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 That’s outdated thinking, plain and simple.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.
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After many years of client requests, we’re releasing the first cut of our StreetLight Volume: 2016 AADT Metric on the StreetLight InSight® platform. These beta Metrics provide a very robust estimate of 2016 Annual Average Daily Traffic (AADT) for almost any road in the US. To learn more, keep reading this blog post, or join our webinar on June 16th at 12pm PT. Click here to register for the webinar.
We believe this Metric provides estimates that are comparable or better than most of the standard AADT estimation practices for three reasons:
The more I work with StreetLight Data’s location-based services (LBS) data set, the more I realize that it is the data source the transportation industry has been waiting for – and that it deserves. Over the past few months, LBS data has emerged as a resource with all the benefits of cellular data, but without its limitations. LBS data can answer a huge array of travel questions that fill in the long-standing information gaps for the transportation industry, especially when used in combination with navigation-GPS data.
But since it’s so new, there’s very little information available to planners about its value today. We’re working to correct that with a series of blog posts that zero in on a different aspect of LBS data – and this is the first. In this post, I’ll highlight LBS data’s spatial precision.
At StreetLight Data, we transform location data from connected vehicles and mobile devices into Metrics that describe travel patterns through our easy-to-use StreetLight InSight® platform. The name StreetLight Data is a metaphor for the light that our analytics shine on mobility behavior. In other words, we don’t do streetlights – or rather, we haven’t before.
Building accurate travel demand models requires a detailed understanding of the ways behavior changes during specific conditions. Route choice, for example, can vary dramatically depending on time of day and other factors that influence drivers.
Let’s say that Sarah needs to drive from her home in Pittsburgh’s east end to the downtown business district. Most of the time, she finds Penn Avenue to be the fastest and most enjoyable route. But she also knows it would be foolish to take that route during rush hour. Or during a sports game, major concert or visit from the President.