Autonomous vehicles (AVs) are beginning to dominate much of the public conversation about our transportation future. This was certainly the case at South by Southwest, where I participated in an excellent panel discussion at the C3 Smart Mobility Showcase: “Smart Cities and Data-Driven Deployment of Autonomous Vehicles.” Nearly every single panel at the showcase was related to AV technology. People in that tent were very excited about AVs. However, I found myself thinking back to the 12-lane urban highway that my taxi driver took from my hotel to the event. The local bus would have taken over three times as long, and the drive reminded me that AVs are not a panacea for all that ails our transportation system.
Don’t get me wrong: Talking about AVs at an event like SXSW makes sense, and I’m glad we’re having these conversations. But I think the broader discussion around AVs needs to be focused on accountability. The impact of AVs could be very positive or very negative, as many transportation experts have suggested. In this blog post, I’ll explore how a data-driven approach can help us strike the right balance with AVs, and hold ourselves accountable for achieving a positive outcome for all.
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.
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For even the most seasoned consulting firms, winning a government contract through competitive bid is no cakewalk. However, there are ways to make your proposals rise to the top – and today, StreetLight Data is introducing a new way to differentiate with StreetLight InSight®, our easy-to-use online platform for turning Big Data into transportation Metrics. Our new Consultant Subscription allows consultants to customize, visualize and download Metrics derived from Massive Mobile Data in ways designed to help grow their businesses. Keep reading this blog post for all the details.
Two of the questions we’re often asked here at StreetLight Data are: “What percentage of the population is creating the location records in your sample?" and "Does the location data in your sample fairly represent all income groups, or is it biased?” In this blog post, we’re pulling back the curtain on our internal evaluation process with a deep-dive analysis of our newest data source: Location-Based Services data. We started using this data source chiefly because of its large sample size and representativeness, so in this blog post, I will show you our process for determining these characteristics. (Click here to read more about Location-Based Services data in general.)
I’m excited to share that we updated StreetLight InSight® again this week – and it’s an update that I’ve been eagerly anticipating for quite a long time. Beginning now, our clients can access Metrics derived from our new Location-Based Services data source directly from our StreetLight InSight web app – that’s our one-stop, cloud-based platform for the best Big Data resources and the processing software that makes them useful. So, why am I so excited about this? It means some of our most popular Metrics are even more comprehensive and accurate than before. That’s because our device sample size now represents about 10% of the U.S. population. We’re processing roughly 60 billion location data points into travel pattern analytics every month – and counting!
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.)