In my hometown of San Francisco, California I grew up riding the municipal bus home from school with a group of classmates. As a group, we saw the San Francisco Municipal Agency (SFMTA) try a multitude of strategies to make the system more efficient and cost-effective. For example, SFMTA transformed car lanes into dedicated bus rapid transit (BRT) lanes to increase the fleet’s speed. When many of these decisions were made, most of my bus crew was under the voting age, and as teenagers, public forums were not an exciting Friday night activity.
I remember that we complained almost every day about how slow or inefficient the bus was, yet we never did anything to try to fix public transit. We never participated in the public forums or in outreach programs that gathered feedback on BRT. Now, as BRT expands, huge changes are happening to the system that affect my current commute. However, at the same time as SFMTA was inviting the public to share their feedback, I bought a smartphone. To the dismay of my parents, I used that smartphone constantly. Now imagine if my teenage obsession had allowed me to be a more active participant in decisions like the BRT, simply because SFMTA could use the location data created by my phone to develop their future plans.
When it comes to transportation planning, it seems like Big Data—location data created by connected cars and trucks, smartphones, and wearables—is the next big thing. How public agencies will actually use and implement this data is the big question on people’s minds.
With 2.5 quintillion bytes of Big Data being created daily, and much of this data offering valuable insights for transportation planning, it almost seems negligent for government agencies to not use this information source. Brookings Institution, a non-partisan research institute, recently looked into why government agencies are lagging behind on Big Data adoption. With the help of our CEO, Laura Schewel, the Brookings Institution has brought to light the core disconnects between government agencies and Big Data, and some of their findings may surprise you! In this blog post, I’ll take you through our key takeaways from the report.
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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.
Traditional methods of collecting information travel data feel familiar and intuitive to most transportation professionals. That makes sense because household surveys and simple sensors like tube counts have been around for decades. In contrast, Big Data can seem vague and conceptual. (Note: we define “Big Data” as the location records created by mobile devices.) The size and complexity of raw geospatial data sets make it nearly impossible for most transportation professionals to collect and process Big Data on their own.
But as vague and conceptual as Big Data may appear at first, it is actually just as straight-forward as traditional tools if you approach it in the right way. In this blog post, I’ll walk through three key steps that planners can take to use Big Data effectively.
From ride-hailing apps and volatile gas prices to electric cars and (theoretically) autonomous vehicles, transportation behavior is rapidly changing. To properly plan for and manage our evolving transportation system, engineers and planners must keep pace with these changes. If managing transportation demand is important to your community, it’s not enough to follow the old pattern of creating new core analytics every 5-10 years to feed your models for any type of planning. For transportation demand management (TDM), which could be most profoundly affected by these new trends, the need for up-to-date, real, accurate data is even sharper.
In today’s evolving environment, effective TDM requires regular access to clean, up-to-date data. One problem that many planners face is that surveys and other traditional data-gathering methods simply cannot deliver high quality data at a frequent update cadence and affordable price.
Keep reading this blog post to learn all about TDM, and to find out how Big Data can help you maximize the impact of TDM strategies.
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.