Earlier this year, we integrated our new Location-Based Services data source into the StreetLight InSight® platform. Since then, we have steadily introduced Metrics and features built off that data that help our clients glean even more useful information from our platform. In this blog post, I’ll highlight the most exciting developments. (In case you need a refresher, StreetLight InSight is our easy-to-use cloud-based platform for transforming Massive Mobile Data into analytics that describe travel behavior.)
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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When we founded StreetLight Data back in 2011, our sole focus was to help educate and plan for electric vehicles (EVs). But we quickly realized that our transportation analytics would have a more significant positive impact if we expanded our mission. However, EVs are still one of my deep interests: I drove a Chevy Volt for several years before going car-free just a few months ago, and I focused on EVs in my early career at the Rocky Mountain Institute and Federal Energy Regulatory Commission. Based on my personal experience, I know we can do a better job of planning and deploying EV charging infrastructure. If we want to see wide adoption of EVs, then we must make charging more convenient and affordable while minimizing its impact on our electrical grid. Given the wave of new charging station deployments in the US and abroad, now seems like the right time to explain how Big Data can help.
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.
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!