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.
In this blog post, we highlight how to use Big Data to measure the performance of transportation policies and infrastructure projects. Since we’re deeply concerned by the rise in traffic fatalities in 2015, we decided to evaluate the performance of a popular safety measure: a road diet.
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At StreetLight Data, we’re excited that Vehicle Miles Traveled (VMT) is gaining traction as a transportation performance measure. In this blog post, we explore the reasons behind increasing interest in VMT and discuss why StreetLight InSight®, our easy-to-use web app for transportation Metrics, is a great tool for calculating and exploring VMT. If you’re not familiar with StreetLight InSight, click here to learn more by watching our demo videos.
Bike shares are riding a wave of popularity in the intermodal transit planning community. They are great because they help address several hot-button urban challenges simultaneously; for example, congestion, air pollution, “last mile” transit gaps, and even sedentary lifestyles. From 2004 to 2015, the number of bike share systems worldwide grew from 14 to 855 – that’s over 6,000% growth!1
However, there are still only 54 bike share systems1 across America’s 486 urbanized areas.2 That means there is plenty of room for expansion in the U.S. We wanted to find out how Big Data could play a role in this expansion.
Sample size is not a simple concept when it comes to Massive Mobile Data Analytics - see our prior blog post for some examples. In this post, we're analyzing our commercial vehicle data’s sample size in California based on a Daily Trip Sample Ratio. The results are that our archival data captures ~11.8% of commercial vehicle trips that took place in California in 2015.
In the transportation industry, we love to debate the optimal sample size for data collection. But this deceptively simple concept can be tricky.