Since Donald Trump's election on November 8th, 2016, we’ve noticed a major uptick in complaints about traffic in his Manhattan neighborhood. That’s no small feat, especially given that New Yorkers are known for complaining about traffic – just ask Jerry Seinfeld. (For the record, we complain about traffic a ton in San Francisco, too.) However, we hesitate to use subjective grumbling to measure the impact of events. Thanks in part to cognitive biases, people have a tendency to exaggerate traffic and other negative events. Since we were curious about exactly how much travel patterns changed in New York after the election, we decided to use Big Data to crunch the numbers ourselves. (Note: Our study originally appeared in USA Today. Click here to read the article.)
At last week’s Transportation Research Board Annual Meeting, I attended an excellent panel discussion about transportation data. Towards the end of the panel, the moderator challenged the group with a somewhat loaded question. I don’t recall the exact phrasing, but it was along these lines: “As transportation professionals, we know that we have a huge amount of work to do to upgrade, maintain, and repair our infrastructure. The backlog of projects that we have not yet begun is overwhelming. Given our clear mandate - and the often politicized process of infrastructure investments - does all this new data actually impact our decisions in the real world?” It’s an important question to ask - but based on audience feedback and in my own opinion, the answer is clearly yes.
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Occasionally, when I introduce myself as an employee of StreetLight Data, people ask: “Oh, so you measure the data from streetlights?” We do not do that - yet! (But stay tuned for more information on our new partnership with Current Powered by GE down the road)
We're beginning to hear this question often enough that we wanted to publish a blog post about our name. And now is the right time to tell our story: Next week, we will be exhibiting at the Transportation Research Board Annual Meeting, and the StreetLight Data booth (#639) is located near a traffic signal manufacturer.
At the heart of every transportation project lies the need for mobility data. But actually getting accurate, comprehensive data in a cost-effective, appropriate, and timely manner is not always easy. Many transportation planners must rely on costly, outdated data or use time-consuming, assumption-based models to estimate behavior. Although surveys and sensors can certainly reveal important insights, planners who rely solely on conventional data collection tools often struggle to answer important travel behavior questions empirically, accurately, and comprehensively. But transportation planners can take control and get better results by taking advantage of new, more cost-effective, and more effecient data collection and analysis tools. In this blog post, we’ll discuss three key transportation data challenges, and how to overcome them by collecting data that is current and precise, and that describes real-world travel patterns.
A few weeks ago, one of my best friends from graduate school moved to Colorado to work for my old employer, Rocky Mountain Institute. It’s a nonprofit research and educational foundation dedicated to efficient and sustainable use of resources. The downside – from my perspective – is that he and his equally awesome wife moved to Boulder, CO for this job. The upside – from his perspective – is that now he can go skiing every weekend. The question is: where should he go skiing?
External trip estimation is one of the most important measures for planning departments across the country. Like all kinds of travel, external trips cause wear and tear on local roads and contribute to traffic jams. But unlike your local residents, drivers simply passing through your community don’t contribute to your local economy. They’re not stopping to eat or shop. In this blog post, we've identified 10 ways that these trips can impact transportation planning in your community.