{"id":35375,"date":"2018-11-13T19:03:20","date_gmt":"2018-11-14T00:03:20","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/keeping-nyc-moving-how-machine-learning-can-lead-to-sustainable-and-mobile-cities\/"},"modified":"2018-11-13T19:03:20","modified_gmt":"2018-11-14T00:03:20","slug":"keeping-nyc-moving-how-machine-learning-can-lead-to-sustainable-and-mobile-cities","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/keeping-nyc-moving-how-machine-learning-can-lead-to-sustainable-and-mobile-cities\/","title":{"rendered":"Keeping NYC Moving: How Machine Learning Can Lead to Sustainable and Mobile Cities"},"content":{"rendered":"

Currently 54% of the world\u2019s population lives in cities and this is expected to increase to 66% by 2050.[1]<\/a> With all these new dwellers, innovation is not just needed but expected, particularly in mobility. This simple but challenging task is executed in a variety of ways depending on city size, population and infrastructure.\u00a0 New York City, constrained by an extensive underground subway and utilities network as well as predetermined city limits, has already begun leveraging machine learning to enhance its mobility networks.<\/p>\n

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New York City is comprised of 5 counties connected by over 6,000 miles of streets and highways, 12,000 miles of sidewalk, 794 bridges and tunnels plus ferries.[2]<\/a> The New York City Department of Transportation (DOT), comprised of over 5000 people and an annual budget of 900 million, is responsible for maintaining and enhancing this network, serving over 9 million New Yorkers and 60 million annual visitors.\u00a0 With a population that large and growing the DOT needs a way to plan for a sustainable future.<\/p>\n

In an effort to serve and secure this large population DOT is investing in data collection, data scientist and big data to algorithmically plan for mobility patterns. New York City has already started with a few innovative ways to collect data, such as metered parking apps, Citi bikes, and smart traffic control systems. Very new to New York is the ability to pay for parking via mobile device instead of using the traditional meter. The analytics collected are applied to a predictive algorithm to allow city planners to understand the ebbs and flows of traffic, who is driving into the city, where traffic congestion is increasing as well as predict the need for future capital expenditures like road repavement.\u00a0 Additionally, the app is also used to help drivers find parking spots that are available, thus reducing idle time and carbon emissions.<\/p>\n

The Citi bikes initiative, a docked bike-sharing program, when piloted and pitched was heavily focused on providing a healthy and environmentally friendly alternative to the over crowded subway and environmentally unfriendly cabs. Furthermore, it provides key insights into traffic flow, where riders ride as well as what days of the week are most rides taken. Understanding that thousands of people daily are crossing Central Park by bike or commuting over the Williamsburg Bridge, ensures that DOT can make bikes available to communities that use them, prioritize the creation of bike lanes, as well as plan for space allocation and large-scale capital projects[3]<\/a>. As the city is in the earliest stages around hiring data scientist, they have made large scale data sets available to the public.\u00a0 The city can then benefit from private business research conducted by students and businesses.<\/a>\"\"<\/a>[4]<\/a><\/p>\n

An additional and truly innovative data collector is the smart traffic signal systems. Previously traffic cameras collected speed violations for local agencies to monitor, analyze and ticket violators over the mail. \u00a0Yet under new smart systems, the current traffic cameras in New York have sensors that are able to collect data on the number of cars and trucks that pass through an intersection and any current delays. It then communicates in real-time, allowing the entire system to react, diverting traffic from congested areas, and timing lights to reduce wait time and therefore reduce emissions.[5]<\/a> As better technology becomes available, such as the adaptive traffic lights described in Sidewalk Lab\u2019s City of the Future Podcast \u201cAdaptive Traffic Lights,\u201d[6]<\/a> lights armed with cameras will be able to not just measure vehicular traffic but also bikes and pedestrians in real-time and decide who gets prioritized at the intersection.<\/p>\n

While these are three very different ways to leverage machine learning to solve the issue of mobility, there are still a few additional steps that the city can take. The city must increase its training for current employees so that the can better leverage the data coming from these applications and move away from relying on partnerships to parse the date.\u00a0 Watchdogs have already critiqued the city for the infringement on privacy.\u00a0 Another step that will be important is finding ways to feed the algorithm data in regard to those that lie outside of the scope of these programs.\u00a0 Citibikes are not available across all parts of the city and the lack of data on these individuals could leave members of vulnerable communities out of future mobility plans.<\/p>\n

There is no doubt that these new innovations in mobility will be needed to keep up with the growing population. Additional considerations are: (1) How do we ensured this data is not used to track citizens and that privacy is respected? (2) How do we ensure equitable access to city resources? (3) Ultimately, who do we prioritize at the intersection?<\/p>\n

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[1]<\/a>\u00a0Un.org. (2018).\u00a0World\u2019s population increasingly urban with more than half living in urban areas | UN DESA | United Nations Department of Economic and Social Affairs<\/i>. [online] Available at: http:\/\/www.un.org\/en\/development\/desa\/news\/population\/world-urbanization-prospects-2014.html [Accessed 13 Nov. 2018].<\/p>\n

[2]<\/a>\u00a0Trottenberg, P., Jarrin, J., Forgione, M., Gallo, E., Damashek, P., Replogle, M., Browne, C., Stroughter, J., Schachter, C., Gastel, S., Orlando, G., Heyward, L., DeSimone, J., Benson, J., Beaton, E., Lopez, N., Bray, K., Jr., E., Garcia, N. and Cocola, T. (2018).\u00a0NYC DOT – About DOT<\/i>. [online] Nyc.gov. Available at: http:\/\/www.nyc.gov\/html\/dot\/html\/about\/about.shtml [Accessed 5 Nov. 2018].<\/p>\n

[3]<\/a>\u00a0Citi Bike NYC. (2018).\u00a0Citi Bike System Data | Citi Bike NYC<\/i>. [online] Available at: https:\/\/www.citibikenyc.com\/system-data [Accessed 5 Nov. 2018].<\/p>\n

[4]<\/a>\u00a0Data-Smart City Solutions. (2018).\u00a0Map Monday: NYC Citi Bike Visualization<\/i>. [online] Available at: https:\/\/datasmart.ash.harvard.edu\/news\/article\/map-monday-nyc-citi-bike-visualization-1180 [Accessed 13 Nov. 2018].<\/p>\n

[5]<\/a>\u00a0Caitlin, B. (2018).\u00a0Business Wire<\/i>. [online] Businesswire.com. Available at: https:\/\/www.businesswire.com\/news\/home\/20110927005530\/en\/New-York-City-Launches-Nation%E2%80%99s-Sophisticated-Active [Accessed 12 Nov. 2018].<\/p>\n

[6]<\/a>\u00a0Sidewalk Labs (2018).\u00a0Adaptive Traffic Lights<\/i>. [podcast] City of the Future. Available at: https:\/\/itunes.apple.com\/us\/podcast\/city-of-the-future\/id1353905337?mt=2 [Accessed 13 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"

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