Walking & Biking: High tech understanding of low tech solutions

by Apr 25, 2013Infrastructure, Mobility

Frank Teng

Frank Teng is a current MBA in Sustainable Management student at Presidio Graduate School in San Francisco and is on the board of Sustainable Silicon Valley. He works with Jones Lang LaSalle, a global real estate services firm, to manage global energy and sustainability programs for corporate clients in the technology and financial services sectors. Please note: Frank's views are his own and do not necessarily reflect the views of his employer.


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Sometimes smart cities have simple solutions at their disposal – if they just view their data slightly differently. Walk Score recently released their updated rankings of bikeable, walkable, and transit-oriented cities:

  • 10 most bikeable large U.S. & Canadian cities (>200,000 people). The data on bike lanes, hilliness, and connectivity are all easily shown with elegant “heat maps”. An interesting inclusion in their scoring methodology is a “bicycle mode share” metric, which tries to capture the social network effect of biking. Rather than relying only on data about the built environment, the metric also attempts to measure actual cycling activity, often showing that there are “hot spots” in certain neighborhoods of cities aside from where the bike lane networks are.
  • 50 most walkable largest U.S cities, as well as Canada and Australia. This ranking relies on an algorithm that gives higher weighting to proximity to amenities within .25 miles and zero weighting to amenities further than one mile. This metric is used by the real estate industry in promoting walkable, livable neighborhoods.
  • 25 top-ranked public transit systems in the largest U.S. cities, measured based on “usefulness” of routes nearby, such as distance to the nearest stop, frequency, and type. Unlike the bike score, they don’t take into consideration riders’ satisfaction or effectiveness of the transit.

It’s hard to argue with Walk Score’s main conclusions on the benefits of walkability: residents average 6-10 pounds lighter in a walkable neighborhood, property values can be $600-$3,000 higher with each Walk Score point (and reduce the second largest household expense, your car), as well as the study that for every 10 minutes spent in a daily car commute, time spent in community activities falls by 10%. You can now even search hotels by proximity and travel time to the locations you’re planning on visiting.

Walk Score data is even being used now in urban planning using such metrics as average block length and intersection density. This is probably an improvement considering that the Project for Public Spaces calculated the Walkscore for every state’s Department of Transportation headquarters, and came up with an average 67 on a scale of 0-100. See the PDF list here.

Another clever use of data is the City of Hoboken, NJ, announcing a combined bike rental-and-sharing program that will greatly reduce costs of operating the system. Social Bicycles puts a lock and GPS on the bike so it can be locked to traditional bike racks and while serving to track where bikes travel at what time, thus gathering large amounts of data data to help guide investments in improving effectiveness and safety of the system. Now they just need an integrated payment system like the transit pass proposed in Sao Paulo’s 150,000 bike-sharing system.

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