This year, the Eastern Research Group (ERG), Coordinating Research Council (CRC), and StreetLight Data teamed up to validate one of the big as yet unmet promises for Big Data – the ability to better model and thus manage criteria pollutant air emissions from vehicles.
The results of our work show that using Big Data to model emissions at the county level is more accurate than industry-standard practices today. Of three different counties we analyzed, we found that:
Modeling emissions accurately matters: It allows air quality models to better predict concentrations of the regulated air pollutants ground-level ozone and particulate matter in different counties, which informs air quality planning and control strategies at the local level. In this blog post, we will walk you through the new methodology and some of our key findings.
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.
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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 watch our recorded webinar on the new Metrics. Click here to watch the webinar.
We believe this Metric provides estimates that are comparable or better than most of the standard AADT estimation practices for three reasons:
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.
I’m excited to share that StreetLight Data has officially announced its new data partnership with Cuebiq, a next generation location intelligence company. As a result of this partnership, StreetLight’s total device sample size will increase to more than 30M devices – that represents over 10% of the adult US population.
Today, the Administration is announcing a new “Smart Cities” Initiative that will invest over $160 million in federal research and leverage more than 25 new technology collaborations to help local communities tackle key challenges such as reducing traffic congestion, fighting crime, fostering economic growth, managing the effects of a changing climate, and improving the delivery of city services. "