better performance. Thus, there is a need of tools to rapid prototype and evaluate urban scenarios and possible interventions.

Urban transformation usually imply complex conversations between stakeholders, which com-

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monly involve community members, administrative bodies and technical professionals. Finding consensus is then a difficult task, due to facts such as different levels of expertise or different interests among stakeholders [22]. Moreover, community people are usually included in the last stage of the process [4]. This is also why, in the United States, projects take more than ten years to get community approval [4].

The City Science Group has focused these last years on developing tools around the City Scope [1]

platform, with human-machine interaction platforms that democratize the urban planning process. These tools include community members in the consensus process, traditionally occupied by some experienced stakeholders. Besides, tools as the City Matrix [4] have performed human- intelligence augmentation using AI, helping users to optimize their solutions being assisted by machines.

However, human-intelligence augmentation tools may fall in the optimization task and users will blindly follow their suggestions, not understanding the reason of the output and so killing the so essential consensus process. There is thus a need for a tool that leverages non-experienced users in the urban planning decision process, and this tool must be transparent and inspirational enough so it help users arrive to their desired designs without guiding them towards a unique and optimized solution.

This master’s thesis will then propose a human-machine interaction tool, with human-intelligence augmentation. This tool will offer a transparent guidance to decision-makers, to they understand how the urban development can be optimized and so serve as a source of inspiration for them. It will solve the multi-objective optimization process of urban planning with an evolutionary genetic algorithm, in such a manner that users will be inspired towards a design that prioritizes their objectives but which is ultimately designed by them. A first iteration of this tool will be built, showing the potential in the stakeholders consensus process, blurring the level of experience among stakeholders and so leveraging non-experience community people. These community members will now be able to propose their own and valid proposals for new urban interventions.

Furthermore, with the growing complexity in urban scenarios, such a tool would be useful even for experienced stakeholders.

1.3.2 A new necessary urban planning for high-dense scenarios: towards vertical cities

The population growth and its migration to cities, as it is expected that 7 out of 10 people will live in urban areas by 2050 [3], is putting pressure on land consumption, as it is expected to add 1.2 million km² to new urban built areas [3]. This is critical as well for natural resources, and it requires new forms of urban planning. Bettencourt’s, who claims that cities’ growth is mainly driven by social networks, affirms that cities need to evolve to be spatially denser and more productive [91].

So far, skyscrappers in cities are receiving this density and containing in some sense the urban sprawl, being equivalent to small cities (e.g. the World Trade Center’s twin towers had about 50,000 workers in a 10 million square feet of space) [92]. Besides, the same way cities are composed by multiple land uses, these tall buildings can be mixed-used too. The Shanghai Tower is a great example of a mixed-used tower, divided in nine vertical zones which include retail at the bottom, offices in the middle, and hotels, cultural facilities, and observation desks at the top [93].

Although urban morphologies need to be more compact to attain with this density objective,

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there is a current situation where verticality has been implemented in single buildings (cent- ralization), while the urban area continues to grow laterally (decentralization) [2]. This makes the urban ecosystem grow as separate entities instead as an integrated area which would be increasing the city density while providing more accessibility and proximity to the required resources.

Thus, the CS Group is looking beyond building a bunch of unconnected sky-scrappers, or even connected in multiple heights. The CS Group is proposing a whole new form of vertical urbanism, not vertical architecture, as reflected by Rem Koolhaas in these words:

“If there is to be a “new urbanism” it will not be based on the twin fantasies of order and omnipotence; it will be the staging of uncertainty; it will no longer be concerned with the arrangement of more or less permanent objects but with the irrigation of territories with potential; it will no longer aim for stable configurations but for the creation of enabling fields that accommodate processes that refuse to be crystallized into definitive form; it will no longer be about the meticulous definition, the imposition of limits, but about expanding notions, denying boundaries, not about separating and identifying entities, but about discovering unnameable hybrids; it will no longer be obsessed with the city but with the manipulation of infrastructure for endless intensifications and diversifications, shortcuts and redistributions – the reinvention of psychological space.” - Rem Koolhaas, S,M,L,XL [94]

Figure 1.6: City of the Future by Harvey Corbett (1913), proposing horizontal mobility at multiple levels within the city [25]

This being said, vertical urbanism seems to mean replicating the pulse of the city, its buoyancy, vitality, vibrancy, serendipity, which are usually located in the ground floor, in upper-level areas.

This is not building simplified and sterilized structures, which would result in inert areas.

Some already existing examples of vertical urbanism can be found in places as the Cosmo Park

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in Jakarta [40] or the city of Hong Kong. Hong Kong, one of the best current references in vertical cities, shows a hyper-dense, vertical, mixed land use, with multi-functional and integrated development, land preservation and environmental sustainability (it has been able to preserve 75% of its overall area in its natural state), among other important characteristics [23].

However, Hong Kong also suffer in the world’s livable rankings for a lack of open space, greeneries, privacy and for having important vehicular and noise pollution, due to a poor natural ventilation, as well as for some incompatibility in land uses, due to poor planning [23].

As cities are evolving to be more dense and vertical, tools to design and analyze vertical organization are becoming more and more important. The idea of a vertical city with multi-functional land uses is gaining attention as the most viable solution to allocate the increasing density while limiting the build-able land and maintaining or improving the economy and sustainability of the city [23].

The appearing challenges, such as incompatibility of uses, environmental health deficiencies or miss-access to sunlight, together with the already growing inherent complexities in cities [4], are adding complexity for the design of these tools.

In the CS Group there is an ongoing effort to understand how these aforementioned hyper- performing and resilient communities can grow in a vertical city, as it will be the only way to comply with the vastly increasing density needed. Furthermore, the CS Group is working in a tool that allows to both design and evaluate urban scenarios and possible urban interventions, a reinvention of the City Scope platform, to quick iterate and address great challenges humans are facing (e.g., public health, global warming, equity).

This master’s thesis will contribute to this tool. First, with a module to assist urban planners in the optimization of their designs. The first 2D iteration of this module, mentioned in the last section, is standalone and compatible with the current City Scope platform. Then, a 3D version will be introduced as a second iteration for the vertical city tool. Second, with a module to parse CAD files, traditionally used by architects and urban planners to propose their designs.

This module will allow to transform a CAD design in a nodes & links network data structure so urban proposals can be easily evaluated.

2 OBJECTIVES

In this section, the objectives tied to the realization of this master’s thesis are presented. These objectives pursue the application of all the knowledge acquired by the author as an engineering student, as well as that knowledge coming from a minor in robotics.

1. Contribute to the improvement of the well-being and sustainability of urban communities.

This contribution will be framed within the main mission of the City Science group: building 15 minutes high-performing walkable cities, more resilient, sustainable and equitable than the current ones.

(a) Contribute to the mobility necessary to meet the needs in those high-performing urban scenarios, by:

• Defining a new future urban mobility framework, which will allow researchers, including the CS Group, have a solid base-point to define and develop new systems, technologies, policies, etc. that will drive the present mobility towards this