At this point, it seems clear that autonomous vehicles are on the verge of technical feasibility. Just last week, Waymo announced that it is testing self-driving minivans without a human back-up in the front seat. Its employees will be riding in the back with an emergency stop button – but no steering wheel. But do these technical advances mean that we’re ready for AVs? How should we manage the non-technical aspects of AV deployment to ensure they achieve promised improvements in safety and accessibility?
I decided to write this article to address these issues after participating in the Intelligent Transportation System World Congress earlier this month. There were tons of panels focused on autonomous vehicles, and I was lucky enough to be speak on one that dove into the critical questions for civic leaders and transportation professionals. We went beyond technical readiness to ask ourselves if should we deploy AVs, and, if so, how should we deploy them?
As a San Francisco-based company, the wildfires that recently spread across northern California have been extremely troubling for our team at StreetLight Data. For us, this went beyond poor air quality in the Bay Area. The fires impacted the homes and personal well-being of our employees, our clients, and our families. While it is always difficult to see tragic events occur anywhere in the world, watching fires destroy places we love was something else entirely.
The experience forced us to think harder about what we, as a company, can do to help. Our product, StreetLight InSight®, helps transportation professionals use Big Data from mobile devices to understand travel patterns – but what specific information can it provide to aid in evacuations? At a personal level, how can we help communities in need, at least in the continental US and southern Canada, where we currently operate?
Before I dive into details, I want to stress that we’re here to help. If your community is facing an imminent evacuation, and our Metrics could help you get people get out of harm’s way, email me (I’m the CEO) directly. Tell me what you need, and we’ll skip the formalities and paperwork to provide the data for free as quickly as we can.
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We just passed our one-year anniversary of using Location-Based Services (LBS) data, so we decided to update some key sample size figures. The results are exciting: Our sample size has doubled to more than 62 million devices in the US and Canada in the past year. In other words, now our analytics anonymously describe the travel behavior of 23% of the US and Canadian adult population.
There are many reasons for this increase, including our main LBS data partner, Cuebiq, doing a great job. However, the most important reason is that Location-Based Services are becoming more and more widely adopted by consumers. As a result, our clients can now analyze the aggregate travel patterns of nearly ¼ of the population in just a few mouse clicks.
That’s a large sample by any measure, but when you consider the “status quo” methods of collecting travel behavior data, it’s even more dramatic. Imagine how much it would cost – and how long it would take – to collect household travel surveys from 62 million people, or to install sensors and traffic counters on the roads they use every day. It just wouldn’t be feasible. In this blog post, I’ll explain how we calculate sample size (hint: accuracy is more important to us than flashiness) and why it’s grown so much in just one year.
The Labor Day public holiday celebrates American workers by giving them the day off – or at least, that’s the idea. Here at StreetLight Data, we wanted to find out how many American workers are still commuting to their jobs on Labor Day. The results were surprising: Only about ~56% of American workers get the day off nationwide, with some variation in results across different states. In this blog post, we’ll walk you through our analysis of Labor Day travel patterns.
I’m thrilled to announce our latest monthly update of StreetLight InSight—that’s our online platform for transforming Big Data into transportation Metrics in minutes. With this release, our platform does even more to close the gap between Big Data and actionable analytics.
We’ve achieved this by adding several new Metrics, updating our databases with the latest location data sets from our suppliers, and implementing features that make StreetLight InSight more user-friendly and responsive. In this blog post, we’ll dive into the most important updates.
It’s undeniable that “Smart Cities” are getting all the buzz these days, especially when it comes to using Big Data for transportation. But it’s not right to leave rural communities out of the conversation. In fact, rural communities stand to reap the same benefits from better travel behavior data as densely populated areas – if not more.
That’s why it’s so upsetting for me to hear this type of comment at industry conferences: “Big Data sounds great. But I know it won’t work in my rural county. There’s not enough coverage.” That’s outdated thinking, plain and simple. It may have been true just a few years ago, but “Big Data” – AKA the billions of location records created by mobile devices every month – is a fast-growing, continually improving resource. With the rise of Location-Based Services (LBS) for smart phones in particular, we’ve seen an explosion of geospatial data sets with excellent coverage in rural areas.
As a Virginia native with family and friends across the many rural areas of the state, this issue hits close to home. I know from our data and my personal experience that rural drivers drive many more miles per day on average than urban drivers. We owe it to these communities to plan transportation systems that account for their unique travel behaviors.
To do that, rural planners need the up-to-date, comprehensive, and precise travel data that is only available from mobile devices. So in this blog post, I’m going to show that Big Data analytics for transportation are in fact widely available and extremely useful for rural areas.