The pandemic has fundamentally changed our perception of how we can live, work, and move. We’ve figured out how to get goods and services without jumping in the car. We’ve learned that all sorts of jobs can be done from home offices. And we’ve learned that people like, and want, to walk and bike as part of their daily journey. Cleaner air, quieter neighborhoods, and healthier residents can be among the positive outcomes of the crisis for cities that were on their heels with traffic and congestion before. Smarter mobility can help retain these benefits.
Advanced communications networks pave the way for data mining and real-time crowdsourcing across social media platforms. For example, StreetLight Data, based in San Francisco, combines Big Data with transportation knowledge to enable smarter mobility. In Columbus, Ohio, the company has identified a link between transportation issues and infant mortality rates, noting that low-income neighborhoods often do not have easy access to health care facilities, and by using transportation data, the city can increase accessibility and reduce mortality rates.
OurStreets origins are rooted in capturing latent sentiment on social media and converting it to standardized data. It all started in July 2018, when OurStreets co-founder, Daniel Schep, was inspired by the #bikeDC community tweeting photos of cars blocking bike lanes, and built the @HowsMyDrivingDC Twitter bot. The bot used license plate info to produce a screenshot of the vehicle’s outstanding citations from the DC DMV website.
Fast forward to March 2020, and D.C. Department of Public Works asking if we could repurpose OurStreets to crowdsource the availability of essential supplies during the COVID-19 crisis. Knowing how quickly we needed to move in order to be effective, we set out to make a new OurStreets functionality viable nationwide.
We encourage public sector partners to think about data monetization as a spectrum of opportunities. On one end, there’s indirect monetization, which refers to the obvious idea of getting more value from data by doing more with what already exists. That could mean putting data in a more accessible form or location; sharing it across departments more effectively; or mining it more deeply to identify potential operational insights, anomalies, or efficiencies.
On the other end of the spectrum is the idea of direct monetization, meaning new, incremental revenue flowing directly to the city in exchange for the rental, purchase, or limited use of the city’s data. This is approach requires some focus and a proactive sales effort, but can deliver attractive, meaningful revenue streams.
In the middle of the spectrum is what we think of as the Hybrid opportunity. This is often where cities are most comfortable getting started, since its initial focus is on ensuring that the municipality is getting fair value for the time, effort, and costs of the city’s current efforts supplying data to other entities.
There are already more than 60 COVID-19 vaccines in the works. When interconnected individuals with a common goal pool knowledge and share their assets, we experience unparalleled advances. Data fluency is foundational to societal and civic engagement. It can invigorate constituencies and shift systemic power dynamics. At a time when we trust fewer entities to watch our backs and we can become crippled by fear and powerlessness, data fluency can help us find and activate opportunity narratives.
The prevalence of data in our lives represents the need to repeatedly evaluate trade-offs. Narratives have power, as fellow management consultant John Hagel reminds us: “every successful social movement in history has been driven at its core by a narrative that drove people to do amazing things.” Powerful narratives can drive us to act or prevent us from taking action via distraction or disinformation. Predictive analytics are being employed across many sectors, often without our knowledge and sometimes in violation of laws. In order to exercise agency, we need to understand who controls the narratives coloring our daily realities.
Social distancing is becoming the new normal, at least for those of us who are heeding the Center for Disease Control’s warnings and guidelines. But if you don’t have reliable, high-speed broadband, it is impossible to engage in what is now the world’s largest telecommunity. As many schools and universities around the world (including those of my kids) are shut down, these institutions are optimistically converting to online and digital learning. However, with our current broadband layout, this movement will certainly leave many Americans behind.
Shared mobility services have been proposed as a solution to urban congestion. When Uber and Lyft launched a decade ago, proponents of this model of peer-to-peer “ride sharing” claimed it would revolutionize public transportation to the point of replacing it. Opponents of a 2016 ballot measure to fund transit projects in Detroit wrote, “The proposal spends billions on old transit tech like buses and rail while other cities are contracting out transit services to Uber, Lyft, Chariot and others that provide door-to-door service at substantial savings.”
In the meantime, we’ve learned that peer-to-peer ride sharing services, better called ride-hailing services since they primarily function as taxis carrying individual passengers, have made traffic 180 percent worse in some cities. They have over-supplied the market with vehicles that are empty most of the time, on average adding 2.8 miles of traffic for every mile they carry passengers.
Shifting to a high-tech mobility future is challenging transportation experts to think in a different way. It used to be the car was the common thread for all this data, but we are now making room for so many new modes, and new ways to gather analytics for those modes.
We’re at the point where we have plenty of data, now it’s time to start understanding these issues and how they interact. Mobility experts have created measurement tools, but not as much thought about how they all come together for the bigger picture.
Big Data is helping integrate these data flows, making sense of disparate sensors and creating a single-source “dashboard” that gives cities a whole new level of insight into the modes on their streets.
Improved understanding about local air quality can support significant policy changes and targeted incentives, including electric fleet conversions for particular transit routes, the provision of emission-control technologies or alternative routes for heavy duty trucks, targeted fuel-switching efforts for home heating in heavily impacted communities, or the enactment of new regulations for specific industrial operations. We can also use data about localized air pollution exposures to study health outcomes under specific environmental conditions. With the wealth of these new, localized data on air quality, supported by low-cost sensor technology, we can design the policies and deploy clean energy strategies that truly empower local communities and protect public health.
The growth of smart cities – projected to increase fourfold by 2025 – will continue. Unfortunately, at best, only a few cities have the skilled staff needed to address these new risks and cybersecurity challenges. Hence, the onus is increasingly on city administrators, technology providers, and even community leaders to take on a steep learning curve together, and better understand how cybersecurity fits into making cities safe and secure. The question remains – how do we get there?
The use platform provides information on how to develop and implement approaches in response to complex urban issues in a local context. Each of the case studies offers a summary of a project, program or policy, including challenges, lessons learned, impacts and an assessment of the transferability potential to another location. The use platform is free and accessible to everyone who shares an interest in urban sustainability. Search our database, join the community, and upload your project.