Building a Brave New World in Toronto’s Quayside

What would the smartest city in the world look like? And how would we even begin to plan, build, and maintain it? This is exactly what Alphabet-owned Sidewalk Labs is trying to figure out with their latest Toronto waterfront development called Quayside. With the help of machine learning, and other enabling technologies, Sidewalk Labs is “reimagining cities to improve quality of life.”

Conceptual illustration of Quayside. Sidewalk Toronto

 

What would the smartest city in the world look like? And how would we even begin to plan, build, and maintain it? This is exactly what Alphabet-owned Sidewalk Labs is trying to figure out with their latest Toronto waterfront development called Quayside. With the help of machine learning, and other enabling technologies, Sidewalk Labs is “reimagining cities to improve quality of life.” (Sidewalk Labs, 2018)

Beginning in the mid 1900’s, city planners began leveraging systematic observational data of human behavior to make planning decisions and inform city policy. (Depts.washington.edu, 2018) The insights garnered through this observation have been incredibly valuable in creating meaningful public spaces, managing traffic flows, and developing overall city planning solutions to support more effective and efficient interactions between people. Over the last 70+ years, however, there’s been little innovation in this space. While, today, we have pervasive behavioral monitoring technology throughout our cities such as infrared cameras, motion sensors, and geo-tracking, until recently it has been incredibly complicated to distill significant insights from the data collected by these technologies.

Sidewalk Labs is attempting to take a more calculated approach to solving city planning problems as they look to predict the societal impacts of design choices, and, ultimately, build cities that help residents live more connected and fulfilling lives. Sidewalk Labs is using the same theoretical components developed in the mid 1900’s around using observable human behavior to influence urban planning choices, but is supplanting the role of human observer with machine learning. The company’s charter for Quayside to “create a place that deployed emerging technology and people-first design innovations to address the challenges that face growing cities” (Sidewalktoronto.ca, 2018) is no small task, but one Sidewalk Labs has developed a robust framework to try and address.

The company has what they call a “digital layer” made up of four components; a city-wide distribution network of sensors, a map collecting location-based information about the infrastructure and resources, a personalized account portal for residents to access services, and a machine-learning powered model simulating various “what if” scenarios for city operations and long-term planning. Arguably the most complicated element of this digital layer lies in the machine learning component, because its success relies upon accurately predicting typically unpredictable customer behavior.

Sidewalks Labs is attempting to solve this issue beginning, in part, in the more straightforward and behaviorally consistent space of human travel modeling. The Model Lab team develops high-fidelity predictive models by creating a “synthetic population” statistically representative of a true population, training machine learning models to identify and eventually understand behavioral patterns, and then generate predictions for each person in the model as well as predictions around aggregate behavior for the synthetic population. Theories around human travel preferences, such as one which posits “travelers consider every minute waiting for a bus about twice as annoying as every minute riding on a bus,” (Sidewalk Labs, 2018) are tested and validated in these simulations, and the results of which grow the model’s so-called “intelligence” and accuracy. (Stories.dask.org, 2018) The company plans to use similar models to inform ideas related to land use, transportation, and government processes. Running these simulations is incredibly complex, but, as machine learning capabilities advance, they can be used to tackle more challenging urban planning questions around sustainability, mobility, safety and wellbeing. (Sidewalk Labs, 2018)

There are innumerable inputs that could result in vastly different results, which is where the true power of machine learning comes in. The technology has the capability to rapidly model out these scenarios with a reasonable degree of accuracy at a frequency humans alone never could. The more data the machine receives the more accurately complex situations can be modeled, considered, and ultimately predicted. But, while the outputs of this modeling are important, the greatest impact will come from a human analysis of the results to help account for a level of human-centered understanding a machine may never be able to reach. This balance between machine thinking and human analysis is a tricky one to strike, but in order to build the cities of the future they envision, Sidewalk Labs will need to master it.

While the opportunities Sidewalk Labs are exploring in this space are immensely exciting, there are two major questions I want to pose to the audience as we think about the future of machine-enabled city planning. First, as the physical world becomes more digital, how should we protect customer identity and data in smart cities? Second, how does technology change the culture of a city, and what are the negative consequences to consider in thinking about the impact on behavioral norms and interactions?

 

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Bibliography

Sidewalktoronto.ca. (2018). [online] Available at: https://sidewalktoronto.ca/wp-content/uploads/2017/10/Sidewalk-Labs-Vision-Sections-of-RFP-Submission.pdf [Accessed 10 Nov. 2018].

Depts.washington.edu. (2018). [online] Available at: https://depts.washington.edu/dmgftp/publications/pdfs/mouse_class/chapter2.pdf [Accessed 10 Nov. 2018].

Bond, M. (2018). The hidden ways that architecture affects how you feel. [online] Bbc.com. Available at: http://www.bbc.com/future/story/20170605-the-psychology-behind-your-citys-design [Accessed 8 Nov. 2018].

Garfield, L. (2018). Google’s parent company just reached an agreement with Toronto to plan a $50 million high-tech neighborhood. [online] Business Insider. Available at: https://www.businessinsider.com/google-sidewalk-labs-toronto-neighborhood-2018-7 [Accessed 3 Nov. 2018].

Sidewalk Labs. [online] Available at: https://www.sidewalklabs.com/ [Accessed 1 Nov. 2018].

Sidewalk Labs. (2018). Sidewalk Labs | A key to democratizing urban solutions is building better models. [online] Available at: https://www.sidewalklabs.com/blog/a-key-to-democratizing-urban-solutions-is-building-better-models/ [Accessed 1 Nov. 2018].

Stories.dask.org. (2018). Sidewalk Labs: Civic Modeling — Dask Stories documentation. [online] Available at: http://stories.dask.org/en/latest/sidewalk-labs.html [Accessed 1 Nov. 2018].

 

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14 thoughts on “Building a Brave New World in Toronto’s Quayside

  1. This was very fascinating, I didn’t realize Alphabet was working so heavily on smart cities. While I agree with the notion of high-powered machine learning cities, we can’t dismiss the fact that there is still limitations on our current quantitative computing power. I don’t think we’ll be able to truly realize the capability of machine learning in this space until we solve the problem of quantum computing.

    1. 100%. The technology, particularly in this application, is in its infancy. Quantum computing is an interesting space and certainly would expedite a lot of the necessary progress machine learning needs to make to process the endless prediction modeling variables, but I still think 1. we currently have viable machine learning capabilities to help inform less complex and unpredictable human behavior and situational outcomes that have been instrumental in expansive fields spanning from predicting loan defaults to understanding consumer voting behavior and 2. quantum computing can help supplement human reason but I have a hard time conceiving of a future where machine learning completely replaces the value/insights derived from human analysis particularly in behavioral outcomes that are unpredictable and non-linear.

  2. While extremely interesting and absolutely closer to the direction humanity needs to head in to solve the problems of the present and future, I wonder about the efficacy of problem solving via machine learning in an environment that is as static as an established city. The efforts could potentially improve the city as it grows, but I think the real impact of this sort of study would be much better realized when the gathered knowledge is applied to the construction or overhaul of a new city.

    1. Cameron, really great point, and something a lot of smart city designers are grappling with right now. In the broader context of a connected city (enabled by technologies well beyond machine learning), architects, designers, city planners, consultants, and technologists are trying to figure out ways to make structures, buildings, services, layouts, and supporting infrastructure more adaptable. Literally they are trying to design modular-type cities where the physical attributes can change and adapt based on needs, preferences etc. One of the most basic ways to think about this is how Tesla has revolutionized cars– your entire driving experience can change based on the software downloaded to the car. While the car’s physical attributes are for the most part unchanged, the result of your driving experience can be vastly different when new updates are pushed out. Connected cities can function much in the same way. The much more challenging topic area is with physical objects (i.e. buildings), but engineers have already designed materials capable of changing their atomic structure (i.e. capable of going from rigid to malleable). Structural innovations as such could completely revolutionize the way cities are built, operated, and maintained, but that future is still a ways off. The most basic way we have to address physical adaptability currently is with modular design (i.e. think about a bunch of blocks that can fit together/move around to make different shapes). People in this space are actively exploring these options and the results are fascinating.
      Really great point to bring up, and incredibly excited to see how Sidewalk Labs and others continue to think about these issues.

  3. Chrissy- love that you covered Sidewalk Labs’ work in Toronto. Your question on how technology is changing the culture of a city is one that applies not only to Quayside, but also to many other cities that are experiencing the convergence of multiple technologies and how they translate onto the physical landscape. For instance, when/if autonomous vehicles are rolled out in large cities like Toronto and New York, traffic patterns and consequently human travel patterns may change. While this could act as a positive feed into SWL’s machine learning around travel patterns, is it possible that feeding multiple inputs of technological change could cloud the machine learning process?
    On potential negative consequences- as humans become increasingly reliant on technology, it will be interesting to see how lazy we become as a population and what we will do when said technology temporarily fails.

    1. Really great point about skewing the data with multiple inputs facilitated through tech changes, and definitely worth considering. Re your point about technology making us lazy, this is something I personally worry about regularly. I always have flashbacks to the movie “Wall-e”, if you’ve seen it, and it makes me immensely disconcerted. My personal belief is that we have an obligation through channels like urban design (in this space specifically) to create spaces that not just encourage but necessitate things like physical activity and societal connections. But particularly with the pace of technological advancement, and its mass application, I think we’re running a huge risk as a society to fall victim to such “laziness”.

  4. I find this article incredibly interesting because it opens up a wide range of possibilities for what it means to change a city, and its implications. After working on a study last year on autonomous vehicles in Toronto, I realize that opening up a city to new technology does not just mean an overhaul of a city, but also an overhaul of different parts of what creates an urban environment (which leads to gradual change over time). In my opinion, this is incredibly important as we start exposing people to the positive and negative consequences of technological overhaul – especially to your question on potential negative consequences. In our study’s case, this was mainly the increase in short term unemployment and unused spaces (e.g. parking lots), which posed a difficult problem to the government of Toronto, especially in some areas which may already be struggling to improve economic mobility.

    1. Really interesting points– I’d love to discuss more! I’ve done a bit of research around the unintended consequences of civic design, in particular, and the results are really mixed. Sometimes spaces are used in a an unforeseen way and it results in a positive change, but other times it can stimulate crime, community disengagement etc.

  5. Hey Chrissy! great article and very interesting topic, too. I have some experience at this and I have seen the power of implementing big data and machine learning to improve policy making.

    However, I think that the problem that many (if not most) governments face is that of having internal mechanisms to engange in those kind of efforts. For instance, in many countries, a public officer purchasing a product for which there is only one supplier might (and will) be prosecuted.

    I really wonder how technology can help in those cases. Blockchain has a great potential to add trustworthiness to the system, replacing bureaucratic controls by smarter and more modern techonologies, but there many “big if’s” witch which officers could not deal yet.

    1. Appreciate the insight! I think, in part because of those reasons, we’re seeing a lot of this innovation and traction happening right now with private companies. Obviously with urban development it becomes a little more complicated as there is a certain level of government interaction, ownership, and approvals that are necessitated, but a least most of the initial planning is coming from less restricted bodies. Blockchain certainly adds a layer of trust to information transaction as well as a very intense layer of complication. Getting a few players on a network who participate in simple transactions is difficult enough, but it’s going to take quite a lot of tech advancements and incentivization to get players involved across the city planning value chain onboard. We’re seeing some governments and other involved parties begin this process with title/land registry, but the number of potential stakeholders in this example far surpass that.

  6. The sharing of data for convenience is one of the great trade-offs our generation will face – unfortunately, something that has received little consideration from users of various platforms thus far. Complex user agreements prove too much of a hurdle to be bothered by. I feel there is work the legal industry needs to do here by finding the tricky balance between usability of agreements, yet enough detail to cover all elements of the interaction. In terms of data protection, encryption is going to become more important. Users have to become more willing to pay for encryption services such as PIA VPN. Furthermore, there are interesting blockchain applications that are debated around the world that will allow users to decide on what data they share with who. As big as an industry as all these amazing innovations are, the protection around data will certainly rival that in terms of business opportunities.

    1. Fantastic point and 100% agree. We sign our rights away to so much of our personal information without even thinking twice about it. Blockchain has great application here, but privacy is not entirely solved with the technology (+ think a network of meaningful size in this space is still a ways off). Would love to talk more about this.

  7. Fantastic analysis, Chrissy! As for your question on how we can protect consumer identity and data and smart city technology proliferates (which I believe it most certainly will), there’s been a lot of development going on around using the Blockchain and decentralized networks to connect people to each other and to government services in a smart city, in both an anonymous (at least peer to peer) and extremely secure way. One example of how I envision this working is if I’m walking to my car (driverless, of course) to leave a parking spot, you could get a notification that my parking spot was going to be free in a couple minutes (while I’m still walking there), and your car would be rerouted to take that spot immediately after I left. Maximizes revenue for the city, quick and easy for us, and completely secure. I’m really excited to see the future of smart cities!

    Here’s an interesting read on the blockchain for smart cities: https://www.pwc.in/assets/pdfs/publications/2018/blockchain-the-next-innovation-to-make-our-cities-smarter.pdf

  8. Great analysis on a really interesting topic! I think your question about how this application of technology potentially changes the culture of a city is paramount – especially as we consider its application in restructuring an existing city versus applying it to a newly planned community. As redevelopers and city governments grapple with making once vibrant cities work again, this technology could present a significant opportunity to optimize infrastructure and community safety projects. Beyond that, there are major implications to physical and spacial design – literally how cities will look and function in the future. This application could help to combat some of the impact of gentrification and redevelopment that has pushed entire groups of citizens to the margins of some urban areas and disrupted long-established networks in some cities. Historically, these groups are left to carve out new areas most commonly cut off from public transportation, etc. If smart cites can start to predict movement patterns and account for changes in socioeconomics within different city neighborhoods, local community governments might actually be able to plan ahead for migration shifts that have historically left entire citizen populations forced to uproot their lives in search of more affordable housing, access to work, etc.

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