New Ways of Counting Pedestrians Could Change City Planning Forever | Next City

Henry Grabar at Next City writes “New Ways of Counting Pedestrians Could Change City Planning Forever” in his Science of Cities column.  Our work is discussed and Brendan Murphy and I were interviewed.

What if what we already know about the locations of homes, streets and workplaces is enough to design a function for foot traffic?

That’s the aim of three transportation scientists at the University of Minnesota, who released a working paper last month with a model for pedestrian activity. Using metrics for economic accessibility (where jobs are), “betweenness network centrality” (a design-based estimate of likely routes pioneered by MIT’s Andres Sevtsuk) and auto traffic, the researchers attempted to predict pedestrian activity at over a thousand intersections in Minneapolis.

It was a bit like your typical algebra problem, in that the team, led by graduate research assistant Brendan Murphy, already had the right side of the equation in place — Minneapolis has a smattering of pedestrian and bicycle counts on file. They had to come up with an algorithm to best correlate known and easily accessible data (like jobs) with those elusive counts.

What they found isn’t shocking: Accessibility to jobs by walking and transit, auto traffic, and certain types of jobs (education, finance) are strongly correlated with increased foot and pedal traffic. But the particular results are less important than the general validity of the concept. Adjust a few coefficients, and you could potentially take the model to cities that don’t already have pedestrian data, and produce a citywide traffic estimate with just a few hours of work.

“There have not been a lot of pedestrian data models because there’s not a lot of data to calibrate them to,” says David Levinson, a co-author and professor who also runs the blog Transportationist. But that will change, he adds, as cities become more cognizant of walkers and cyclists. (See, for example, Placemeter.)

More data makes a better model, which could in turn be used in places where data is harder to obtain, like the megacities of the developing world. “You can take this type of model and apply it to a city where you have no data,” Murphy explains, “and from that predictive model you can inform urban planning decisions much, much better.”