Analyzing Urban Traffic Statistics with Wolfram|Alpha
New York City. Los Angeles. Chicago. Each of these cities is renowned for a diverse array of cultural, entertainment, culinary, and other experiences—as well as for legendary traffic delays. But just how bad do native commuters have it? And if you drive to work in a different city, how does your commute stack up? Wolfram|Alpha can’t yet guide you through the traffic, but it can visualize and compare statistics about traffic and urban transportation in more than 100 US urban areas, with data from the Texas Transportation Institute’s Urban Mobility Report.
Ask Wolfram|Alpha about traffic in NYC, LA, and Chicago, for example, to see how they compare:
You can see that despite being only the third-largest city in the country, Chicago has the worst traffic of the three—with each commuter’s daily traffic delay lasting 1.7 times as long as in New York, wasting an estimated 52 gallons of fuel per year per person.
If you glance down at the “Public transportation” pod, you can get a hint of why NYC’s overall traffic stats aren’t so bad (even better than traffic delays in Houston, San Francisco, Dallas, and Atlanta, in fact): the Big Apple clocks in with an estimated 21 billion person-miles per year of public transit use. Click on “total public transit use” in the pod shown above, and you can see that this is about 6.5 times the total public transit use in Los Angeles.
But what’s even more interesting is to mash up this data with other socioeconomic indicators. Take unemployment, for example: is there an observable correlation between local unemployment rates and, say, public transit use? Chicago shows an interesting inverse relationship:
Or what about home prices and traffic delays? You can see a correlation between these two properties in the LA urban area:
This is where Wolfram|Alpha’s strengths really shine through—in allowing you to perform comparisons and computations like this as easily as you can think up a question.
Speaking of which: given the high cost of gas this summer, here’s the answer to one question that came to my mind as soon as I saw data on Chicago’s traffic delays. Ouch!
These are just a few of the dataset mash-ups you can do with urban traffic statistics. What interesting facts can you uncover mashing up data within Wolfram|Alpha?