Traffic congestion negatively effects the economy, roads and our quality of life. Some people tend to blame pass-through trips, commercial attractions or employers, but expectations don’t always line up with hard data about what really causes congestion.
In California’s Napa County, for example, it is common to blame traffic on wine-tasting tourists. However, when planners used Big Data to map out travel behavior in traffic congestion studies, they found out that commuters actually contributed as much to congestion as tourists. High housing costs in Napa are a major part of the problem.
It’s not easy to figure out why congestion happens, especially in downtown districts. Learn how to improve the accuracy of your traffic congestion studies by using analytics derived from Big Data.
Lately, I’ve been thinking a lot about the city of Atlanta, where a fire recently destroyed a portion of the I-85. It’s a major highway that hundreds of thousands of people use every day to access to their jobs, their schools, their groceries, and more. For me, the highway’s closure highlights how vital our transportation networks have become to quality of life in our communities. Even in a best case scenario, residents of the Atlanta region are likely to spend several months without this vital transportation connection – and the typical Atlanta resident already spends more than 70 hours in traffic each year. What can we, the transportation community, do to limit the negative consequences of unforeseen events like this? It's not a simple problem for anyone to solve, and we know that the folks in Atlanta are working day and night to solve it. In this blog post, I will describe a few data-driven tactics for reducing congestion misery on I-85 in Atlanta. We hope that this analysis will be useful for detour management in Atlanta in the coming months.
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Transportation planners today face a ton of challenges as they work to build efficient, safe, and sustainable urban transportation systems. From rising congestion to increased demand for public transit, the travel behavior and transportation preferences of modern city dwellers are changing fast. These challenges raise complicated questions for urban transportation planners; for example, “How do we handle the rise of ride hailing apps? If we add more public transit options, will people use them? How do we minimize the impact of construction if we do expand public transit? And how do we pay for all of this?”
The smart city movement’s first wave brought tons of stationary sensors to our cities, especially in the context of transportation. These sensors are passively collecting valuable travel pattern information at traffic lights, parking lots, bus stops, sidewalks, and more. But if we want cities that are truly smart – if we want to solve the challenges exposed by our stationary sensors – we have to go beyond them. In this blog post, I will use New York City as a case study to explain why.
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.)