Code shift : data, governance, and equity in Los Angeles's shared mobility pilots

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Code Shift: Data, Governance, and Equity in Los Angeles’s Shared Mobility Pilots

By

Emmett Z. McKinney BA in Public Policy and French

Vanderbilt University Nashville, Tennessee (2016)

Submitted to the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degree of

Master in City Planning at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020

The author here by grants to MIT the permission to reproduce and to distribute publicly paper and electronic copies of the thesis document in whole or in part in any medium now known or hereafter created.

Author___________________________________________________________________ Department of Urban Studies and Planning May 20, 2020 Certified by _______________________________________________________________

Lawrence E. Susskind Ford Professor of Urban and Environmental Planning Thesis Supervisor Accepted by_______________________________________________________________ Ceasar McDowell Professor of the Practice Chair, MCP Committee Department of Urban Studies and Planning

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Code Shift:

Data, Governance, and Equity in Los Angeles’s Shared Mobility Pilots

By

Emmett Z. McKinney

Submitted to the Department of Urban Studies and Planning on May 20, 2020, in partial fulfillment of the

requirements for the Degree of Master in City Planning

ABSTRACT

Transportation planners suggest that smart mobility systems – cars, bikes, scooters and other vehicles connected to the internet – can advance social equity. While smart mobility systems can help address transport poverty, new technologies may also reproduce power asymmetries

between communities, government, and mobility service providers. Through case studies of several of Los Angeles’s shared mobility pilots, I argue that mobility equity demands the fair distribution of power (i.e. the right to co-design new systems and a role in adapting their

operations), not only of resources. Designing mobility systems that are both equitable and smart, therefore, requires transportation planners to better integrate the lived experiences of residents, especially the poor and the disadvantaged, into data-driven planning efforts. Open data

frameworks such as MDS (i.e. Mobility Data Specification) enhance the possibility for co-design and increased mobility equity – while also presenting new obstacles to overcome. To advance mobility equity, transportation planners should begin with inclusive data governance.

Thesis Supervisor: Lawrence E. Susskind

Title: Ford Professor of Urban and Environmental Planning Reader: Karilyn Crockett

Title: Lecturer of Public Policy and Urban Planning Reader: Eric Huntley

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Acknowledgements

I arrived at the end of this journey with help from many along the way. I’d like to share my deepest gratitude with:

My committee – for their thoughtful feedback, patience as I tried out new ideas, and support until the very end.

Community activists – who, after decades of working for justice, took the time to help me grow. People for Mobility Justice, Alliance for Community Transit-LA, the Los Angeles County Bicycle Coalition, and the Labor/Community Strategy Center were particularly instrumental. Transportation planners – who sharpened my ideas and lent a helping hand to set up interviews. Jascha-Franklin Hodge, Andrew Salzberg, Joshua Schank, and Natalia Barbour deserve

particular thanks.

CoLab – for inspiring me to think deeply about equity, affirmed me as I took intellectual risks, and offering me a home within DUSP.

My fellow DUSPers – who awe me with their compassion, intellect, and creativity. I am grateful to Hannah Hunt Moeller, Dylan Halpern, Ian Ollis, Dan Powers, Max Arnell, Alex Acuña, Ayushi Roy and many others who reassured me when my confidence wavered, and shared many laughs along the way.

Finally, none of this would be possible without Zach and Zoë, who taught me to live with empathy and a sense of adventure – or Mom and Dad, who have always said ‘yes’ to my curiosities. I love you all so much.

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Author’s Note

This research started, as many projects do, on Twitter. While transit experts increasingly signaled commitments to equity, other commentators – and women of color in particular – questioned the veracity of these claims. I set out to understand where the conflicts emerged, and what role new technologies might play in addressing them.

Among my favorite discoveries was the term ‘Fakequity,’ coined to describe “when you think you’re doing equity work but you’re really passing off a project as equity and perpetuating the same power dynamics with no community accountability.” Fakequity practices range from ‘Potlucks and Fake Community Engagement’ and ‘Awareness Raising But Doing All the

Talking’, to, at the high end of the spectrum, being ‘Equity Brave’ or an ‘Equity Champion.’ The advanced user can play ‘Entitlement Bingo,’ which helps them listen for phrases that hint their colleagues may only be giving lip service to ‘equity’(Okuna et al., 2015).

This portmanteau is a tongue-in-cheek framing of a larger and more serious debate in urban planning as to how ‘equity’ should be defined, measured, and implemented. I used this thesis as an opportunity to delve into this debate, and consider my own role. I first attempted this

computationally, trying out Gini coefficients and measurements of spatial accessibility to assess equity. I soon learned that equity is complex – not easily reducible to an algorithm. It depends much on the history of a place, and one’s personal experiences, which invariably shape the statistical models we use to describe the world.

I am a male, white, cis straight man who has the privilege to study at MIT. I cannot speak to what ‘transportation equity’ means from personal experience of having been marginalized. What I can do is use my power as a graduate student to amplify the voices of people doing the work on the front lines. I can listen, and make room for these experiences in spaces where enthusiasm for cutting edge technology often overpowers personal reflection on the problems we are trying to solve.

My hope is for this thesis to leave readers feeling challenged; that it will push transportation planners to consider deeply what they mean by the term ‘equity,’ and reflect on how their practice and methods reflect their values and experiences. I hope that this will fortify the ability of community groups to intervene in tech spaces, and amplify marginalized voices in mainstream planning discussions. Finally, as urban planning schools (including my own) develop new

curricula in urban science and analytics, I hope this research prompts scholars to place cutting edge technology in conversation with urban history.

In engaging these topics, I joined a conversation that started long before I wrote the first word, and which will continue long after the final footnote. Community activists, planning

professionals, and various scholars generously offered their perspectives for this project. I have done my best to organize their views here, in a way that is engaging and clear. In so doing I imposed my own lens, and could not include all that I learned. This is a starting point – and I would encourage you to continue straight to the source. Communities can recount their own experiences far better than I ever could.

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Chapter 1: Introduction

“Through daily mobility, we socialize or seek solitude, negotiate our identity and perform a range of social roles; through mobility we may contest power relationships and claim our right to participate in society or we may be excluded and ignored.”- Marco Brömmelstroet

Transportation systems are not, and have never been, neutral. People experience them differently based on gender, color of their skin, socioeconomic status, disability status, sexual orientation, and many other facets of identity. This complexity should give us pause when we hear ‘data-driven’ and ‘equitable’ invoked alongside one another. As “three revolutions” unfold toward shared, autonomous and electric mobility (Sperling, 2018), transportation planners increasingly declare a commitment to both data-driven and equitable planning practices.

Data-driven implies efficiency, standardization, and scale. Equity implies history,

complexity, attention to physical space and personal experience. These two sets of principles are not inherently at odds – indeed, establishing equity metrics and outcomes is key to addressing historical inequalities. Realizing transportation systems that are both ‘smart’ and ‘equitable’ demands that planners engage with the potential conflict between these ideas – and modify planning practice accordingly.

This thesis calls attention to the discord between the way that 'equity' is talked about in the private sector, and how communities understand it. New mobility technologies are not likely to be silver-bullets for historically inequality -- but by changing the relationship between

communities and government, they may be able to facilitate the rebalance of power that is essential to more just cities. In the context of data-driven governance, this implies that planners must look past the operation of mobility services, to consider the data systems that define the basis for discussion – an idea I call code shift.

code shift /kōd/ /SHift/

1. noun : a refocusing, by professional planners, private service providers, and

communities, on data governance as an essential domain of equity.

2. verb: to facilitate the implementation of community-defined transportation equity

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Code shift' proposes that in order for smart mobility systems to be equitable, community representatives must be present in the room in order to decide how data is collected, interpreted, applied, and shared. It may not be the case that a community advocate is the one writing the actual code; indeed, co-design implies that technical experts also offer their own abilities. But the governance of MDS extends far beyond just the design of the specification; it also gets

combined with other datasets, packaged and sold, and generally used as the basis for

policymaking. In the rooms where MDS is being designed, it is not only a way of packaging data; it is an entirely new system of governance. This system of governance, 'open' as it is, holds promising possibilities for participation and equity. However, the accountability structures that surround MDS are rather unclear; thus it is not certain who has power in this new, quasi-open environment, and if that individual is responsive to community needs. As these new technologies are deployed in a context of structural inequality, it is not at all clear that, absent community participation, that 'smart mobility' tech will, in fact, produce the equitable cities they propose to.

The central argument is that equity demands the fair distribution of power; not only of resources. Smart mobility technologies hold potential to serve unmet mobility needs – yet,

without concerted efforts to correct historical power imbalances, these technologies are not likely to serve the needs of marginalized groups. Realizing mobility equity in the future, therefore, demands meaningful community engagement in governance of smart mobility devices – and specifically in the digital infrastructures that define what information is considered a valid input for policymaking. I introduce the idea of code shift, to assert that transportation planners, community advocates, and private mobility service providers ought to focus on inclusive data governance as a key strategy for ‘mobility’ equity.

This high-level assertion unfolds through three more specific arguments about Los Angeles. First is that the open data systems developed in LA are creating a fundamentally novel relationship between governments, private mobility providers, and the public -- which creates both opportunities and hazards from the vantage point of equity. Data systems being developed now offer the potential for more responsive service provision, but also raise questions about accountability and present tradeoffs between transparency and privacy. The outcome of these debates will shape mobility systems long into the future; as such, it is critical that communities play a role in data governance.

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Second, the history of transportation planning in Los Angeles reveals severe power imbalances, which have not been reflected in the official assessments, or data sources

conventionally used to measure 'equity.' Moreover; transportation (in) equity in Los Angeles is complex. The fact that low-income and minority communities are under-served results not only from transportation technologies and infrastructure, but also in housing discrimination, police brutality, and the geography of the Los Angeles region. Amending these inequities will require far more than the introduction of new technology; rather, it will take prolonged coordinated effort between communities and government – which, in turn, will require the equitable sharing of power. However, historical power imbalances are likely to permeate into today’s mobility systems, absent intervention from planners that aim to create a more even playing field. The high-tech nature of smart mobility systems poses a particular barrier to meaningful participation – which will requires particular attention from planners to enable more effective participation. The third key argument is that just placing a new mobility technology in an under-served community is not enough for people to use it and derive benefit from it; it also matters how people are engaged. Case studies of electric vehicle, bicycle, and scooter-sharing pilots in Los Angeles reveal that the methods of sharing power community representatives shapes has an important impact on the efficacy of other policies aimed at reaching under-served groups, such as price discounts and unbanked access options.

These arguments – that data is changing governance, that transportation systems have never been equal, and that engagement matters – point to the need to re-think planning practice. . The final chapter draws on principles of participatory action research (Greenwood and Levin, 2007), Data Feminism (D’Ignazio & Klein, 2020) and Design Justice (Costanza-Chock, 2020) – as well as various frameworks offered by communities – to outline implications for practice.

Motivation

Since the 1991 Rio Declaration introduced the idea of sustainability, equity has been invoked as a guiding principle in all kinds of interventions aimed at mitigating climate change (Alcock, 2008). Yet – as scholars of the “just transition” have argued, the global environmental movement has often deepened inequalities, or at the very least failed to engage with the ones that exist (Evans & Phelan, 2016; NEWELL & MULVANEY, 2013). Transportation planners must

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reckon with how transportation systems have failed to serve some groups in the past. Despite talking about “sustainability” and “equity,” but they have often failed to add new transport technologies in ways that have addressed existing inequities caused by non-transport related decisions (Mullen & Marsden, 2016).

Recent calls for climate justice have renewed public discourse around how efforts to minimize greenhouse gas emissions intersect with historical inequality (Goh, 2020). As the largest source of greenhouse gas emissions in the United States, transportation is a key field to consider this tension. By emphasizing “equity” as a concern, smart mobility providers are making some claim to this unjust history. There is a sound argument to be made for increasing the availability of non-automobile transportation options on economic and environmental grounds alone. The addition of this 3rd_{“E” signals that through interventions that are both} profitable and produce lower greenhouse gas emissions, transportation planners can

simultaneously address the forces that have systematically denied mobility opportunities to communities of color, low-income groups, indigenous populations, and gender non-conforming groups (as well as the many others that have been excluded or short-changed).

Equity, however, is a contested and complex term. I adopt the stance offered by the Greenlining Institute’s Making Equity Real in Mobility Pilots Toolkit (2019).

Equity is transforming the behaviors, institutions, and systems that disproportionately harm people of color. Equity means increasing access to power, redistributing and providing additional resources, and eliminating barriers to opportunity, in order to empower low-income communities of color to thrive and reach full potential. While this definition focuses on racial and economic notions of justice, the most important element is its focus on power. Important equity discussions must also be had with respect to gender, age, disability status, and other personal features that impact one’s ability to move safely and comfortably. Similarly, I acknowledge that there are many notions, sometimes in tension with one another, about how equity ought to be measured, which groups should receive preferential treatment, and which indicators ought to be used to assess outcomes. Many scholars have offered high-level philosophical frameworks for assessing transportation equity (Banister, 2019; Golub & Martens, 2014; Martens et al., 2012; Pereira et al., 2017; Soja, 2013). This discourse has translated into debates over how philosophical judgments ought to be

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embedded in transportation models, and other digital systems (Bertolaccini, n.d.; Bills & Walker, 2017; Karner & Golub, n.d.; Karner & Niemeier, 2013). In parallel, a significant group of

thinkers have considered equity through a community-based lens, questioning whether the historical methods for assessing equity have adequately addressed the needs of marginalized groups (Larson, 2018; Mann, 1996). This school tends to emphasize differences in lived

experiences among different travelers to underscore a complex reality, which may not be readily captured in datasets fed into the models – regardless of how those models are calibrated (Barajas, 2019; Lugo, 2018; Sheller, 2018).

It is this diversity of viewpoints on the meaning of ‘equity’ means that makes it so important to consider how power distributed. It matters great who gets to decide how equity is defined, and what tools are being used to carry it out and measure outcomes. To label a given technology as ‘equitable’ vastly over-simplifies an unjust history, and overstates the ability of any one technology to address all facets of it. Indeed, a growing body of literature documents new equity concerns of racial discrimination and economic exclusion in smart mobility

technologies (Brown, 2018; Fleming, 2018; Groth, 2019; Kim et al., 2019; Marsden & Reardon, 2018; Shaheen et al., 2017).

This complexity is not disqualifying to new technologies. Technological innovation can and should make contributions to people that have been underserved in the past. However, this discourse reveals that such an outcome is not inevitable; and implies that in order for new

technologies to advance social equity, they must engage with the historical context in which they are conceived.

Why Los Angeles?

In City of Quartz, Mike Davis suggests that ‘the ultimate world-history significance – and oddity – of Los Angeles is that it has come to play the double role of utopia and dystopia for advanced capitalism” (Davis, 1990, p. 18). Indeed, LA looms large in the public mind about ‘cities,’ and wields outsize influence in conversation about mobility innovation. However, the city is itself deeply complex and divided. Thus it offers a microcosm for how tech innovations interact with structural inequality.

The auto-oriented metropolis has recently set ambitious goals to overhaul the regional transportation system. Measure M – a ½ cent sales tax ballot measure passed in 2016 – will

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provide $860 million per year, to 40 major transit projects over the next 40 years, county-wide (Los Angeles Metropolitan Transportation Authority, 2019a). The City of Los Angeles’ Green New Deal is similarly ambitious, aiming to increase the share of zero-emission vehicles on the city’s streets to 25% by 2025, 80% 2035, and 100% by 2050 – part of a larger plan to reach carbon neutrality by 2050 (L.A.’s Green New Deal | Sustainability PLAn, 2019). Shared, autonomous, and electric mobility systems – collectively referred to here as ‘smart mobility systems’ – will play a central role in reaching these goals. Moreover, a central plank of the city’s plan is “a promise to deliver environmental justice and equity through an inclusive green

economy.” Such a prominent commitment to equity, combined with the scale of transformation the city envisions, make it a key place to examine how climate justice goals are being

implemented in practice.

Likewise, Los Angeles is a key place to see what role technological innovation in bringing about this transformation. The Los Angeles Department of Transportation’s 2019 Technology Action Plan acknowledges that emerging mobility technologies “are changing the foundational assumptions of how we build and manage transportation systems” (LADOT, 2019). Indeed, LA has taken a proactive stance to tech innovation, creating new forms of governing disruptive mobility technologies such as shared, bikes, scooters, and cars. As these new technologieshave rolled out, public debate has renewed over what mobility equity means and how it ought to be measured.

Los Angeles does not fit neatly into the ‘spatial mismatch’ hypothesis that has often been used to describe transportation (in)equity (Blumenberg & Manville, 2004). The low-income communities clustered in a dense halo around downtown, such as South LA, Little Tokyo, and Boyle Heights have been underserved by infrequent and unreliable transit service. Car ownership rates in the central city are much lower than those in more peripheral communities (SUMC |

Mapping Shared Mobility, n.d.). These communities’ needs and the prospects for better serving

them are fundamentally different than more peripheral communities, such as those in the South Bay and San Fernando Valley – where housing density is more sparse. Flexible and low-cost shared mobility systems offer the potential to extend service across this complex landscape in a way that fixed-rail, capital intensive systems never could (Mann, 1996).

Second, the scale and polycentric nature of the city has made auto ownership an essential tool for upward mobility (Bliss, n.d.-a). Riding public transit has not been viewed as a luxury – but

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rather a mode of last resort for the city’s low income workers. This is also true of the bicycle. There is a key distinction to be drawn across both of these modes between ‘choice riders’ and ‘transit dependent riders’ (Lugo, 2018). This has produced rifts among sustainability advocates, as well as resentment that the city’s heavy focus on rail investment since 1980 reflects a greater attention towards luring the affluent out of their cars than serving the needs of low-income travelers that could never afford one (Mann, 1996).

Third, the city’s profound orientation around the automobile has had severe public health consequences for the city’s low income and non-white populations. One angle is through air pollution and smog resulting from auto-exhaust. These are exacerbated by the placement of the city in a basin that experiences pressure inversions, often producing a thick layer of smog over downtown and its environs. This has produced high rates of asthma in south and east LA

(August, 2016). Another is through obesity, and the imperative that most travelers face to spend their lives in the car. Third is physical safety; Los Angeles is a dangerous place for pedestrians and cyclists. Despite the city’s recent Vision Zero campaign to reduce pedestrian fatalities, deaths have remained stubbornly high (Nelson, 2019).

Fourth, displacement of low-income communities has been a recurrent theme throughout the city’s history. The colonization of indigenous land by Spanish settlers, the demolition of black and brown communities from West Adams to Chavez Ravine (Shatkin, 2018), the severe lack of affordable housing in the city today: in the eyes of many, these are different chapters of the same story of LA’s transportation equity history. While various empirical studies have generated inconclusive results as to the link between rail investment and gentrification (Baker & Lee, 2019a; Marion G Boarnet et al., 2018; Marlon G Boarnet, n.d.), conflict has nonetheless emerged in many communities surrounding recent new transit investments (Eastsider, n.d.; Flores, 2019; Sandoval, 2018). The discord between empirical assessment and community perception reflect that locals’ anxiety is not only about having an affordable place to live. Rather, it invokes a deeper and more painful history of being repeatedly cast aside.

Why does data governance matter for equity?

As mobility planners turn increasingly to ‘big data’ to define problems and potential solutions, the question of whether how these histories are reflected in digital systems becomes

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paramount. Scholars of ‘smart cities’ have begun to question how far into data-driven planning organizations equity goals can permeate. In a case study of six cities’ offices of innovation, Nguyen and Boundy (2017) found that local governments using big data approaches have tended to “focus on tame problems using a rational framework that promotes efficiency in government systems, raises long-standing concerns about ‘problem definition’ within government” (p. 533). Nguyen and Boundy (2017) further note that the typical decision making processes around big data tend to be top-down, favoring the voices of already advantaged populations.

Equity is far from a tame problem. It cannot be easily reduced to a rational framework; and problem definition, in particular, is a key point of contention. Therefore it is valuable to consider whether, somewhere between community engagement and project implementation, the original ‘equity objectives’ may have been lost.

Consider a typical planning workflow: within a broad project, one team of people is tasked with doing the public outreach. They receive a range of written, verbal, and graphic input from the community, and synthesize it into a set of over-arching principles and goals. They hand off these goals to individuals with technical expertise – for example, engineers who are

knowledgeable in the design of roadways, or data scientists who specialize in geographic

information systems. Technical experts tasked with implementing the over-arching goals may or may not be sensitive to community context, much less encouraged to reflect on the value

judgments embedded in their practices. Even socially aware technical experts may not lack the leeway to apply to modify the designs as a result. Indeed, the transportation industry has

historically been extremely rigid, with designers adhering to street design manuals and engineers adhering to design and modeling manuals issued by the Institute of Transportation Engineers. By the time any given project implemented, a small army of individuals, each with specialized knowledge, has interpreted and reshaped community input.

The technologically savvy (i.e., architects, engineers, and data scientists) hold ultimate decision-making power over how a project gets implemented. By deciding which variables to include in the models, how to weight them, which statistical tests to apply, and how to interpret and apply the results, this group defines the set of options that policymakers are ultimately able to choose from. However, the individuals making these decisions may or may not have been present to actually speak with the community – and therefore may not have a personal sense of the implications of their decisions. This dynamic results in part from the specialization of labor,

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which is necessary for any large-scale infrastructure project to be carried out. However, it comes at the cost of the persons who are actually building transportation systems being knowledgeable or aware of the implications of decisions that are deemed standard practice. Few other officials within a transit agency may have the technical knowledge to challenge the underlying

assumptions, once they are embedded into a standard system.

This presents a formidable barrier for meaningful community engagement. It implies that even when communities have been engaged in problem definition, data collection, and the selection of alternatives, the final result may not capture the community intent. In order for community input to have an impact, the people with technical expertise must have the awareness and resources to embed equity values in the digital infrastructures they are building. Equity planning in the 21st_{century, as such, requires attention to the way human values are translated} into digital systems.

These challenges become all the more pressing as the transportation planning field moves full speed ahead towards autonomous vehicles, and enthusiastically embraces machine learning approaches. In the planning workflow described above, there is at least an individual who mediates between the output of a model and transportation decisions. Machine learning approaches, by contrast, suggest that this middle man will be cut out altogether. This is deeply problematic – foremost because plentiful evidence exists that our current transportation system is shaped by structural inequality, which will permeate into mathematized approaches to planning (Green, 2019b; O’Neil, 2017).

The basic idea of machine learning is that computer algorithms detect patterns in training data, usually a sample of data collected on society as it currently exists. Machine learning

algorithms then generate models to describe society, predict future patterns, and these models are then used to make real life planning decisions. They are in this way self-fulfilling – meaning that the patterns embedded in the training data are especially impactful. If we are interested in

advancing equity, it is important to make sure that the training data which will ultimately be fed into machine learning models is actually capturing the variables of interest. Machine learning algorithms are not trained to reflect on whether the modelers are asking the right question in the first place, or if the training data supplied is the most useful, and what this means for equity (Noble, 2018).

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Unsupervised machine learning also creates the possibility that any equity-blind

assumptions that were embedded into the code in the first place will be main-lined directly into transportation operations (Eubanks, 2018; O’Neil, 2017). There will be no opportunity for a person to say ‘what the model is telling me is just one point of reference – but I can decide what to make of that.’ Proponents of autonomous vehicles might reasonably object that human supervision is becoming standard practice in many cases. For example, the autonomous vehicle pilot put in place in Columbus, Ohio charges a human attendant with monitoring safety, answer passengers’ questions, and generally make it a more comfortable experience (Columbus AV Pilot

“LEAPs” into Residential Service, 2020). Similarly, AV regulations in many cities are tending

towards requiring a person to continue monitoring the vehicle. But these interventions are still missing the point, by inserting a human to ensuring safety and comfort for passengers rather than hard-coded operations of the system.1

Public agencies have put many policies in place so far (e.g. mandating the distribution of bikes and scooters in disadvantaged communities a census-tract level measure of exposure to environmental hazard) – but the case studies detailed in later chapters reveal that this fairly coarse measure of ‘equity’ is insufficient to meet the needs of populations that have been excluded. Given that these geospatial indexes are used as primary tools for governing,

implementing more equitable policies would require the individuals actually touching the system – i.e. writing the code – to change the model parameters and analysis methods.

The introduction of deep neural networks – or black boxes – in transportation planning makes the need for inclusion in the process early on particularly acute. Conventional machine learning practices have permitted this to some degree; a person may write a model, choose the parameters that go into it, and task a machine with identifying the coefficients for the variables in that model that most closely capture the phenomena of interest. Deep neural networks go one step beyond conventional machine learning model, by identifying patterns. The technical

intricacies of DNNs are beyond my knowledge and scope of this paper – but they key difference is that even the researcher who calibrates the model can’t describe, precisely, the specifications used to estimate the model. An additional layer of accountability is removed; and even the data scientists that designed the model may not be able to explain, with certainty, the assumptions that the DNN used to arrive at its model. This means there is no individual whom a community

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member can approach and demand an explanation for inequitable outcomes. Whereas the Bus Riders Union litigants could directly challenge the MTA’s budgeting methodology at board meetings, the same guarantee does not hold in contexts where deep neural networks are used as a central planning tool.

When challenged on this particular point, proponents of DNNs have countered that DNN’s stunning accuracy in predicting past trends is evidence that they should be used going forward. But this amounts to conceding that the patterns we have in transportation now are desirable to re-create going forward. To be fair, machine learning holds many promising opportunities to improve public transit, lowering operations, etc. But whether these cost savings ultimately translate into improvements in the lives of riders depends on policymakers being knowledgeable about the systems that they are using, and in intentionally redirecting resources towards policies that benefit historically disadvantaged groups.

The business models undergirding shared mobility make it particularly urgent to consider this, as they rely on sophisticated matching algorithms that can be shifted in real-time, with little oversight from riders. To offer a point of reference, changes in conventional transit modes require extensive oversight processes (e.g. bus route redesign or fare increases). These take months. This slow process is in some ways a drawback -- but it at least offers the opportunity for deliberative decision-making. A model that can be calibrated, and re-calibrated in real-time weakens accountability in the long-term as the basic assumptions guiding service may change.

There is a growing chorus of voices within the computer science movement to promote FAT (fairness, accountability, and transparency). To this I would add that a focus on the data which is being in produced in the first place is a threshold issue; without variables that adequately capture, from the community’s point of view, the issues of interest, no model or algorithm can be expected to produce an outcome that is equitable or fair.

A danger also exists in the framing of machine learning as a tool to ‘solve’ urban transportation issues, which also implies an end to deliberative process. If the most powerful technological tool that we can use to improve mobility fundamentally doesn’t lend itself to this deliberation, then this deliberation needs to be shifted elsewhere. Rather than deliberation over the proper calibration of the model, the deliberation should be shifted to its inputs, interpretation, and modification over time.

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Another looming threat is that the movement towards standardization and interoperability could also lead to the erasure of the context-specific phenomena that equity-minded planners are seeking to address. One data standard to rule them all (such as MDS) poses the risk that the standard will incorporate only issues that are universal and leave out the ones that are more context specific. This approach may allow a data standard to serve the maximum number of users and produce the maximum social good (a utilitarian approach). But in so doing, it leaves out the communities at the margins whose experiences may be localized. Placing these

historically marginalized groups at the center of planning is not only an ethical imperative, but a practical one – as low income and communities of color represent the majority of transit riders (Mann 1996, Soja 2013, Greenling Institute 2019). Trip-making behavior is incredibly

individualized. dependent on context and an individual’s personal experiences and psychological make up; hence the growing interest in transportation research on urban design and behavioral psychology as approaches to understanding behavior. If transportation planning systems routinely fail to address individual needs– and only think about equity at the highest, most aggregate levels – they will ultimately underserve vast quantities of riders.

To sum up, the digital transformations underway demands that transportation planners, concerned with equity expand their focus beyond just the operations of modes and design of streets, to consider the digital systems themselves. It is vital to consider not just how decisions are made with the data provided, but what data we choose to collect and how it describes the ground truth (Ground Truth: The Social Implications of Geographic Information Systems -

Google Books, n.d.). The current discourse on the Mobility Data Specification is example of

these debates unfolding in real time - and their outcomes will shape the policies options that planners can choose from for decades to come.

The evolving digital governance of shared mobility creates an opportunity to revisit the supposed tradeoff between social equity and efficiency (Dietz & Atkinson, 2010). This is rooted in the idea that context-responsive planning is inherently resource intensive, demanding far more time and resources to meet with community members and understand the issue. Then, the

ultimate solution is not replicable elsewhere. The replicability of digital governance schemes at low cost (for example, through platforms like GitHub) enables much faster policy learning between governments experimenting with more equitable approaches to planning. Moreover, flexible standards like MDS offer the potential for both a universally legible and

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machine-readable way to govern shared mobility devices, as well as the possibility for individual cities to add in variables of local relevance. Tailoring mobility policy to local context, in other words, can be done at much lower cost – meaning that ‘equity’ is itself becoming more efficient.

One might ask: is scalability even an appropriate goal for mobility planning? What is the problem with hyper-local planning processes ruling the day? This is a fair critique, as it was a modernist impulse towards maximum speed and efficiency that produced the sprawling megacity that LADOT and Metro is now trying to transform.

But in response to climate change, speed and scale remain quite important. As a planet, we need to shift as many trips as we can away from the internal combustion engine, as quickly as we can. A hyper-focus on this scale can lead to local experiences being lost along the way; but we must take a practical approach to equity to ensure that global, intergenerational justice goals are aligned with local, distributional justice goals. As equity and justice become more central themes of the modern climate movement, it is essential that we accomplish these two goals together (Goh, 2020).

At present, a barrier to mobility equity scaling now is that there is a narrow set of universal approaches to planning transportation systems, determined in part by the terms of the federal grants that provide a majority of local project. For any municipality, designing by different terms could mean forgoing federal money and seeking out the revenue sources, this requires

developing new policies and procedures; hiring more staff; training more people. As a digital governance system, MDS offers a platform for cities to innovate at very low marginal cost in their response to community input. Through pull-requests and collaboration across cities, these equity innovations can plausibly proliferate to cities across the world. Thus MDS enables innovation – not just in tech, but in the way that policy goals are set and described. Realizing these benefits demands intention and introspection on the part of planners charged with

deploying these technologies. As John F. Kennedy cautioned at the dawn of the ‘space race’ – another moment of profound disruption -- technology has no conscience of its own (JFK RICE MOON SPEECH, n.d.).

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We must be thoughtful about how cities adopt digital technology not for technological reasons— but because the technical infrastructure undergirding the smart city will go a long way toward determining the social and political infrastructure of twenty-first-century urbanism.

- Ben Green Mobility stands at an unprecedented moment of disruption. As connected, autonomous, shared, and electric vehicles (collectively, ‘smart mobility’ systems), are being rolled out, so are new ways of governing. The interconnected nature of smart mobility offers the potential to provide affordable, high quality transportation to historically under-served communities. However, this outcome is not inevitable (Fleming, 2018). It is also important for planners to consider the network of public and private actors that govern the systems, and how they craft policy. While creating new possibilities for transparency and public participation, these networks also raise new questions of accountability and democracy. As these new technologies and

governance systems are overlaid onto cities with deep structural inequality, we must ask how they shift the balance of power.

I argue here that smart mobility systems, and the accompanying data governance structures, present planners with a series of tradeoffs, from the viewpoint of equity. No correct answer exists to these tradeoffs, and equity arguments can be made for multiple approaches. The individuals overseeing technical systems therefore get to decide what ‘equity’ means in practice. It follows that realizing a vision of mobility equity aligned with community goals depends on meaningful participation in the design, collection, and interpretation of data produced by smart mobility systems. The notion of ‘code shift’ describes this imperative.

Defining Code Shift

In an era when data are increasingly used to define and solve problems, the design,

collection, management, and interpretation of data become key determinants of policy outcomes. Planners interested in advancing mobility equity, therefore, must look past the operation of mobility services, to consider the data systems that define the basis for discussion. As will be shown in greater detail in Chapter 3, data-driven approaches to gauging transportation equity have captured only a fragment of the issues relevant to community stakeholders. In order to

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needs to be an expansion of the ideas that are captured in the databases that will serve as basis for discussions going forward – as well as an expansion of the people empowered to access and use this data. Code shift describes this philosophy.

code shift /kōd/ /SHift/

3. noun : a refocusing, by professional planners, private service providers, and

communities, on data governance as an essential domain of equity.

4. verb: to facilitate the implementation of community-defined transportation equity

strategies through inclusive data governance.

Formulated as a noun, code shift describes a refocusing on the public discourse around equity on data structures themselves. In some ways, this is already happening – organizations such as the American Civil Liberties Union and Electronic Frontiers Foundation have intervened in lawsuits regarding data privacy, each using a civil rights framing to oppose LADOT’s data collection strategies. Organizations such as the Greenlining Institute (Creger et al., 2019) and Investing in Place (Guevarra & Meaney, 2016) have begun to consider the equity implications of autonomous vehicle technology. Each of these groups recognize the paradigm shift underway, and suggest approaches more profound participation strategies – but stop short of considering how the handling of data shapes the possibilities for the changes they propose. Code shift

proposes that advocates consider, explicitly, the link between data governance and transportation equity outcomes.

It also implies that government planners should include public participation in the creation and management of data systems as a part of their broader equity initiatives. A key plank of LA Metro’s Equity Platform Framework is to ‘Establish meaningful goals around a shared definition of equity and actions to achieve those goals’ (Metro Equity Platform

Framework, 2018). For this shared definition of equity to be translated into action, it must also

be taken seriously in decision-making processes, which are increasingly informed by terabytes of digital records. Tribone (2013) observes that the presence of data, on its own, is not guaranteed to improve service outcomes in transit agencies. This implies that advancing mobility equity requires not only the collection of more data, but also on making that data accessible, useful, and relevant to marginalized groups. Because individuals interpret data through the lens of their own

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experiences (or as D’Ignazio and Klein (2018c) put it, “The data never, ever speak for themselves”), this demands collaboration and co-design on the governance of data systems.

As a verb, code shift describes the planning practice of engaging community at the level of institutional design. It means asking communities to not only react to data that transit agencies provide, but rather engaging them in the design of data systems themselves. This implies an active and intentional dismantling of the mystique around data systems; considering data scientists less as ninjas, rockstars, wizards, and unicorns, and more as janitors (D’Ignazio & Klein, 2018b). In other words, we should respect data scientists as stewards of the public good, on whom we depend to maintain the spaces we live in – but recognize that their trade is learnable by all. Much the way Justice for Janitors movement in Los Angeles demanded that historically marginalized groups have a seat at the table to shape the institutions of labor, code shift asserts that planners should provide community groups a seat at the table and tools to meaningfully participate in the shaping of ‘smart cities’ (Green, 2019a).

Code shift approaches ‘equity’ as fundamentally a language issue. Different stakeholders use the same term to talk about different constructs. For example – mobility advocates typically think about it through a lens of interconnected injustices, which extend to environmental hazards, the theft of indigenous lands, policy brutality, and housing segregation (Sheller, 2018). It would be very presumptuous to suggest that introducing a scooter or bicycle in a given city will

remediate all the issues of interest to advocates. Yet, by broadly assigning the term ‘equitable’ to these devices, planners imply as much.

The trouble is that prevailing methods of gauging ‘equity’ in transportation projects, consider only the impacts of individual transportation projects (e.g. the increase in job

accessibility as a result of adding a new bus line) – and not the pre-existing inequalities that the proposed intervention aims to amend. For their part, private mobility operators consider ‘equity’ mostly through the lens of how devices are used and deployed; not who is at the table to make decisions. These divergent formulations produce conflict the different stakeholders, which can impede the successful implementation of devices that could meet a real need.

This conflict becomes all the more vexing in the context of smart cities. Expressing ‘equity’ goals through data demands that planners and communities arrive at a definition that can be encoded in 1s and 0s. In other words, planners must translate human values into digital

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managed along the way. Like human languages, digital languages simplify our knowledge of the world into a format that is standardized, legible and scalable. Similarly, digital languages are not infallible measures of reality; rather they are the products of interpretation. Programmers must decide how best to represent the complexity of the world in a simplified format, which means that some information is left out, some related concepts are grouped together, and some are emphasized over others. The same way that the structure of human languages reflects experience of the world, structures used to organize digital information ultimately influence the integration of community-held knowledge in planning.

Opportunity arises in the fact that digital languages, like human ones, are fluid. They are redesigned and re-arranged to describe new social phenomena. In open-source programming languages like R, programmers introduce new functions, packages, and libraries to facilitate certain data analysis tasks that are frequently used. The ‘sf’ and ‘spdep’ packages, for example, enable R programmers to do sophisticated geospatial analyses with just a few lines of code. Rather than having to engage with every single data point distributed in space, write out the full mathematical equation, and consider the assumptions embedded therein, tidy packages like these enable programmers to skip straight to the geospatial analysis. Standardization of certain

functions -- e.g., computing a local Moran’s I, or the Gini Coefficient – enables the quick calculation of statistics that may be useful as part of a broader discussion about equity. The cost of this convenience is that technologists need not consider as deeply the implication of their analytical method. However, the complexity of cities remains. Thus planners must reflect on how they are using digital tools – and whether the design of those digital tools inclines them towards a particular way of thinking.

Code shift asserts that if a multifaceted issue like ‘equity’ is to be a central consideration in smart cities, planners must arrive at a shared vocabulary to describe novel phenomena. Metro acknowledges as much in its Equity Platform Framework, flagging the “need to define a

common basis for talking about and building an agenda around equity, and how to improve it” (Metro Equity Platform Framework, 2018). Indeed, there are many issues that communities consider part of the equity discussion – such as police harassment and gentrification. Some data systems capture elements of these; for example, Los Angeles publishes data on pedestrian and bicycle interactions with police officers (Vehicle and Pedestrian Stop Data 2010 to Present | Los

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assess gentrification risk (Preis et al., 2020). However, these datasets have not been introduced in spaces where planners consider how data can advance mobility equity. In order for smart

mobility systems to respond to the full range of issues impacting mobility equity, we must consider the interaction between different datasets that, collectively, form the basis for policy evaluation.

The parallel between ‘code shift’ and ‘mode shift’ is intentional, signaling that digital infrastructure (i.e., code), is integral to efforts to change travel behavior. The word ‘code’ carries multiple meanings, relating to both digital syntax which commands the actions of a machine, and legal text that governs the machinations of a body politic. Indeed, as technology occupies an expanding role in urban planning, political and digital systems must respond directly to one another. Therefore the planner interested in implementing their values in physical systems must also focus on embedding their values in digital ones.

Code shift also invokes ‘code switch,’ a social concept describing the tactic employed by people of color to change the way they speak in order to be listened to and taken seriously (Demby, 2013). Marginalized groups have often struggled to have their concerns taken seriously in transportation planning (Lugo, 2018; Mann, 1996; Sheller, 2018). In responses, marginalized groups have complained about having to shoehorn the diversity and complexity of their

experiences into technical terms that resonate with engineers and formally designated

policymakers – or as the Principles of Mobility Justice put it, “pick their battles” (Untokening 1.0

— Principles of Mobility Justice, n.d.). Code shift asserts that in a more equitable planning

process, transportation planners would be open to modifying the languages they use (digital, written, and spoken) that they use, in order to more fully integrate the issues that advocates have pointed to as significant barriers.

One fundamental question is – why do communities need to be engaged at the stage of data governance, and not just later on once data formats have been established? The data that we gather, and the ways we organize it, define the questions we can ask and answers we can

uncover. Communities hold essential knowledge that technologists need to use to decide how to organize information in databases, such that it will be most useful for making decision process. The design of object-relational databases is a non-trivial matter, as equity is concerned. Identifying "entities" -- that is, ideas to be considered as separate objects -- is an essential step in database management. All the attributes related to a particular entity -- for example, a street --

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might be stored in one table about that. A standard set of information to include in that dataset may be physical attributes of a street: its width, the presence of a bike lane, how many lanes it contains, and in which directions. This construction of a street depicts it primarily as a link in a network; holding little value apart from conveying travelers and goods.

In reality, a street serves many functions – a social space in and of itself. Embedded in each street are different sensory experiences, as well as different histories. Individuals encounter this street in different ways. For example, the width of a sidewalk or height of a curb take on special importance for a person with disabilities. Considering a more complete set of attributes to be part of the same entity within a database, could inform the design of transportation networks that meet the specialized needs of groups that have been underserved in the past.2_Meaningful participation at the earliest stages ensures that later in the planning process, the data will be available to answer the questions of interest to communities – and that when planners go to collect data about streets, they can readily view the full range of variables of interest.

Mobility equity advocates, moreover, have much to gain by shifting their attention to the way digital systems are constructed, so as to tighten broad social critiques of technological systems to specific, constructive input (Schuurman & Pratt, 2002). Geographic information systems (GIS) have been critiqued as imposing a positivist framing in the world; signaling through the graphic representation of data that the researchers are capturing a single ‘objective’ reality (Ground Truth, n.d.; Openshaw, 1997). Such a uniform reality does not exist, which has led critics of smart cities to assail data-driven approaches as tools to advance neoliberal

ideologies and the interests of businesses that develop civic technologies (Grossi & Pianezzi, 2017).

Dismissing quantitative data approaches out of hand, however, also means missing an opportunity to understand the disparate impacts on sub-groups within a larger population, the ability to track progress over time, and design transportation systems that more effectively meet the needs of all people in a city. From the technologist’s point of view, these are useful benefits no matter the context; from the social critic’s point of view, these uses are trivial (or even harmful) in a context of systemic inequality. Recalling similar disputes in the early days of GIS,

2_{What if we designed a transportation network to steer more pedestrians towards minority-owned businesses? Or to}

protect citizens with disabilities from high rates of automobile traffic? These approaches to advancing equity require more attention to individual experiences.

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Schuurman & Pratt (2002) note that “Part of the tension over positivism between researchers and critics of GIS stemmed from a general ignorance of the other’s research domain” (p. 295). A similar observation might me made about smart mobility systems, with each different group of stakeholders applying a different framing of equity.

Code shift embodies the idea that by focusing on the mechanics of civic technologies social critics can both play a more direct role in shaping the future of smart mobility, as well as identifying areas of common ground.

Case Study: The Mobility Data Specification

The opportunity for code shift will be developed through the case study of the Mobility Data Specification: an open-source data standard that has been central to Los Angeles’s

regulation of shared mobility devices. Because it is used to define and enforce legal regulations, MDS is changing not only the way planners analyze transportation system, but the way that public, private, and community stakeholders relate to one another. That MDS is hosted so publicly creates profound implications for public participation in policymaking. This shift in governance, towards openness and transparency creates both opportunities and hazards from an equity viewpoint.

As a standard hosted on GitHub, it offers a unique opportunity for the public to participate directly in the governance of smart mobility systems. The founding of the independent Open Mobility Foundation to manage MDS creates space for deliberative democracy to occur at the level at which the data standard is being built and re-defined. This deliberation early in the process is more critical than ever as artificial intelligence and machine learning approaches weaken the ability of the planner to infuse their values later on. And as MDS expands to more cities around the world, and begins to govern new technologies, there is an opportunity to embed equity in transportation planning on a profound and global scale.

MDS is a system for mobility companies to report information about the operation of bicycles and scooters, as well as for transit agencies to implement policies. It provides a schema, which detail a series of event types that describe how vehicles are being used by both users and companies, as well as the policies that are put in place. These data are structured in the Javascript Object Notation (JSON) and Geographic JSON (GeoJSON) formats, and are passed between

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mobility providers and government through three application programming interfaces (APIs). These are digital ports that enable users with an API key (i.e. a code to grant them access) to query and access data stored on the host’s servers. MDS is composed of 3 API endpoints – policy, agency, and provider – which enable public agencies and mobility service providers to share data back and forth (City of Los Angeles, n.d.).

Governance of MDS

MDS was first developed by LADOT in 2018 as a component of the One Year Dockless Mobility Pilot. The official version of the standard is hosted on GitHub – an open platform where citizens can view the most up-to-date version of the code, review changes made in the past, and propose their own modifications. It forms part of the city’s Active Management strategy, in which they pivot from being a passive responder to tech disruptions, towards an active player that defines the terms of the market to align with their policy goals (Bliss, n.d.-b; Final One-Year Dockless Permit, 2019a). Accordingly, LADOT leveraged their permitting power and the size of their city to demand that private mobility providers seeking to operate in the market link their systems directly to the city’s servers via three application programming interfaces (APIs) and submit data in LA’s preferred format.

The original standard was developed at the impetus of LADOT, with assistance from the consulting firm Ellis & Associates. Ellis and Associates developed a strategic plan that suggested ‘standardization of data practices to enable more effective regulation. In turn, a new industry of firms has grown up to assist cities with the handling and managing of MDS data. Ride Report, for example, offers user-friendly interfaces for private mobility operators and cities alike to understand ridership patterns in scooters (Ride Report, n.d.). Lacuna – which acquired Ellis and Associates – is another start up that was created to help guide cities’ implementation of the standard (Hawkins, 2019). Thus, the governance of MDS entails not just the collection of data by a public agency, but an entire ecosystem of private actors that mediate between citizens and government. The implication is that, while citizens contribute data about their personal

movements through public space, the power to view, interpret, and act upon that data is current far from public reach. Moreover, the technical complexity of managing mobility systems makes it difficult to understand precisely which actors are involved, what their respective roles are, and who the most powerful decisionmakers are.

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This complexity raises questions about the naming of the Open Mobility Foundation (OMF), an independent non-profit which assumed full responsibility for managing the MDS standard in April 2019. OMF currently has only two employees, and primarily plays a coordinating role between each of these actors. The Open Mobility Foundation (OMF) was founded in order to “develop and promote technology used in commercial products that either use the right-of-way or that help government entities manage the public right-of-way.” OMF is governed by 26 municipalities (‘Public Members’), which range in both size and geography from megacities like Los Angeles, Chicago, and New York, to mid-size central like Louisville,

Columbus, and Washington DCs. To small cities like San José, California and international cities like Bogota Colombia and Dublin, Ireland. OMF also includes as ‘Private Members’ several private tech firms, including Microsoft, Bird, Waymo and Lacuna. OMF is one of many projects supported by OASIS-Open, a consortium of non-profits engaged in standardization for software. As such – not only are there many actors contributing to the design and implementation of MDS, but also many cities and private companies standing at the ready to implement it. Decisions made about how MDS is designed and used are likely to reverberate around the world.

This ecosystem of powerful actors makes public participation all the more important for advancing mobility equity. MDS is updated through two working groups: Provider Services and City Services. The Provider services group is convened to manage the Provider API. The City Services groups manages the two APIs implemented by cities (agency and policy) (City of Los Angeles, n.d.). Each group is composed mostly of software engineers from the MSPs, and their counterparts in city-government. In the bi-weekly conference calls, the groups discuss recent pull requests and issues, as well as set longer term goals. These working groups are organized

informally, holding bi-weekly 1-hour conference calls that are open to the public, and

communicating through a Google Groups mailing list

(Openmobilityfoundation/Mobility-Data-Specification, 2018/2020). Individuals can also be designated as official contributors to the

MDS, on the condition that OMF owns the intellectual property of any suggestions that are made. In this respect, MDS offers a degree of transparency that other digital standards may not. However, the fact that a standard which can have such a global and long-lasting impact is managed so informally raises questions about whether the relevant institutions are prepared to ensure equitable outcomes.

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A Different Kind of Open Data

Open data is not new; what is novel about MDS is the way it changes the relationship between public and private actors. Beyond just defining a set of parameters and syntax for mobility providers to describe where they are deploying their scooters, LA’s MDS strategy constitutes a reversal of the power dynamics between the city and providers. When dockless mobility providers first appeared in Los Angeles 2017, they benefited from a regulatory vacuum that allowed them to design a new market. By LADOT’s own admission, they were caught ‘flat footed’ (Mayersohn, 2020). The city’s existing data systems lacked a classification for ‘scooter,’ nor rules in place to decide what behavior was permissible. The creation of MDS represents an exemplifies the city going on offense; devising its own data standard to suit city goals, and forcing providers to comply (Bliss, n.d.-b).

Open data standards such as the General Transit Feed Specification (GTFS) and the Global Bicycle Feed Specification have been used in mobility planning for a long-time.

Considering these precedents reveals new possibilities in MDS. GTFS is an open data standard that cities use to publish public transit schedules (General Transit Feed Specification, n.d.). It came about in 2005, when TriMet – the transit agency for Portland Oregon, sought to help travelers, who did not already know that city, more easily access their routes and schedules. TriMet collaborated with Google to develop a standard set of comma separated values (CSVs) describing the geometry, schedule, and features of the routes. When TriMet first released this standardized dataset (known at the time as the Google Transit Feed Specification) through an early version of the Google Transit platform, the service was immensely among users and drew the attention of transit agencies in other cities. Simplicity was key to its success; though at first critiqued as low-tech because it shared sensitive data in CSV format – a relatively old-school approach – this also enabled many different users, both professionals and common citizens to interact with the data and make it their own, using any editor (e.g. Microsoft Excel) of their choice. From the get-go, public participation has been key to the development of tech standards (Pioneering Open Data Standards: The GTFS Story, n.d.).

GTFS has continued to be developed over time, with various new features being added to the core set of information, including a real-time feature. Now, GTFS is near universally used as a standard approach for transit agencies to communicate their schedules and for navigation apps to relay this information. The standard was renamed the General Transit Feed Specification in 2010

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to reflect its wide usage. It has also given way to other standards, such as the Global Bikeshare Feed Specification (GBFS), which undergirds docked bikeshare systems and facilitates their representation in smartphone apps like the Transit App. These standards provide information on station location, the number of bikes and docks available at a given time, how many are broken, and records where bikes are picked up and dropped off.

While MDS was heavily influenced by GTFS, this earlier standard implies a different relationship between private and public sectors than MDS. First, it was developed by a private company, Google, to support the operations of a public system. While Google benefited from sharing public transit information on their platform, they were not offering a competing mobility service. GTFS drives more riders to public transit by making the public system more legible. MDS, by contrast, was developed by LADOT with help from consultants as a way to wrangle private mobility providers operating in their jurisdiction. Thus it acts as a regulatory tool, whereas GTFS communicates public transit operation to the public.

Second, the original GTFS describes what planners intend for the transit system will do, if everything goes as planned. But research has shown the actual spatiotemporal accessibility provided by transit systems is quite different from that implied by GTFS. This can have important implications for public perception, as riders’ actual experience of unreliable transit services may differ significantly from the experience implied by data (Stewart & Zegras, 2019). Real-time GTFS feeds offer a closer look at what is actually happening – but only offer users snapshots of where vehicles are located at a given moment. MDS builds on real-time GTFS, by both offering real-time location data, as well as creating the possibility for more detailed insights into business decisions. Event types include data on where bikes and scooters were dropped off and picked up, which ones were taken off line, when, and why. MDS therefore offers the possibility of users observing how decisions made by private mobility providers translate into outcomes on the ground.

Third, MDS traces individual movements, whereas real-time GTFS shows only the movement of buses along routes. Given that dockless scooters and bikes enable point-to-point travel, this makes MDS data a far more sensitive data stream. It can be used to trace individuals’ place of work, home, in a degree of detail that GTFS and GBFS do not permit. While raising serious equity concerns about data privacy and civil rights, this granularity portends new possibilities for travel demand modeling. Modeling individual movement along fixed rail and

Code shift : data, governance, and equity in Los Angeles's shared mobility pilots