A neckdown is a traffic calming strategy: narrowing the width of a street at an intersection to slow vehicles. A sneckdown is a neckdown created by untrod or plow-piled snow. A post at the Transportation Alternatives blog from February 2014 discusses how sneckdowns reveal that our streets give cars more space than they need. [ WHAT SNOW REVEALS ABOUT STREETS].
Never waste a good crisis, people of New England (and other snowy places). Post and follow sneckdown photos on Twitter: #sneckdowns. And use this evidence to demonstrate that cars don’t need all that precious urban space.
The (quite wonderful) CityLab blog from The Atlantic has an article describing “what has been called ‘one of the transportation safety field’s greatest success stories'”: the road diet. [So What Exactly Is a ‘Road Diet’?].
Road diets are inexpensive ways to achieve good transportation outcomes, such as increased traffic safety, as well as better walkability and bikeability. Despite popular conceptions, it does not necessarily increase traffic congestion. And did I mention they are cheap?
Road diets are as close as we get to a “magic bullet” in transportation planning. As planner Charles Marohn writes:
Why, when our leadership has expressed so clearly the enormous financial gap we have in funding a “world class” transportation system, are road diets not an obsession of transportation departments everywhere?
I recently participated in the second of two National Science Foundation workshops on Big Data and Urban Informatics, hosted in the great city of Chicago by our friends in the Urban Planning and Policy program at University of Illinois – Chicago. The future is here, folks. The things we can do now with respect to data and simulations is simply gobsmacking. I’ve been in the transportation and urban science business for roughly 25 years. I never dreamed we would come so far so quickly. This is truly a revolution.
Here are only a few highlights, based on my tweets (@MobileHarv)
- Small data -> big models. Big Data -> small models (workshop on Big Data and Urban Informatics, UI-Chicago)
- Carlo Ratti keynote – driverless city will require 80% less automobile infrastructure for the same mobility. Yes, please! #UrbanBigData
- Dutta & Anderson (Columbia U) – electric taxi fleet through vehicle to vehicle wireless charging – reducing stops to charge #UrbanBigData
- Adam Davidson (CUNY): Women use NYC bikeshare less but pay more and do more reverse trips, supporting and rebalancing system #UrbanBigData
What ties all of this together is the ability collect, store and process georeferenced and moving objects data, leading to new GIScience, simulation and visualization techniques. We can now look at all of the bikeshare data or simulate the behavior of all cars, taxis and people in a city, leading to new insights and discoveries.
But now what? How do we translate these Urban Big Data and Grand Urban Simulations into actionable knowledge quickly enough to make a difference in an increasingly speedy world? This is a question I raised at the recent (and excellent) Big Data Future conference at The OSU. Its a trickier question than it seems – we now understand that human systems such as cities and transportation are becoming more complex (in the formal sense) as the world becomes more crowded and connected. How do we manage systems that cannot be predicted in principle, even with all of the data and computation in the universe?
We can understand and manage complex human systems, but it requires humility: a recognition that societies and cities cannot be engineered like machines; they must be cultivated and shaped like gardens. Big plans are fine for perspective, but small actions, self-organization and cooperation are how we translate Big Urban Data into policy and actions.
In her opening keynote, Vonu Thakuriah suggested: Big Data -> small models. My suggestion: Big Data -> small models -> small actions.
More later – I have a couple of papers coming out soon on these topics. This was just a teaser.